Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications. Advances in digital technologies make large datasets commonly available, which demands faster and more effective pattern analysis techniques. However, methodologies to find the most suitable technique for a given application do not usually take into account the learning-time constraint required by the application. One may argue that parallel processing is possible in many situations and that machines are faster, but in practice datasets grow fast and opportunities for new applications continually emerge.Consider a large database of face images obtained from many individuals through video cameras and all possible applications involving face recognition and verification. When a cell phone user uploads a video of her face to that database, such that a classifier can be trained to identify her and unlock the cell phone, the learning time for this application should not take more than a few seconds. In computer-assisted diagnosis of parasites , each miIn general, applications in which a classifier must be trained upon user request  should provide interactive response time. Even without user interaction, applications that require parameter optmization, using the accuracy of a classifier as criterion function, also require training and testing the classifier several times. As the dataset grow large, this becomes a problem. A good example of this occurs when learning the architecture of a convolutional neural network for feature extraction and classification—a hot topic nowadays. The response time does not need to be interactive, but processing time limitations may compromise the success of the optimization procedure by reducing the search space. Feature selection from large datasets may be used as a similar example. In the case of face recognition in mobile devices, this may be considered a future application that will become reality with the advances of cloud computing and communication networks. Currently, the mobile devices provide mechanisms for face recognition that are independent of the user—i.e., the design of the classifier does not consider the most informative samples to distinguish a particular user from other individuals with similar face characteristics. As a consequence of that, face recognition in mobile devices does not work properly. In order to have a robust face recognition system, it is desirable to learn the most informative samples from a large negative dataset, which could be stored and processed in a cloud system. During user enrollment, the important negative samples could be mined and together with the face samples from the user could train an user-specific classifier, which would be transmitted back to the mobile device, being the whole process performed in interactive time. Furthermore, for research purposes, where some techniques commonly take several minutes or hours to train a classifier, it is desirable to reduce the learning time to no longer than a few minutes. After all, we may need to repeat experiments hundreds of times in order to obtain statistically significant results and the increasing size of the datasets may also prevent us from train the classifier with all labeled samples.Methodologies to compare pattern classification models usually fix features, training samples, test samples, and accuracy measures for all classifiers. This approach is adequate when evaluating the effectiveness of different techniques under the same conditions, but it contemplates neither learning-time constraints from the applications nor the fact that faster classifiers may be able to achieve higher performance on the same unseen testing set, given an allowance for a larger training set. For fairness with faster techniques and from a practical viewpoint, it is important to relax the constraint of a fixed training set for all classification models, provided that: (1) the training samples come from the same large learning set, and (2) all techniques must choose their own training samples and complete training within a pre-established learning-time limit, granted by the application.In this work, we propose a methodology consistent with the above conditions. At first, a large dataset is randomly divided into learning and testing sets. The learning set should be large enough to contain representative samples from all classes. Given the learning-time limit allowed by a given application, it is usually not feasible to train a classifier using all learning samples. Therefore, the proposition is to start with a very small training set composed of randomly selected samples from the large learning set. This initial training set is the same for all classification models. Each classifier is then trained and subsequently evaluated on the learning set. By assembling a subset of randomly chosen misclassified samples, containing no more than the number of samples in the current training set, and incorporating it to the training set, we prepare the stage for a new learning cycle. This three-step procedure is then repeated until either (i) the learning-time limit from the application is reached or (ii) the number of errors becomes zero. In this way, faster techniques may acquire more error samples and complete the learning process with larger training sets. However, for their performance to be better on the unseen testing set, they must be more effective in learning from their own errors. Moreover, in order to achieve statistically significant results, this entire process, with distinct learning and test sets, has to be repeated several times.very interactive (less than 1s), interactive (from 1s to 5s), nearly interactive (from 5s to 1 minute), and non-interactive (above 1 minute). As this work aims at presenting a new methodology for the evaluation of classifiers and not to endorse any particular technique, we opted for standard implementations of each of the techniques used, since choosing optimized implementations might skew the results unfairly.We have evaluated our methodology on large datasets, using several techniques, and subject to the learning-time limits typically required by applications. We categorized these time limits into This work is organized as follows. We start by describing the background material and related works. Next, we introduce the new methodology. Then, we discuss the experiments and results. Finally, we present our conclusions.Many works have presented pattern classification models based on discriminant analysis, nearest neighbors, neural networks, support vector machines and decision trees, among other techniques. In  the authSeveral aspects must be carefully considered for such methodologies to work. Firstly, call to mind that certain characteristics of the datasets  may require a specific sampling strategy . Also, dOn the other hand, one aspect rarely considered is that those methodologies are sensitive to the order of the samples in the dataset.  have evaDifferent measures can also alter conclusions with respect to effectiveness. The literature offers many measures, such as: named accuracy –21, miscThe methodology presented in the next section can be used with any measure of effectiveness, since it is able to maximize effectiveness under a learning-time limit, as required by applications. To the best of our knowledge, our methodology is unique in the sense that it takes into account both the efficiency of the techniques in acquiring more knowledge (training samples) from the problem instance and their effectiveness in identifying the most representative training samples (errors in a learning set) to lead to a more accurate classifier. The methodologies mentioned above also aim at maximizing effectiveness, but they attempt this without regard for learning time. Given that each classifier selects its own training samples during the learning phase and the number of learning iterations is dependent on its efficiency, our methodology is robust with respect to the order of samples in the learning set. As we will see, the fact that all classifiers start with a small training set, obtained from randomly selected samples from the large learning set, considerably reduces the correlation between the initial training sets among multiple executions of the method.In this section, we present the proposed evaluation methodology that considers efficacy and efficiency at the same time. In order to accomplish a fair comparison, the proposed methodology allows the classifiers to learn from their own errors, within a given time limit as required by the application.2 and a test set 𝓩3. Due to the learning-time limit of the given application, training with all learning samples is usually not possible. So, an initial training set 𝓩1 is created with a very small subset of randomly selected samples from 𝓩2, such that each class is represented by at least one sample.A dataset 𝓩 is first randomly divided into a learning set 𝓩1, they are evaluated on 𝓩2∖𝓩1. Next, we randomly select from the misclassified samples of each classifier a number of samples to be incorporated into (its own) 𝓩1 . Retraining, evaluation, and misclassified sample selection is repeated until either the number of errors goes to zero or the learning-time limit 𝓣 of the application is reached. Within the learning-time limit 𝓣, each classifier has the opportunity to learn from its own classification errors on the learning set 𝓩2∖𝓩1, as the training set 𝓩1 increases.After training each of the models with the same training set 𝓩2∖𝓩1. So, after each learning phase, an improvement in accuracy should also be expected on the unseen testing set 𝓩3. Algorithm 1 details this learning approach.This procedure works under the reasonable assumption that the most informative samples can be obtained from the errors on 𝓩Algorithm 1: Learning Algorithminput: A learning-time limit 𝒯, a learning set 𝓩2 and a function λ(s) that returns the correct label of any sample s ∈ 𝓩2. Output: A supervised classifier. auxiliaries: A training set 𝓩1 and a list 𝓜 of misclassified samples. 1 ← small random sampling from all classes in 𝓩2;1 𝓩repeat2 3   𝓜 ← ∅1;4   Create a classifier instance 𝓘 from 𝓩for each sample t ∈ 𝓩2∖𝓩1do5   t) using 𝓘;6    Compute the label 𝓛(if 𝓛(t) ≠ λ(t) then7    t}8     𝓜 ← 𝓜∪ {end9    end10   1 ← 𝓩1∪(random subset of samples from 𝓜)11  𝓩until (𝓜 = ∅ or learning time ≥ 𝓣);12 2 and test set 𝓩3. After starting off with an identical small training set containing at least one sample from each class, each individual classifier assembled its own training set 𝓩1 from 𝓩2. The resulting training sets ended up having different sizes, after the learning-time limit, according to the efficiency of each classifier. All the experiments reported here were performed on an off the shelf desktop computer featuring an Intel Core I5 processor and 4 GB of RAM. We used the standard C implementations of each classifier, as well as their own specific training strategy, with or without parameter optimization depending on the case, which is repeated during each learning iteration. It is impossible to establish the same parameter optimization method for all classifiers, because each classifier has its own mechanism. Some of them, such as OPF, does not require parameter optimization. Note that, since the methodology can be used to select the most suitable classifier for any given application, we are not targeting any application in particular. The experiments essentially demostrate the main characteristics of the methodology when comparing classifiers in different scenarios (datasets and time constraints).In this section, we describe the overall experimental methodology, including datasets, effectiveness measure, classification models and the computational environment used. The experiments were carried out 100 times with randomly generated learning set 𝓩Cod-RNA Dataset : comprisConnect-4 Dataset: obtained from the UCI Machine Learning Repository  containiCovertype Dataset: also obtained from the UCI Machine Learning Repository ; it contIJCNN 2001 neural network competition : consistSensIT Vehicle (combined) : made upFor the experiments, we selected commonly available datasets of modest sizes with feature spaces of various dimensions.F1 score, which Jardine and van Rijsbergen [It is important to highlight that the proposed methodology can be used with any effectiveness measure appropriate to the specific domain of application. The literature suggests several interesting effectiveness measures, as previously stated. In our experiments, we adopted the jsbergen  defined very interactive, interactive, nearly interactive and non-interactive. For each dataset, we used four different learning-time limits, which were empirically chosen to simulate potential applications with different response times, so named: For the sake of completion, the compared classifiers are briefly described in the following subsections. For more details, see , 50.Support Vector Machines (SVM) , a widelγ using 5-fold cross-validation within the training set and a grid search over exponentially growing sequences of 𝓒 and γ , as well as the linear version [In our experiments, we used the latest version of the LibSVM package  with Gau version  of SVM (k-nearest neighbor (k-NN) algorithm is amongst the simplest and most largely used of all classification techniques. k-NN classifies a given sample by assigning it to the label most frequently present among its k nearest neighbors. For k = 1, a given sample is simply assigned to the class of its nearest neighbor and it corresponds to a first order Voronoi tesselation of the training data. k-NN takes into account k neighbors, so making the variance of the method less sensitive to noise and outliers. In this work, we estimated the value for k using a leave-one-out procedure over the training set .The The Optimum-Path Forest classifier (OPF) , 54 is aIn our experiments, we used LibOPF , which i1; 49.99%, for the learning set, 𝓩2∖𝓩1; and 50.0%, for the test set, 𝓩3. Given that each experiment was repeated 100 times with randomly selected learning and test sets, this procedure resembles a 50 × 2 Monte Carlo cross validation [2∣ (the learning set size) at each iteration, we can easily estimate the number of iterations, that each classifier required to complete the learning process (sample selection and training) within each given time limit, based on the final training set size. For instance, if the final training set contains 0.05% of the learning samples, then the classifier required 5 iterations.In this section, we discuss the average results obtained by following the approach presented in the Methodology Section, with the experiments repeated 100 times with randomly selected learning and test sets. In the preprocessing step, we employed a feature standardization to avoid that attributes in larger numeric ranges dominate those in smaller ranges. We initialized each learning instance with 0.01% of 𝓩 for the initial training set, 𝓩lidation . HoweverConcerning the problem of overlapped training sets in cross-validation methodologies, as mentioned in the Background Section, we studied the correlation between each pair of training sets to evaluate the effectiveness of our methodology with respect to the choice of statistically different training sets see . The traF1-score and the final training set size for each classification model, grouped by the learning-time constraint over different datasets.Tables From the experimental results for Cod-RNA dataset , we see k-NN, can acquire more samples within the time constraint as well as achieve higher mean accuracy. However, k-NN usually presents higher variance, being more sensitive to noise. Differently, OPF presents a more stable performance  are connected by using a calculated critical distance (CD) equals to 0.4690.Figs It is worth noting the importance of the statistical test, since the mean and standard deviation , it seems that some randomly selected training samples impaired the performance of the classifier, while our methodology is capable to avoid them in the training set  in a different way in the feature space. By selecting classification errors as training samples, the learning process converges faster to the corresponding decision boundaries. The errors tend to be samples close to the decision boundaries rather than outliers, as long as outliers are minority. If this is not the case, outlier removal should be applied before the learning process. In order to better clarify this issue, we have added k-NN and OPF were more efficient. KSVM and LSVM require significantly more time for training.In order to analyze the performance of each classifier using the entire learning set, We presented a methodology to compare multiple classifiers under a learning-time constraint, which is useful to select the best classifier for a given application. In this paper, the applications were represented by different datasets with unbalancing of classes, distinct number of classes and feature space dimensions. The proposed methodology allows each classifier to select its most representative samples from a learning set during the training phase. The experiments allowed us to reach several conclusions.Although it was not possible to assert which is the most effective classification model under a given time constraint, due to the variability of results on each application domain, experiments obtained using the proposed methodology allowed us to arrive at some relevant observations.Larger training sets do not necessarily lead to higher predictive performance on unseen test sets, which indicates the effectiveness of some classifiers in learning from their own errors.The methodology is able to produce statistically independent training sets as observed by the low correlations between each pair of training set obtained for a given dataset-classifier pair, following 100 executions. This demonstrates the advantage of our approach with respect to the regular cross-validation procedure, largely used in related works.It is also very common in the literature for the presentation of experimental results to rely solely on the mean and standard deviation of accuracy values. The statistical test shows that this approach is not always reliable, due to the relative variations of the standard deviation.


With reference to the social science literature, the current paper explores the circumstances under which statistical inference can be meaningful for such studies. It concludes that its use implicitly requires a target population which is wider than the whole population studied — for example future cases, or a supranational geographic region — and that the validity of such statistical analysis depends on the generalizability of the whole to the target population.Statistical inference is commonly said to be inapplicable to complete population studies, such as censuses, due to the absence of sampling variability. Nevertheless, in recent years, studies of whole populations, e.g., all cases of a certain cancer in a given country, have become more common, and often report  If Czech history could be repeated, we should of course find it desirable to test the other possibility each time and compare the results. Without such an experiment, all considerations of this kind remain a game of hypotheses ’ was used in PubMed on 25 February 2015, restricted to publications in 1994 or later, yielding 64 publications. They were retained if containing primary data on studies of humans, and reporting Registry  has estip values and confidence intervals which have no explicit theoretical basis. This is a problem that social sciences have, perhaps, confronted more directly than epidemiology [Whole population studies often calculate emiology , 27. Havemiology ? The curemiology , 30, andemiology , and is In epidemiology, although aspirations to generalizability are not always met, groundwork has been done on its objective assessment –24, 32.


Inquiry 58(4):368–377 de re attitudes.I present a concrete DRT-based syntax and semantics for the representation of mental states in the style of Kamp . This sy On Recanati’s view this means Lucy has two distinct mental files that both refer to the same individual. Lucy’s Clemens-file will have the predicate IS NOT FAMOUS inscribed in it, while her Twain-file contains IS FAMOUS. Now, Ninan observes, this mental files analysis of Frege cases does not extend to counterfactual attitudes like imagination. Say, Lucy imagines that things were the other way around, i.e. Samuel Clemens is famous but Mark Twain is not. Again, we’re dealing with two incompatible de re attitudes about the same individual, so these attitudes must again involve distinct Clemens- and Twain-files. But how can Lucy’s Clemens-file, which contains IS NOT FAMOUS, capture her mental state of imagining that he is famous?In a previous special issue on Mental Files, Ninan  argues tPerhaps Recanati has a different picture in mind. Maybe Lucy’s mind can be thought of as divided up into boxes corresponding to the different types of attitudes that she has: there is a belief box, a desire box, an imagination box, etc. Lucy’s belief box contains a Clemens-file, as does her imagination box (perhaps these two files are in some way linked). If Lucy imagines that Clemens is famous and Twain is not, then the Clemens-file in her imagination box has the predicate IS FAMOUS inscribed on it, even though the Clemens-file in her belief box has the predicate IS NOT FAMOUS inscribed on it. The predicates inscribed on the files in her belief box are irrelevant to the content of her imagining. Ninan :376Recanati’s . The indefinite a woman introduces a new discourse referent into the common ground. Concretely, interpreting the sentence transforms the empty input DRS into the following output DRS: (2)This boxy formula is read as ‘there exists an x that has the properties of being a woman and of standing outside’. In other words, with the standard syntax and semantics of the DRS language She’s smoking.The subject of this sentence is a pronoun, she, which is treated not as introducing new material but as looking for a previously established (“familiar”) discourse referent to bind to. In this case, the pronoun, initially represented as ‘?’, can be bound to the discourse referent x that was introduced into the common ground by the previous sentence: (4)The final output represents the truth-conditions of the whole two-sentence discourse: there’s a woman standing outside smoking.To illustrate the basic DRT framework, consider 1). . A womaThe analysis of anaphora has been extended significantly to provide, for instance, one of the leading theories of presupposition van der Sandt  and GeurWhat is often glossed over in such linguistic applications – even in many analyses of attitude ascriptions, including my own Linguists have since stripped DRT of its cognitive interpretation. But Kamp and a few others have kept it alive, even extending DRT to a full-blown representational theory of attitudes Kamp . In the it won’t wake up. This dependence is cashed out in the same way as anaphoric dependencies in discourse are modeled in standard DRT, viz. by sharing accessible discourse referents.The starting point of ADT is that mental states are (i) compartmentalized into beliefs, desires, fears, intentions, etc., and (ii) these compartments are highly interconnected. For instance, my mental state could contain the belief that there’s a monster under my bed and, dependent on that belief, the hope that de re attitude. Kamp’s internal anchors are reminiscent of what philosophers have called dossiers This ADS contains at the global level two perceptual anchors, representing my perceptual acquaintance with the letter and the mailbox it’s in. Formally, an anchor consists of a label (anch) and a DRS that introduces a new anchored discourse referent along with the acquaintance-based descriptive mode of presentation that the agent associates with it.3By way of illustration of the theory thus far, the so-called Attitude Description Set (ADS) below represents the mental state I’m in when I look in my pigeon hole at work and see an envelope, believe it’s publisher’s junk mail, hope it’s a letter of acceptance for a recent grant application, and intend to open it right away.2bel, des, int) and a DRS that gives the descriptive content of the attitude. A key feature of the framework is that discourse referents may be shared across anchors and attitudes. In our example the first anchor uses a discourse referent introduced in the second, and all three attitudes use the discourse referent x. We’ll say that these attitudes referentially depend on the anchor for the letter, which in turn referentially depends on the anchor for the mailbox. Finally, to model de se attitudes there is a special, essentially indexical discourse referent i, which we may think of as introduced by an implicit, non-descriptive anchor that represents the agent’s self , again consisting of a label ((6)The actual causal sources of internal anchors are represented outside of the ADS proper. Kamp formalizes the causal links between a mental state and its surroundings as a mapping from discourse referents to entities. In our example, this so-called “external anchor” maps x to the actual letter, y to the mailbox, and i to me: about the causal source of that mental representation, the actual letter.The external anchor allows us to capture singular attitudes: any attitude compartment that depends on  a discourse referent introduced by an externally anchored internal anchor represents a genuinely singular attitude, about the causal source of that internal representation. In our example all three attitudes, the belief, the hope, and the intention depend on the anchor introducing x, representing the letter as something I am seeing in my mailbox. Consequently, they are Before I go into the formal semantics in Section de se center. Similarly, we could use n to model the subject’s subjective now, but for simplicity I’ll ignore the temporal domain throughout. In (7) we see a fragment of the mental state of Lingens who self-ascribes the property of being a lost amnesiac, while reading about Lingens in an encyclopedia and learning that this Lingens is working in Stanford. (7)The crucial realization that follows this state of confusion in Perry’s story, that he himself is Lingens, can then be represented by adding the condition ‘x =i’ to the belief box in (7).We use the dedicated indexical discourse referent i to represent the subject’s first person Note that there is no explicit anchor introducing the discourse referent i in (7). The idea is that, since we’re assuming that every mental state contains a unique, non-descriptive, first person representation of the self we can suppress such an anchor in our ADS notations.de re and de se, based on the idea that subjects have a privileged, direct access to themselves, ‘unencumbered by any form of descriptive content’ (p.8) and ‘immune to error through misidentification’ (p.51) . More on hallucination and faulty perception in Section de re and de se attitudes based on the idea that the actual reference of a mental file is always fully independent of its descriptive content, and hence that de re reference is always ‘direct’ and ‘unencumbered’ by any (accurate or inaccurate) descriptive information. Pending a more thorough exploration of this disagreement over the de re/de se distinction, I will follow Kamp in this paper. In Section 4The implicit self-anchor is like other anchors in that it introduces a discourse referent that is thereby accessible to all attitudes and other anchors. However, Kamp  assumes The philosophical literature on attitudes and ascriptions is replete with puzzles where an agent forms two distinct representations of something, failing to realize that it is actually the same thing: Frege’s Babylonians see a distinct morning and evening star, Quine’s Ralph sees mayor Ortcutt as distinct from the suspicious figure in the alley, Kripke’s Pierre believes London is terrible, but Londres is pretty, Perry’s shopper believes himself to be distinct from the shopper with the torn sack, etc. In the current framework, each of these cases involves a mental state description with two distinct files, each associated with different descriptive contents based on the different ways of being acquainted with an entity, but the external anchor maps both files to the same real-world entity, the actual causal source.(8)In Pierre’s mental representation of the world there are two distinct cities: one is the city he read about in his childhood in France, called Londres; the other is the city he lives in, called London. Based on these two epistemic links, he has formed two mental files, and through these he can have singular beliefs, hopes, etc.. We, as outside observers, know that his beliefs are in fact inconsistent. As represented in (8) by the external anchor, both mental files derive from a single source, so both beliefs are in fact de re about the same city. However, intuitively, the fact that Pierre believes the one city to be pretty and the other not does not entail that Pierre himself is irrational in the sense that his internal mental state is logically inconsistent.Pierre’s predicament, for instance, can be represented as follows Kripke : (8)In To reconcile this apparent contradiction I will provide in Section de re attitudes, based on our acquaintance with our surroundings, this is as it should be. Hence, Kamp’s , then the internal anchor is ‘ungrounded”’  from Section de re) belief about the letter instead. But how about the information that what I see is an envelope, is made of paper, is white? Or the information that x bears a certain name, or that y uses a certain expression to refer to x , but through the relations on which the files are based. The reference is the entity we are acquainted with (in the appropriate way), not the entity which best ‘fits’ information in the file. Recanati :35By contrast, we just saw that Kamp holds that the referent, i.e. the external anchor, should fit the descriptive information in the file, i.e. the internal anchor. Interestingly, this disagreement between Kamp and Recanati may well be related to Ninan’s argument, discussed in Section res, not just the relational acquaintance information.This brings us back to the divergence between Kamp and Recanati that we first came across in discussing the de re beliefs and imaginations about him, despite the fact that I have no direct perceptual relation to this ancient philosopher. In this case, the “epistemically rewarding” acquaintance link between Aristotle and me is not direct, but involves a long causal–historical chain of communication We capture vicarious anchoring – at least for names – with a condition ‘refer’ (speaker x used expression y to refer to z). For instance, when my wife starts talking to me about how Susan was being obnoxious at work today and I cannot link this name to any of my existing internal anchors, I may introduce into my mental state a new, vicarious anchor to refer to Susan: ADSs may give a pretty picture of, say, Pierre’s mental state, but what does it really mean to say that Pierre has a mental state as described in (8)? In what sense does Pierre’s mind contain a set of labeled DRSs?w′ is a doxastic alternative of a in w ) means that if you take a from world w, freeze her mental state, and place her in world w′, she will not be able to tell the difference. We then say that this person believes the proposition that it is raining iff all her doxastic alternatives are worlds where it is raining: a believes proposition p(⊆W) in w iff Dox ⊆ p.I take as my point of departure the familiar possible worlds conception of propositions and beliefs. A person’s beliefs are described by a set of possible worlds, her doxastic alternatives. We can explicate the notion of a doxastic alternative as follows: Dox to denote a set of centered worlds, i.e. 〈w′, a′〉 ∈ Dox means that if you place a in w′ and let her experience it from the perspective of a′, she will be unable to distinguish it from w as experienced from her own perspective (Lewisde se belief as follows: Lingens in w believes de se that he is lost (or: Lingens self-ascribes in w the property of being lost) iff Dox⊆ {〈w′, a′〉|a′ is lost in w′}.In order to deal with the essentially indexical discourse referent i we switch from worlds and propositions to centered worlds  and centered propositions , respectively. We take ive Lewis. We thenBul denoting the set of a’s centered buletic alternatives in w, and for imagination we have a set of imagination alternatives. A person’s full mental state can thus be characterized as a sequence of subsets of C, the set of centered worlds. Formally, these attitude characterizations of people across possible worlds are part of the model, i.e., a model D, W, I,〈Dox, Bul, Img…〉〉 with C = D×W, and As a first approximation, let’s assume the same story applies to other attitudes: to model desires we have a’s mental state in w iff the narrow contents of the belief, desire, imagination, etc. components within K are compatible with the corresponding sets of doxastic, buletic, imaginative etc. alternatives of a in w.So how do we relate this Lewis/Hintikka-style set-theoretic conception of an agent’s various attitudes, to our syntactic, DRT-based mental state descriptions? The central definition of ADT semantics says that an ADS K, requires a model-theoretic interpretation of the various labeled parts of a mental state description in terms of centered worlds. This in turn requires an intensional semantics for standard DRT, which I sketch below.Making this precise, taking into account also the anchors in U(K), the so-called universe, contains the discourse referents. The bottom part, Con(K) contains the conditions, which are either atomic formulas ), or complex ones containing subDRSs (e.g. ¬K′ or K′→K″).Let’s start with some syntactic terminology. A DRS consists of two compartments. The top compartment, D. For our current purposes we need an intensional parameter. Since we take doxastic and buletic alternatives to be centered worlds, we’re going to need those as our intensional parameter. We say that an embedding g verifies a DRS K in c iff it verifies all conditions of K: (10)g⊧cK iff for allψ∈Con(K):g⊧cψ.The central notion of DRT semantics is that of a verifying embedding, which is a partial function from the set of discourse referents to the model’s domain (11)Condition verification is defined by cases. For example: ac, the agent parameter of the centered world of evaluation c. Our central definition of truth involves the truth of a DRS K in a centered world c relative to an anchor f: (12)From there we can define also the (centered) proposition expressed by K relative to f as the set of centered worlds in which K is true relative to f. (13)The centered proposition expressed by DRS K simpliciter is a special case: [[K]]=[[K]]∅.The essential indexical discourse referent i can be dealt with as a special case of “anchoring” as it was originally developed for standard DRT by Kamp and Reyle . Like thThis concludes our crash course in DRT syntax and semantics. Now let’s return to the syntax and semantics of ADSs.bel-labeled DRS with the set of doxastic alternatives, the des-labeled DRS with the buletic alternatives, etc. We run into a number of complications here, so to keep things manageable I’ll suggest some effective simplifications.An ADS contains descriptions of various mental files and attitudes in the form of labeled DRSs (boxes). We’ll need to decide how to line up these components of the ADS with the attitude sets provided by the model. The idea is to match the bel- or des-labeled DRSs, but the Kripke/Hintikka model provides only one set of doxastic alternatives for an agent (at a given time and world). One of the reasons for allowing multiple belief boxes is to address the problem of logical omniscience using the fragmentation strategy of Lewis One final restriction is that I will focus only on dependencies involving anchors, ignoring potential dependencies between attitudes.f an external anchor that maps all internally anchored discourse referents in D of the model). The belief content expressed by bel-labeled DRS in f, as defined in standard DRT (cf. (13)): [[Kbel]]f. The same holds for the other attitudes. The wide content of my hope in the letter example is then the set of centered worlds in which the actual letter, the causal source of my internal anchor, is a notification of acceptance. Pierre’s wide belief content is the set of worlds w such that the actual city, London, is pretty in w and not pretty in w, i.e., the empty set.We can now use convenient notations like Note that internal anchors and the attitudes provided by the model play no role in the definition of wide content; (14) just defines the singular propositions actually expressed by mental states. Psychologically speaking, whether or not anch? That is, what attitude – if any – does an agent have toward the content of her mental files. From the three attitudes considered so far, belief is the most promising candidate. If an internal anchor contains the descriptive condition ‘see’ or ‘named’ this entails that I believe that I’m seeing x, or that x is called ‘Londres’. The interpretation of internal anchors as beliefs is consistent with the views of both Recanati, as brought out in the discussion with Ninan reconstructed in Section 8Internal anchors introduce some further complications. First of all, what attitude should we associate with KDES), and the imagination DRS (Kimg), and then compare those propositions with the sets of doxastic, buletic and imaginative alternatives in the usual Hintikka/Kripke fashion: (15)For some very simple examples where all non-doxastic attitude components in an ADS are self-contained, proper DRSs this will give adequate results. However, the whole point of the ADS structure is to allow referential dependencies between attitudes, which involves attitude DRSs with free variables. In the letter example, for instance, we saw the singular hope that it – the letter – is a notification of acceptance. Formally, the desire DRS in that example contains the free variable x and hence does not express a proposition: [[KDES]] in (15b) is undefined.We are now in a position to formulate the central definition of ADT semantics a bit more precisely. The idea is to use the intensional DRT semantics to compute the propositions expressed by the merged belief-plus-anchors DRS . This simple fix won’t work for other attitudes because merging the anchors in (15b) would entail that the agent wants the contents of her mental files to be satisfied.The problem with (15) was that we could have free variable in What we need is an external anchor to fix the reference of these free imagination/desire variables “in the background”. But where do we get such an external anchor from? Answer: we construct it from interpreting the material in the internal anchors. In other words, we want to evaluate the desire and imagination components of the ADS relative to embeddings and doxastic alternatives that verify the conditions in the internal anchors.Bul and Img with two-dimensional or “parasitic” notions of desire and imagination, Bul∗ and Img∗. Following a suggestion from Ninan , say, is the set of worlds compatible with what the agent a in w would imagine if her belief set were the singleton {c}, i.e. if she were convinced she was ac inhabiting wc without any uncertainty The definitive narrow psychological semantics for ADT can now be stated as follows: a’s mental state given by Dox and Bul∗ iff the following two conditions hold: (a) c∈Dox there is an embedding f such that, in c: f(x) is an envelope, f(y) a mailbox, f(x) is in f(y), the belief center ac sees f(x) and f(y) in front of her, and f(x) is junk mail. In other words: the agent believes she’s seeing a piece of junk mail in an envelope in her mailbox. The second condition, (b), says that for every doxastic alternative c and every f that verifies the merged internal anchor DRS in c  is an envelope that ac sees in a mailbox f(y) in front of her in c): Bul∗ ⊆ [[KDES]]f, i.e., f(x) is a notification of acceptance in all buletic alternatives of the agent, relative to the background assumption of the agent inhabiting that doxastic alternative c. In other words, (b) says that, relative to each doxastic alternative c, with relevant letter f(x), the agent wants f(x) to be a notification of acceptance. Taken together, (a) and (b) adequately capture the belief and hope described in the scenario: the agent believes the letter she’s looking at is a piece of junk mail, but, hopes that it, the letter she believes she’s seeing, is a notification of acceptance.Applied to the letter example , but minus the intention), (17)The ADS in (17) is a straightforward representation of this Fregean double vision scenario. On the one hand, the wide content expressed by the belief box is the inconsistent proposition that Clemens is both famous and not famous. On the other hand, the ADS in (17) captures Lucy’s narrow, psychological mental state (in w) iff in all her doxastic alternatives there are two entities of which one is named Twain and is famous, and the other is named Clemens and is not famous – which is not at all inconsistent.Now let’s revisit Ninan’s challenge to Recanati and see how our ADT deals with it. As you’ll recall, Lucy is acquainted with Samuel Clemens, the writer, in two different ways. On Recanati’s picture, she has two co-referential mental files, one on Clemens, containing the information that he bears that name and is not famous, and one on Twain, containing the information that he bears that other name and is a famous author. In ADT we represent Lucy’s mental state at this point as containing two internal anchors, externally anchored to the same individual, introducing two mental discourse referents.11(18)To be sure that this formula indeed makes sense we have to check its narrow semantic interpretation. The ADS in (18) captures Lucy’s mental state iff (a) all her doxastic alternatives have a verifying embedding mapping x to a famous Twain and y to a non-famous Clemens, as above, and (b) for all doxastic alternatives c and all anchor-verifying-embeddings f (i.e. with f(x) is named Twain in c and f(y) is named Clemens in c): Img∗ ⊆ [[Kimg]]f. The (b) requirement boils down to the condition that in all of Lucy’s imagination alternatives, relative to each of her doxastic alternatives, f(x), the doxastic alternative’s Twain, is not famous, but f(y), the doxastic alternative’s Clemens, is. Again, there’s no inconsistency, and the interpretation seems to capture precisely what’s going on in Lucy’s head, using only the tried and tested formal machinery of possible worlds semantics.Now, the challenge involves Lucy imagining contrary to what she believes that it were Clemens who is famous instead of Twain. Since we represent imagination separate from belief this should pose no problem: de re, where we clearly have an imagination that is inconsistent with the information represented in the internal anchor .In closing let me address one potential objection. An observant reader may well object that the reason we avoided inconsistency is that already in (17) we chose to represent the properties of being famous or not famous in the belief box rather than as part of the internal anchor, contrary to what Recanati would have done in his mental file framework. To drive this point home she could then point to Ninan’s earlier puzzle about counterfactual or Ninan , 2012. F(19)Fully in line with the description of the case, our semantics says that (19) captures Ralph’s (narrow) mental state if he believes there to be someone he is seeing, and relative to all his belief alternatives c  he is seeing there (and then)), he imagines that he is not seeing f(x).As it turns out the syntax and semantics provided above deals with such trickier cases as well – in fact, I incorporated one of Ninan’s own solutions, the primitive two-dimensional notion of belief-relative-imagination, for just this purpose. Consider a minimal representation of Ninan’s  puzzle: I have presented a DRT-based formal theory for the representation of complex mental states. It analyzes singular attitudes via descriptive internal anchors that are externally anchored to objects in the world. Much like Recanati’s mental files, ADT’s anchors are, as Ninan puts it, “mental representations whose primary function is to carry information about objects”.de se attitudes, double vision, hallucination, vicarious anchors, and, finally, Ninan’s counterfactual attitudes.The crucial difference between Kampian ADSs and Recanati’s mental files is that an ADS represents the different attitudes  separately, as distinct DRS boxes that all have access to the discourse referents introduced by the anchors. In this paper I have shown that the resulting framework is expressive enough to describe mental states involving doxastic and non-doxastic attitudes,


The CanMEDS framework has been widely adopted in residency education and feedback processes are guided by it. It is, however, only one of many influences on what is actually discussed in feedback. The sociohistorical culture of medicine and individual supervisors’ contexts, experiences and beliefs are also influential. Our aim was to find how CanMEDS roles are constructed in feedback in a postgraduate curriculum-in-action. We applied a set of discourse analytic tools to written feedback from 591 feedback forms from 7 hospitals, including 3150 feedback comments in which 126 supervisors provided feedback to 120 residents after observing their performance in authentic settings. The role of Collaborator was constructed in two different ways: a cooperative discourse of equality with other workers and patients; and a discourse, which gave residents positions of power—delegating, asserting and ‘taking a firm stance’. Efficiency—being fast and to the point emerged as an important attribute of physicians. Patients were seldom part of the discourses and, when they were, they were constructed as objects of communication and collaboration rather than partners. Although some of the discourses are in line with what might be expected, others were in striking contrast to the spirit of CanMEDS. This study’s findings suggest that it takes more than a competency framework, evaluation instruments, and supervisor training to change the culture of workplaces. The impact on residents of training in such demanding, efficiency-focused clinical environments is an important topic for future research. In order to prepare physicians better for their future practice and, ultimately, increase the quality of care, competency frameworks have been widely adopted. The assumption that defining professional standards for educational purposes can lead to better care is made explicit in the slogan of the CanMEDS framework: ‘Better standards, better physicians, better care’ Frank . CompeteAlthough CanMEDS is the most widely adopted competency framework, little research has been conducted on how curricula informed by it operate in practice. Previous research has focused mainly on the development of assessment instruments .Professional was changed into Reflective Professional , or oral presentations. These settings influence the topics that are discussed in the feedback conversations, and therefore affect how the roles are constructed in our data. A broad variety of those feedback settings were included in this study, in an attempt to capture the broad discourse of CanMEDS roles as it occurs in the clinical practice. Table The research team was assembled to represent a range of disciplines, which could inform the data interpretation. NR is a Ph.D. candidate in medical education that has a master’s degree in educational sciences. TD is an experienced internist and expert in qualitative research. RG is a medical doctor and programme director in Internal Medicine who helped implement the CanMEDS framework in the Netherlands. JB is a medical doctor and dean of education in a University Medical Centre. JC is a psychologist and professor in medical education. DJ is a professor in medical education whose background is in veterinary medicine. NR, RG, JB, DJ and JC are all native Dutch speakers, whereas TD is a native English speaker who is able to read Dutch. TD used his relative unfamiliarity with the Dutch language to look critically at the written comments, which helped the research team maintain a questioning attitude towards how language was used.Health advocate was not addressed in the feedback system in this setting, therefore the researchers did not have data on this role. Since physicians have always been expected to be medical experts and scholars, formalising the non-technical roles of Communicator, Collaborator, Manager, and ReflectiveProfessional is a novel feature of CanMEDS. The research team therefore chose to concentrate its analysis on those four roles.NR first made herself thoroughly familiar with the data by reading and re-reading all written comments on all forms. Since the dataset consisted of 3150 written feedback comments on 591 feedback forms and the sheer quantity of data can compromise the quality of critical discourse analysis, NR purposefully selected a subset of data that was likely to be most informative. First she excluded all forms with comments that were not amenable for textual analysis because they were too short, fragmentary, or general; e.g. ‘well done’. After that, she purposively chose the 100 forms (20 from each feedback setting) with 297 written comments, which best illustrated the variation in the texts as the dataset for analysis.‘Doing and not just saying’ This tool emphasises the notion that any piece of textual communication consists of more than what is literally said. It examines the social effects that each piece of text might be expected to have. For example, a supervisor who commented that a resident had used over-familiar language when speaking to patients was creating greater professional distance between physician and patient.Significance this tool can be applied to analyse how language increases the significance of certain things and reduces the significance of others. This was exemplified in the dataset by, for example, underlining words, using punctuation such as exclamation marks or brackets, and sentence structure. The following suggestion for improvement illustrates how significance was reduced: ‘No suggestions. But I would like to point out a pitfall: be aware that you could come across business-like to patients (not necessarily a disadvantage)’ The feedback comment begins by stating there are no suggestions for improvement, continues with a formative statement, and ends by saying that the observed behaviour is not necessarily a disadvantage. The net effect of this comment is to sanction ‘coming across business-like’ within the discourse of medical communication. This example also shows how our discourse analysis was ‘critical’, because it shows how coming across to patients as ‘business-like’ is part of the relationship the supervisor builds with the supervisee within the institution of medicine.NR and TD led the application of a set of fine-grained critical discourse analysis “tools” Gee  to draw Communicator and Collaborator of a random subset of 25 forms—five from each setting—by applying Gee’s tools. They read the texts closely and open-coded them with words or short sentences that captured meaning(s) they found in the text. They constantly compared . Ensured good speed (truncates a clarifying answer in a friendly way)”.The discourse included how residents came across: “Be aware of your use of language (too familiar)”. It also included how they created room to involve patients in conversations: “Held a bad news conversation in a calm, empathic, manner with family + patient. Created space for questions and expression of emotions”.Being assertive meant being pro-active, delegating tasks, and telling others what to do. Exercising leadership meant showing responsibility, being in control, and approaching the right people. In the following text, the supervisor equated the metaphor of ‘being king’ with leadership “Be concrete about the policy. Hand over to the ward. Looking ahead would make you king”. Cooperation was constructed in terms of being approachable, conferring, and liaising with others: “Critical about colleagues’ and own performance; but now it is time to propose treatment plans yourself”. Engaging included having good contact with others and inviting others to contribute to discussions: “Focus your presentation  a bit more on what you consider to be essential issues. Invite others to be more engaged in the discussion and provide you with feedback”.The discourse of communicating with staff also strongly emphasised time-efficiency, being to-the-point, concise and clear, and giving just enough detail without wasting time: “Your narrative explanation of the essence of the handover sheet was concise and to the point. Despite high speed, good contact with audience”. Other features of the discourse were, on the one hand, being assertive and exercising leadership, and on the other cooperating and engaging others. Collaborator: a consensual one and one that constructed an imbalance of power in favour of physicians. The consensual Collaborator was a cooperative individual, ready to work with others. Exercising leadership by taking the initiative and being assertive were part of this discourse: “Pleasant collaboration, gives a sense of a joint undertaking. Great sense of responsibility. Clear communication”. The alternative discourse placed residents in positions of power, as people who were in charge of other people and had to defend their position against others: “If people do not do what you want, I think you find it difficult to take a “firm” stance”. This discourse constructs a firm, directive type of leadership: “Work on limiting your responsibility and, if necessary, “resistance”. Articulate assignments more clearly, be more assertive and directive”.There were two different discourses of Manager: ‘managing oneself’, ‘managing others’ and ‘managing means’. ‘Managing oneself’ meant having fore-, back-, and over-sight, being meticulous, and knowing and respecting one’s own boundaries: “Try to do important things first (diagnostics). Make lists, try to schedule tasks. Complete one thing so you can start the next one. Try to be a bit faster.” ‘Managing others’ meant managing staff, knowing when to consult or check others, and being directive and delegating tasks: “Extra attention to triage/prioritizing. Don’t do everything yourself but allocate/delegate tasks in a directive way!” ‘Managing means’ included deliberately choosing diagnostic tests according to how they could contribute to a treatment policy whilst keeping unnecessary tests to a minimum: “Considers advantages and disadvantages, usefulness of diagnostic tests”. Being goal-directed and taking the initiative in making policy and treatment plans was a consistent feature of the discourse of Manager. The Manager role also constructed physicians as people who work fast and prioritize tasks so everything is completed in time.The discourse constructed three facets of a Reflective Professional constructed residents as trainees, members of healthcare teams, and all-round professionals. Residents ‘as trainees’ were to ask for feedback, ask for help, seek to improve themselves, and know their limitations: “Maintains good oversight of a broad differential diagnosis. Presents himself as inquisitive and keen to learn (open). Very good discussion of 2 (more) complex cases”. Residents ‘as team members’ were professionals who were open and approachable, conferred with others, made decisions in dialogue, but also stood up for themselves and took the lead in discussions: “Remain critical about patients that are handed over from other colleagues. Is everything correct? Relevant and logical?” Residents ‘as all-round professionals’ managed time, were critical, had oversight and went deep into problems to make a working diagnosis or treatment plan: “Fine attitude. Dares to go back to the beginning when hits a dead end”.The discourse of Communicator, Collaborator, and Medical Expert, but not even mentioned in relation to Manager, Reflective Professional, and Scholar. The relationship between residents and patients was explicitly distant: “No suggestions. But I would like to point out a pitfall: be aware that you could seem business-like to patients (not necessarily a disadvantage)” and: “Is approachable to other professionals. Clear to patient.“The discourse of Communicator included communicating with patients in friendly ways, and making patients feel comfortable and understood, rather than being comfortable or understood: “Reflect on patient’s feelings so they will feel better understood”. Patients tended to be positioned as subjects of communication or collaboration rather than equal participants. The word ‘listen’ was strikingly absent from the discourse—it was used just once in 3150 items of text.Patients were relatively rarely mentioned, even when the analysis was extended to the roles of medical expert and scholar. Although patients were not privy to some settings, such as CAT and Oral presentation, settings such as Patient Encounters and On-Call directly involved patient interactions. The absence of patients in these data, whilst a finding in its own right, limits the depth to which the analysis could be taken. Patients were part of the discourses of This study has shown that the discourse of physicians, in the authentic workplace settings we studied, constructs the CanMEDS roles in very different ways from what the originators of CanMEDS might have intended. There were three striking differences. First, there was a discourse of collaboration according to which residents were directive towards others, exercising a firm kind of leadership. Although directive types of leadership can be effective in critical or extremely high-workload settings, empowering leadership styles function better in creating a positive climate and increasing cohesion within functioning teams  were constructed in a more limited fashion in our data than in the original CanMEDS framework, this was often compensated for by feedback on other roles. It appears that there is some unifying discourse of ‘a good physician’ that can be viewed through different competency spectacles to highlight certain aspects of practice or performance. This aligns well with common critique that in the CanMEDS framework the complex profession is rather artificially divided into seven roles, whereas the reality is that roles overlap and complement each other (Whitehead et al. A distinctive feature of this study was the detailed textual analysis of relatively fragmented data. Pressure of time obliged physicians giving feedback to express what they saw as key points in limited words, and therefore the fragmentary data indicate what was considered most important. Another strength is that these data are the original feedback that the residents received. Although other methods, such as interviews or focus groups, might result in a more complete articulation of the discourse, these methods would also allow for more socially desirable responses whereas our naturalistic data reflect the actual process-in-action. We must acknowledge, however, that although we received feedback forms from all 120 residents, we are not sure whether they submitted all their feedback forms for inclusion in our analysis, so some unseen bias might have operated.The varied backgrounds and roles taken on by members of the research team in analysing the data increased the rigour in this study. Qualitative analysis is an inherently subjective process, and we cannot guarantee that a different team would have arrived at the same conclusions. However, we worked together as a team, capitalising on how our different backgrounds gave different perspectives on the data.Our conclusion that patients were marginalised in the discourse was undoubtedly influenced by the data having captured conversations between physicians. Whilst we can draw no conclusions about the conversations that might have gone on between a teacher, a resident, and a patient, we can draw the conclusion that the discourse of the CanMEDS roles in this context does not give patients a central place.This example of how outcomes-based education operates in practice highlights that defining learning outcomes and using them to structure a curriculum does not automatically result in the intended changes in practice. Although the qualitative methods we used in this study do not aim to produce generalizable results, readers who recognise similarities between their workplace settings and ours may find them transferable to their own competency-based training programmes (Kuper et al. Health Advocate is constructed in practice. The feedback system used in this study did not generate feedback on this role. Analysing the discourse of this role in practice and especially the position of patients within it could complement this study.Another avenue for future research would be to study how the role of It is striking that supervisors’ discourses constructed a tension between being patient-centred and being time-efficient. Since we do not know whether that tension is inevitable, it is a matter for further research. It is questionable, however, whether such a high-pressure environment is a fitting place to engender patient-centred attitudes. It seems challenging for residents to combine the roles of a well-functioning physician and a trainee in a setting that focuses so strongly on being fast and to the point. Asking a supervisor to spend valuable time to explain something or observe and give feedback in such a time-pressured setting could be difficult for residents. Spending as little time as possible with a patient while trying to manage to listen, comfort and deliver high quality care is certainly a challenge.


Carbohydrates constitute the most abundant class of biomolecules on Earth. They are “global players” and indeed play many roles. They store energy and water. They serve as metabolic intermediates. They function as stable, raw materials or as intelligent, bioengineered products. They are even active in “social media”, that is, they mediate communication between cells and they store information by molecular shaping. They adopt more forms and shapes than one can conceive in 24 hours. They exist as small mono- or oligo-saccharides, as large and highly complex polysaccharides, and in conjugation with, for example, proteins and lipids, forming a kingdom of glycoconjugates that are found on every cell surface.Hence, carbohydrates are like Alice’s Wonderland—they can assume any role. They can be big or small, sweet or sour, silent or explicit, crucial or, less often, irrelevant. What we do understand, however, is that carbohydrates are essential to life, both in a state of health and in the case of disease. Consequently, the glycosciences have gained great respect in chemistry and biology. It is now generally considered important to be able to synthesize carbohydrates and glycoconjugates, to purify and analyze their structures, and to advance our understanding of the biology of carbohydrates in living systems. The two Thematic Series “Synthesis in the glycosciences” I  and II –35 that Now, a third “sweet” Thematic Series is presented in which the borderline between Alice’s Wonderland and real-life applications of carbohydrates has been deliberately crossed. The structural world of natural sugars has been extended towards artificial carbohydrate architectures to achieve potential innovations offered by glycoconjugates. Since 2011, approximately 70 group leaders from 21 countries have gathered in an international COST (European Cooperation in Science and Technology) Action, funded by the European Union (EU) to address the challenges and opportunities in the field of glycosciences. This enterprise has focused its research on the potential applications and social benefits that lie in the field. For instance, multivalent glycoconjugates can be used as anti-adhesive drugs against microbial infections. They could be developed into bioimaging agents that can target specific tissues. Indeed, they will certainly find applications in materials science and in medicine, for example, in drug delivery or diagnostics . ConsequI am very grateful to the five colleagues of our international network who have dealt with the manuscripts submitted for this Thematic Series entitled, “Multivalent glycosystems for nanoscience”. Bruce Turnbull (United Kingdom) has amassed contributions dedicated to “Synthetic methods to make multivalent glycosystems”, Alessandro Casnati  has delivered manuscripts regarding “Glycoconjugates for the delivery of drugs and diagnostic probes” for review, Roland Pieters (The Netherlands) has taken care of manuscripts related to “Glycoconjugates as anti-pathogenic agents”, Vladimir Křen (Czech Republic) has supervised the area of “Glycoconjugates as modulators of the immune system”, and finally, Jean-Louis Reymond (Switzerland) has received contributions regarding “Glycosystems in nanotechnology”. As a result, 27 excellent publications are presented in this fine collection. I would like to sincerely congratulate all contributors for their research and results. We can be proud of what has been achieved. We should be inspired by what has been suggested. And we have definitely been encouraged by the delightful experiences we shared in an international network of colleagues and friends. In our community, we have been obliged to diversity and equality, to liberty and freedom, and to truth and reason. Therefore, in the name of all glycoscientists who have contributed to this Thematic Series, I finally express the hope that the ethical standards that we have applied in our work will provide guidance and confidence, if our healthy world will once have to face the challenges of distrust, fanaticism and hate, as it is currently the case in many areas of this planet.Thisbe K. LindhorstKiel, October 2014


Medication nonadherence has a significant impact on the health and wellbeing of individuals with chronic disease. Several mobile medication management applications are available to help users track, remember, and read about their medication therapy.The objective of this study was to explore the usability and usefulness of existing medication management applications for older adults.We recruited 35 participants aged 50 and over to participate in a 2-hour usability session. The average age ranged from 52-78 years (mean 67 years) and 71% (25/35) of participants were female. Each participant was provided with an iPad loaded with four medication management applications: MyMedRec, DrugHub, Pillboxie, and PocketPharmacist. These applications were evaluated using the 10 item System Usability Scale (SUS) and visual analog scale. An investigator-moderated 30-minute discussion followed, and was recorded. We used a grounded theory (GT) approach to analyze qualitative data.P<.0001) though it offered a drug interaction feature that was among the favorite features of participants. The usability scores for MyMedRec (56/100), DrugHub (57/100), and Pillboxie (52/100) were not significantly different and participants preferred MyMedRec and DrugHub for their simple, linear interfaces.When assessing mobile medication management applications, participants struggled to think of a need for the applications in their own lives. Many were satisfied with their current management system and proposed future use only if cognition and health declined. Most participants felt capable of using the applications after a period of time and training, but were frustrated by their initial experiences with the applications. The early experiences of participants highlighted the benefits of linear navigation and clear wording  when designing for older users. While there was no order effect, participants attributed their poor performance to the order in which they tried the applications. They also described being a part of a technology generation that did not encounter the computer until adulthood. Of the four applications, PocketPharmacist was found to be the least usable with a score of 42/100 (With training, adults aged 50 and over can be capable and interested in using mHealth applications for their medication management. However, in order to adopt such technology, they must find a need that their current medication management system cannot fill. Interface diversity and multimodal reminder methods should be considered to increase usability for older adults. Lastly, regulation or the involvement of older adults in development may help to alleviate generation bias and mistrust for applications. As many as half of all prescriptions are not taken as prescribed, costing the US health system over US $100 billion per year . A decadFor chronic conditions such as diabetes, hypertension, and dyslipidemia, up to one-half of individuals will stop taking a medication as prescribed within the first year -10. For The language of adherence is complex and evolving. Adherence generally refers to how a patient takes a medication in relation to the prescribed timing, dose, frequency, and duration of therapy ,15. TrueThe term “compliance” is often used interchangeably with adherence, but has fallen out of favor in recent years for its paternalistic implication that a good patient passively follows physician instructions . “ConcorMobile health, or mHealth, applications offer one potential solution to help patients adhere to prescribed therapy. Over one-half of American adults own a smartphone and over one-third own a tablet ,20. One For mHealth interventions to be both effective and accepted by end users, it is important to understand the differences between individuals who have, who will, and who will never adopt mHealth interventions. In the same way that there are barriers to medication adherence, there are also likely to be barriers to digital adherence. For example, user experience research has found users over age 50 can be easily frustrated by conceptual misunderstandings about the design of mobile devices ,24. TheyAmong the thousands of mHealth applications commercially available, many are designed to help individuals organize and manage how they take their medications. Adults over age 50 make up the majority of medication users  and mediWe used a mixed-methods approach to examine the usability and user perceptions of commercially available mobile medication management applications at the University of Waterloo School of Pharmacy. We included a qualitative assessment of user experiences using a grounded theory (GT) approach, which is reported according to the consolidated criteria for reporting qualitative research (COREQ) . We alsoWe identified medication management applications by searching the Apples iTunes store using the terms “medication”, “prescription”, and “drug”. After exploring the descriptions of over 100 mobile applications, two researchers identified and downloaded 22 applications focused on helping consumers manage general medication therapy rather than medications related to a specific illness. The researchers independently explored the functionality of each application by entering a list of prescription and nonprescription medications, setting reminders, and reviewing the applications with friends and family members over age 50. After 2 weeks, the research team reconvened and chose five applications for the final review, each highlighting a different feature, such as appearance, reminders, drug information, drug interactions, and connectivity .We chose MyMedRec (Version 1.0.4) for its simple features and linear data entry. MyMedRec was developed as a collaboration between the Institute of Safe Medication Practices Canada, Canada’s Research Based Pharmaceutical Companies (Rx&D), and several health professional association across Canada. We chose Pillboxie (Version 2.6) for its graphical interface. A registered nurse in the United States developed Pillboxie to be a virtual medicine cabinet. We chose DrugHub (Version 1.3) for its drug information feature. The Great-West Life Assurance Company, a large provider of health insurance in Canada, developed DrugHub as a service to the general public. We chose PocketPharmacist  for its drug interaction feature. A pharmacist in the United States designed PocketPharmacist to provide users with medication information and the ability to check multiple drugs for any interaction. Finally, we chose MediSafe  for its cloud-synced, family-centered profile sharing features. MediSafe was designed in Israel. At the time of the study, MyMedRec, Pillboxie, DrugHub, and Pocket Pharmacist were available for the iOS system, and Pocket Pharmacist and MediSafe were available for the Android OS.We included participants aged 50 years of age and over, who could speak and read English and who took some form of chronic medication. We did not require participants to have previous experience using a touchscreen device. We recruited participants by posting flyers and attending events at community centers and medical clinics.Our sampling strategy reflected a GT approach to qualitative analysis . As we dOur final sample included 35 participants aged 52-78 years (mean 67 years), 71% (25/35) of whom were female . All butWe used a group-based assessment model to meet our objectives as it allowed us to test multiple participants at once while capturing moments of consensus, censoring, and dissonance among new, novice, and experienced mobile device users  32]. Th. Th32]. We began each session with a meal or light refreshments and a 10-minute discussion of what medication management meant to participants. Each participant was provided with a third generation Apple iPad and given a series of ordered tasks. Participants in the purposive sample worked from the simplest application to the most complex . For the theoretical sample, we randomized the order of the applications to assess order effect. We also introduced styluses and gave participants access to smaller devices such as the Apple iPhone 4, Apple iPod Touch, and the Samsung Galaxy S3. We introduced the fifth application, MediSafe (Medisafe Project LTD), because it had a unique pillbox graphical interface and had more connectivity than the other study applications, but excluded it after one session because participants complained of several system errors.For each application, we asked participants to complete a series of application-specific tasks that could include the following: (1) adding prescription, nonprescription, and natural medicines, (2) scheduling reminders, (3) recording when a dose was taken, (4) emailing profiles, (5) reading drug information, and (6) scanning for drug interactions. We provided each participant with a set of standardized medication bottles that represented a clinically significant drug-drug interaction: warfarin, aspirin, and St. John’s Wort; clarithromycin and atorvastatin; ramipril and ibuprofen; furosemide and ibuprofen; and levothyroxine and calcium carbonate . We provWe concluded each session with a 30-minute focus-group discussion. The guided discussion included questions on overall user experiences, ease of use, concerns over the potential for data input errors, perceived quality of the information provided, preferences for different features, and expected adoption by adults over age 50. On completion, participants were given a $10 gift card in appreciation for their time.Participant demographics and experiences were gathered using paper-based questionnaires and summarized using descriptive statistics. Participants were encouraged to write comments about their experience as they tried each application. Two researchers also recorded their observations of participant experiences, including questions asked, errors observed, and tasks users could not complete. Focus-group discussions were audio recorded. All data, including audio recordings, field notes, and participant comments, were transcribed and de-identified by a researcher and double-checked by a second researcher.After each application, participants rated usability on a visual analogue scale (easy/difficult) and the 10-item SUS  28]. Th. Th28]. We used a GT approach as it allowed us to develop a theoretical interpretation of the meanings we observed older users attaching to mobile medication management applications without defining the phenomenon a priori . For GT,Our data analysis, which began after the third session, followed three levels. For the first level of analysis , two independent researchers coded data by briefly summarizing each line of data. In the second level of analysis , the two researchers organized the summaries into categories . To asseF test indicates PocketPharmacist had a significantly lower usability score compared with all other applications (P<.0001), whereas MyMedRec, Pillboxie, and DrugHub were not significantly different from each other. Order of use did not affect SUS scores when added to the model (P=.44).Based on the SUS scores , the F tFor participants, the early experience of learning to use a mobile medication management application was frustrating, overwhelming, and challenging but it was also fascinating, fun, and enlightening . As one With a little bit of practice, with all of them, [it] would become a lot easier to like anything. You know, the first try at any of these, regardless whether you’re familiar with an iPod or an iPad, it does not go smoothly. They’re not terribly intuitive.Male, Group 8Early on, we observed that many participants were reluctant to learn by trial and error. They appeared to feel vulnerable or lost and often worried about making a mistake. Many spoke of trying not to break the device. We learned to start each session saying, “Don’t worry, you won’t break it” to encourage learning by trial and error. In describing the need for support, one participant explained,there are a few steps missing I think in each of these [applications]…I didn’t know how to get from a certain screen to another, it wasn’t very evident, but once I was shown, I think it’s easy to use.Female, Group 3Eventually, within the 2-hour timeframe of each session, almost all participants became comfortable inputting information. The experience of learning the applications can be compared with the implementation of new technologies in other areas of life, including banking and transportation:I guess while I was doing it, I kept thinking about ok, when I was first ordering airline tickets on the computer, or bus tickets on the computer, the difference after you’ve done it a few times as opposed to the first time, trying to figure out which buttons to hit.Female, Group 1However, some individuals had more difficulty than others, and would likely need significant support, both technical and emotional, to adopt the application into their lives. One participant who found the applications extremely difficult to use highlighted this challenge when he concluded,I don’t want an app, I don’t ever want to see one in my house or anything because to me they’re just frustrating. Frustration devices.Male, Group 5When asked if they were willing to persist in using the applications, the opinion was divided between willingness to persevere until comfortable given a perceivable benefit, or only for 15 minutes due to impatience and lack of time.I’d spend a bit of time, half an hour to an hour, not even an hour, half an hour to learn something like this, yes.”Female, Group 6Patient Need,The perceived usefulness of a mobile medication management application is closely related to the needs of the end user. Users who believed their current adherence strategies are sufficient are likely to consider the applications as less useful. One participant highlighted the importance of perceived usefulness by sayingI like things fast and dirty, I would just give up. Say forget it, I’ll just do it my old fashioned way.Male, Group 6Strategies for remembering to take medications included scheduling all medication doses at once, using a physical pillbox, wearing a digital watch with an alarm, and/or having a pharmacist who provides telephone refill reminders. Many also carried a written or printed list of medications in a wallet or purse. Participants would only speculate future use under the assumption of declining health, declining memory, or the need to manage medications for a relative.I’m looking at it from the point of view of my mother when she was elderly. She was confined to a wheelchair and okay, she wasn’t computer literate. But had she been, you know, if she’d had it with her, in her chair, she could’ve looked at it and said ‘yeah hey I need to take this pill’ or there’s a reminder, or ‘no I can’t’, somebody’s making lunch for her, ‘no I can’t have grapefruit because I’ve taken Lipitor’ or something like that. I’m sure that kind of information would have been good. If you’re not terribly mobile, I think something like that, and in this day and age, as time goes on, people are much more computer literate and can handle these things much more easily and how do you say, you know, that would be much more useful, if you’re concerned, if you’re taking a lot of medications. Because I know some people who are taking seven or eight a day.Female, Group 1Most also felt it was only appropriate for health care professionals to recommend an mHealth application if they had used it themselves and if the patient was willing, needed it, and was technologically literate.Mobile medication management applications can be conceptually separated into two categories: adherence  and information . An “ideal" application was described as including both features:I found MyMedRec covers everything, it’s ordered properly. But it did miss the other little features, the little pillbox [in Pillboxie] and then the [drug interaction] check [in Pocket Pharmacist] and then [DrugHub]. I guess it’s the access to the information and whether you could check interactions and things like that. If somehow you could incorporate that into the [MyMedRec] then it would be perfect.Female, Group 3In their daily lives, all participants sought information about their medications in order to stay aware and avoid adverse events. The drug information features were seen as providing background information on a new prescription, supplementing the information given by a health care professional, and satisfying curiosity.For me, personally, I take a lot of pills everyday…I’ve got it so down and whenever I take a prescription, well the pharmacist is very good to go over things, but I always, always make a point of reading the literature when I get it.Female, Group 2While the most popular source of drug information was the pharmacist, some participants worried that too much information was dangerous and that the applications were replacing the expertise of the pharmacist. Given the choice, participants preferred an in-person conversation for important information:Something like drug interactions? I don’t want to be bothered by anything like that. I mean I know I should, but I want my pharmacist to say to me when I go in, don’t take this or do take that. You know what I mean? I didn’t go to school, I don’t want to have the responsibility of worrying about that...Female, Group 1When asked to estimate the cost of applications, most participants valued drug information applications over adherence applications. Participants who had purchased applications expected to pay less than Can $5 , but those who had never purchased an application expected to pay up to Can $100 or a monthly fee. Most did not take into consideration the cost of the device itself.There was a competing relationship between functionality and complexity. The “ideal” application may actually be two applications, one for adherence and another for information. Separating the features into two applications would maximize the functionality of both features rather than trying to do both incompletely.I think there’s two parts of it. There’s reminding people to take the medication but then there’s the whole information side with what’s working with what. So it almost seems like you should have two apps.Female, Group 7For many participants, linear navigation was preferred. Participants commonly struggled with going “back and forth”, essentially, moving forward to enter a medication into their profile and once completed, going back to enter a new medication . Moving I’m impatient as hell. So when it comes to an app, it’s got to be simple. See…it wasn’t easy for me to find the prompts, you know, partly from [my] glasses, but also I’m impatient and I quit looking. And I said ‘Oh I’ve spent all this time entering the stuff but if I put CANCEL, does that mean it’s gone?Male, Group 5Similarly, the first screen a user saw with PocketPharmacist contained both a menu and submenu. Participants expressed feeling overwhelmed by the amount of information being presented all at once. Many first time users struggled with basic touchscreen features, such as accessing and using the keyboard and employing application-specific gestures. One participant felt this should be standardized,So ultimately, you’d want a universal language, and a universal kind of, you know, this is where the back button is, this is where the forward button is. But if that doesn’t happen, then every app has its own unique way.Female, Group 2One of the challenges faced by participants was that the adherence features we examined  made assumptions about the end user. For example, the reminder strategies  assumed users were “attached” to mobile devices. Participants said, for example,Like, [young people] live with their cell, live with their Blackberry, and that becomes, you know what I mean. Like, I could see, even obviously, when those kids get to be 50, they will still be attached to the hip with those Blackberries.Female, Group 1Comparing the use of the applications on the tablet to the smartphone, one participant noted,Reminders would probably be the best [feature] but it would be inconvenient unless I had one of the other devices that you could carry in your pocket or your shirt pocket or a woman could carry in her purse. .This is an important distinction because though the tablets are less portable, they are more accessible to individuals with age-related vision loss. In one case, a participant with severe low vision noted that touchscreen devices were surprisingly accessible,I was always afraid to even look at them or try them, because I just thought that I wouldn’t be able to see, so why even bother. But I was surprised…yeah.Female, Group 4, low visionThe participants, as older adults, also described how they power off devices between use to conserve battery power or save it for emergencies.These tablet things, they’re not plugged in, so, most of the time…you tend to turn them off to conserve the battery and maybe they could be designed so that they automatically turn themselves on, give a signal, and then go back to rest.Male, Group 8The reminder strategies also assumed users were physically able to hear alerts. In every session, we observed at least 1 participant, often male, who could not hear the alarms going off in the room.But some people may be hearing impaired and you know, maybe that could be accommodated, I’m not sure how. .Finally, participants worried that adherence strategies that required users to maintain a medication administration record were easily fallible.With your daily plastic, you can see that you took it, with this thing, you may have put in that you took two pills…but you got distracted, so how do you know you took two?…It’s not physical, you can’t see it, as you get older. I mean, there’s two sides. You forget to take things or you know, things like that, I don’t know, it’s too easy to screw up.Female, Group 6We asked 35 adults aged 50 and over to spend 2 hours trying popular medication management applications on mobile devices. Most were new and novice users and we found that the currently available applications were not designed with older users in mind. Without simple and targeted design, the current applications are unlikely to be considered useful, usable, or accessible for a large proportion of individuals who need to take chronic medications.Dr Everett Koop famously said, “Drugs don’t work in patients who don’t take them”. Similarly, mobile medication management applications will not work for users who do not use them. Reminders offered the clearest example of the gap between design and the older end user. One-half of prescriptions are not taken as directed ,6, and dThe Technology Acceptance Model, which was introduced by Davis in 1986 , suggestIn a study of the factors that impact patient acceptance of online self-management technology, Or et al  concludeWe were able to speak with 3 of the 5 creators of the medication management applications tested. None had conducted usability testing in adults 50 and older. A tendency to target designs toward younger adults perpetuates the notion of the “digital divide” or “digital disengagement”, where developers assume older adults lack the technological access and literacy of younger generation . Loos 4 has cautWhile we need to recognize that many older users are technologically savvy, Hawthorn  notes thMed Box. Use this area to enter the names and doses of your medications”). Although some applications did provide examples, they were often small, low contrast, and easily misinterpreted as pre-entered information. The Nielsen Norman Group refers to the inability of identifying a touchable area as low discoverability [To improve usability, it would be helpful for developers to provide clear instructions and describe important buttons or features for first time users (eg, “The area where you list all the medications you take is called the rability . To imprOften, mHealth applications do not follow guidelines or include features considered essential for prevention and public health ,47. The A strength of our study is that it reflects the experiences of first-time users. The rationale was that, in the real-world setting, many new users would try several medication management applications before choosing one to use or would be prescribed a mobile application that they had not used previously. However, the 2-hour format meant some features of each application could not be explored. In some cases, participants assumed certain features were missing. In each case, we demonstrated the feature in question to gather any additional feedback. During the discussions, application names and features were often confused with one another so we tried to ensure we regularly revisited each application to clarify which application was being discussed.In future studies, participants should be given a device for a longer time period to examine the effects of daily use. Previous studies have found SUS scores to increase with the degree of experience with a program prior to usability testing . LongituThough older adults make up the majority of medication users, mobile medication management applications are often designed for younger populations. The result is that age-related physical changes, such as hearing or vision loss, and the use of nonintuitive design features limit the usefulness of the applications for older users. When developing mobile interventions to improve medication use and adherence, designers, programmers, and developers need to consider older adults as potential high-impact end users and include this population in the design process. For older adults, standard features such as reminders may be poorly suited to most mobile devices, whereas applications that provide high quality information on side effects or drug interactions may be more desirable. Considering that the industry is not currently regulated, the focus also needs to be on building applications that limit the risk of errors and omissions.


This particular SAD corresponds to the case when the number of unrecorded species in the sample tends to decrease exponentially with increasing sampling size. It turns out that the shape of this “ideal” SAD often conforms approximately to the usually recorded types in nature, such as “log-normal” or “broken-stick.”. Accordingly, this may explain why Chao's formulation is generally recognized as a particularly satisfying nonparametric estimator. Anne Chao proposed a very popular, nonparametric estimator of the species richness of a community, on the basis of a limited size sampling of this community. This expression was originally derived on a statistical basis as a lower-bound estimate of the number of missing species in the sample and provides accordingly a minimal threshold for the estimation of the total species richness of the community. Hereafter, we propose an alternative, algebraic derivation of Chao's estimator, demonstrating thereby that Chao's formulation may also provide  Estimating the total species richness within large communities of species, using only samplings limited in sizes, is a common, long standing challenge which has elicited numerous procedures of estimations.For a few decades, a series of so-called nonparametricestimators provide elegant and convenient solutions to this question. These new estimators are ordinarily simple format formulations which, moreover, require no specific assumption regarding the statistical distribution of the abundances of species .Within this category of formulations, Anne Chao proposed a very popular nonparametric expression, which actually stands among the most commonly used estimators of the total species richness of a sampled community.f1 and f2 as the numbers of species encountered only once and twice, respectively, in the sample.Let Δ be the number of species that are missed by  the limited sampling of a large community of species. Then, according to Chao's formulation ,(1)Δ=f1fx standing for the number of species recorded x times in the sample.This expression was later generalised  as(2)Δ=These expressions, derived on a statistical basis, provide a lower-bound estimate of the number Δ of missing species in a sampled community , 2. ThatHereafter, we address this apparent paradox and propose new insights and argumentations, issued from an alternative, algebraic derivation of the originally statistically derived formulation by Chao.f1x/(x!fx)]x−1)) species when the sample size N increases. In fact, as demonstrated in (i)“S” as the species richness, that is, the total (unknown) number of species of the community,(ii)k” as a constant,“(iii)N” as the sample size, that is, the number of individuals recorded in the sample.“Yet, while the statistical derivation of Chao's formulation requires no particular restriction (“nonparametric”), the algebraic derivation, implies a particular shape for the expected decrease of the proportion Δ/In turn, this particular form of the decrease of the number of missing species with enlarging sample sizes constrains the shape of the species abundance distribution .r” of recorded species in the sample isk′ be the number of individuals belonging to the less abundant species among the “r” species recorded in the sample . Then, the relative abundance ar of the species of rank r is expected to be inversely proportional to N, asN, every species may be called to play the role of the less abundant species within the sample .According to , the numample isr=S−Δ=S1−to N, asar=k′N.Ai” in the SAD:ai = f(i)) when the proportion of unrecorded species in a sample is exponentially decreasing with the sample size, that is, the shape of the species abundance distribution which conditions the validity of the algebraic derivation of Chao estimator. Therefore,  stands tS, instead of being only a lower-bound estimate, as is the case in the nonparametric context. As mentioned above, the algebraic derivation of Chao's formulation requires that the sampled community satisfies, at least approximately, the particular type of SAD defined by N  seems l (b)among the different types of accumulation curves with such an asymptotic evolution, the negative exponential answer of Δ to increasing sampling size is, admittedly, one among the most simple, robust, and seemingly common , 9; (c)i = f(ai) for the SAD corresponding to a broken-stick distribution is i ≈ S · exp(−S · ai) [the sigmoidal shape of the prescribed SAD  , which iYet, this condition assigned to the shape of the distribution of species abundances might not be so restrictive in practice, at least as a first approximation. Reasons for this may be as follows:Accordingly, it is no real surprise that Chao's formula often approaches a strictly centred estimate, in spite of being only a lower-bound estimate in all generality.This would explain why, in ecological practice in particular, Chao's formulation is yet considered one of the more accurate and reliable estimators of the total species richness within partially sampled communities.As mentioned in particular by Gotelli , a trend would remain for Chao's estimates to increase somehow when a series of sampling of growing sizes are extracted from the same community instead of remaining ideally stable on average. This, however, is not necessarily contradictory to preceding arguments but should certainly result from residual discrepancy between the real SAD and the ideal model described by  and exem