Typically discretisation procedures are implemented as a part of initial pre-processing of data, before knowledge mining is employed. It means that conclusions and observations are based on reduced data, as usually by discretisation some information is discarded. The paper presents a different approach, with taking advantage of discretisation executed after data mining. In the described study firstly decision rules were induced from real-valued features. Secondly, data sets were discretised. Using categories found for attributes, in the third step conditions included in inferred rules were translated into discrete domain. The properties and performance of rule classifiers were tested in the domain of stylometric analysis of texts, where writing styles were defined through quantitative attributes of continuous nature. The performed experiments show that the proposed processing leads to sets of rules with significantly reduced sizes while maintaining quality of predictions, and allows to test many data discretisation methods at the acceptable computational costs. Plenty of observed phenomena, objects, and problems are expressed through features that are real-valued. Such descriptions provide very detailed definitions of studied concepts. The available attributes and their values constitute a source of knowledge that can be used to construct a more general data model, which allows for pattern recognition and classification of unknown examples , 2.Continuous nature of variables is not necessarily an advantage . FirstlyTherefore, typically a tough decision needs to be made, either to accept continuous values of attributes with all consequences, or, to implement some of discretisation algorithms . They atIn an ideal scenario, at the stage of data mining we would appreciate access to all available information, which means operating on real-valued features. Once all knowledge about described concepts is discovered, we would like it to be represented in such form that discards all unimportant, unnecessary details, and keeps broader general categories, which can be obtained through discretisation. To achieve these two aims the paper proposes a new approach, which can be applied to inducers capable of working with both continuous and nominal attributes, and allowing for easy access to structures representing learned knowledge, such as rule classifiers. Decision rules store information about patterns detected in data by listing conditions for features that lead to class labels. This transparent form enhances understanding, and is one of the reasons why rule classification systems are often preferred as inducers .Studied decision rules were induced within Dominance-Based Rough Set Approach (DRSA) , which iIn the proposed methodology firstly decision rules were inferred from input continuous data. Secondly, data was discretised by various approaches. Applying definitions of categories obtained for all features, in the third step conditions in previously induced rules were discretised, translating decision algorithms from real into discrete space. And finally, the characteristics of discretised systems of rules, such as coverage and reduction of storage requirements, were studied, while performance was evaluated by application of rule classifiers to independently discretised test sets , 11. WitIn this research framework, sets of decision rules were also induced from discrete data. Their power was compared to discretised algorithms, and the original versions operating in continuous domain. The results from the experiments show that the changed scenario of data processing is worth of considerations, both with respect to performance of constructed classification systems and size reduction of their structures. The obtained results allowed also for observations on interesting differences in trends visible between applied discretisation methods.The patterns, detected and classified by generated decision algorithms, reflected characteristic properties of writing styles. These stylometric features provided descriptions for linguistic preferences of authors in a numerical form . It enabThe paper is organised in the following manner. Section Background and related works explains motivation leading to research and presents theoretical background, with descriptions of rough set processing applied to data, specifics of characteristic features in stylometric domain, and discretisation approaches. Section Setting up the experiment provides the details of research framework, while Section Observations on research results includes specific comments to the obtained results. Section Concluding remarks contains the summary and conclusions, and indicates directions for future research.The section presents formulation of the problem and motivation for research, which is followed with the theoretical background. There are given brief descriptions for all involved areas, namely rough set processing as a way to mine data, rule induction algorithms, stylometric analysis of texts, and selected approaches to discretisation of input continuous data.The motivation for the new methodology proposed in the paper originated in the observation that in many application areas analysed and studied concepts are described with continuous characteristic features, which are discretised in order to facilitate representation of information and the process of data mining. As the notion of discretisation has been studied for years, there are many categories of algorithms to choose from, and various methods were employed in research works , 15.While some data mining methods require discrete data, there are also some techniques capable of reasoning based on real-valued attributes, for example Naive Bayes, Artificial Neural Networks , or deciThere were also conducted studies where inducers handled both types of data, but could perform better with discretised features –22. PerfIn , an inflDecision rule classifiers also belong with data mining approaches that allow to learn knowledge from continuous data. They can be based on a decision tree, or obtained directly from the training data using different rule induction algorithms. They are often preferred as inducers, because they offer also easily accessible and clear representation of discovered knowledge, which enhances understanding of learned patterns , 25.In , the AutAs there are many discretisation procedures, both parametric and non-parametric , the obsOne of possible attempts at finding a solution to this problem, is to perform discretisation by several procedures, with varying parameters, and execute data mining for all versions of discrete input data sets. Then, by evaluation of performance of obtained classification systems, we could choose the best variant. However, such methodology could be unfeasible, as it would require repeating the knowledge discovery process multiple times, and this step often is the most time consuming part of the entire data mining task, with the highest computational demands and costs.Following this line of reasoning, in the paper a new methodology was proposed. Its originality lies with the reversed order of processing steps, where knowledge discovery (induction of decision rules) precedes discretisation, and not the other way round, as in other traditional approaches. The novelty of this research framework comes down to discretisation of not just data but also the learned knowledge, captured in the directly accessible conditions on attributes, included in the premises of inferred rules.In the first step of processing, decision rules are induced from continuous input data sets. Secondly, all input sets are independently discretised with various methods. Employing definitions of intervals constructed for all features in discretisation of training sets, in the third step, the real-valued conditions in rules are replaced with their categorical representations. It results in obtaining sets of discrete decision rules with greatly decreased sizes.The proposed methodology allows to try many discretisation algorithms for data, with the acceptable overall computational costs. The step of rule induction, which typically is the most demanding, is executed only once, and only discretisation is performed several times. Even though it is more complex, as not only input data sets are transformed but constructed rule sets as well, still the process requires less time and effort than it would take in the traditional experimental setting. This increased complexity of discretisation can be considered as a disadvantage of the methodology. Also, the possibility that for some variant of discrete data better rules could be inferred cannot be entirely ruled out. Yet the guarantee of choosing the best discrete representation of data could only be found in exhaustive induction and comparison of rule sets from all possible versions of discrete data sets.Rough set processing is dedicated to cases of uncertain, imprecise, and incomplete knowledge , 9. In sdecision tables. A decision table consists of a finite set of objects of the universe U, a finite set of condition attributes A = {a1, …, an}, and a finite set of decision attributes D. In multi-criteria decision making condition attributes are called criteria, and it often happens that there is a single decision attribute D = {d}. It partitions the universe into a finite number of recognised classes Cl = {Clt}, with t = 1, …, m.The available information about the universe is stored in the form of equivalence classes of objects that cannot be discerned by the values of their attributes. With the indiscernibility principle, CRSA argues that when two objects x and y of the universe cannot be discerned, they should be classified in the same way—that is to the same class. In consequence CRSA is capable only of observing presence or absence of some property, which leads to nominal classification.CRSA works only for categorical attributes, as the universe of discourse is perceived through granules of knowledge corresponding to dominance relation replaces indiscernibility, and values in all input data sets are perceived as ordered. Let ≽q be a relation of weak preference defined for the set of objects with respect to a criterion q. Let I = {1, …, n} be a set of the indices of condition attributes and, without a loss of generality, i ∈ I. For all objects x, y ∈ U, x is at least as good as y with respect to attribute ai”, and it is denoted as x dominates y with respect to a set of attributes P ⊆ A, when the values of all considered attributes for x are at least as good as their correspondents of y, that is if for every attribute ai ∈ P, xDPy. The relation of P-dominance is reflexive and transitive. y is dominated by x when all attributes of y considered as criteria have values at most as good as those of x. Respectively we have a set of objects dominating x, and a set of objects dominated by x. These sets correspond to granules of knowledge recognised in DRSA:In DRSA P-dominating set—a set of objects dominating x:P-dominated set—a set of objects dominated by x:Pareto principle states that when an object x is as good as another object y , then x needs to be classified at least as good as y. On the other hand, if x is not better than y with respect to their attributes, x should be classified at most as good as y.The dominance or r, s ∈ {1, …, m}, such that r > s, the objects from Clr are preferred to the objects from Cls. Where no clear natural order in data exists, it needs to be defined or discovered [cost type when its lower value is preferred over higher, and for gain type higher values are preferred over lower ones. Decision classes are always considered as gain. For condition attributes of cost type lowest values point to more preferred classes, while gain type links higher values of criteria with more preferred classes.Decision classes are preference ordered , with increasing indices leading to increased preferences, that is for all scovered . An attrdominance cones, The upward or downward unions of classes are called P-lower approximation of P ⊆ A, is the set of objects that belong to P-upper approximation of ai and their values Calculation of approximations for dominance cones is the starting point for the process of induction of decision rules. These rules provide conditions on some attributes Forms  and 5)  5) are is in Eqs  and 6)))x∈Clt≤ (s in Eqs  and (6))DRSA processing does not require any prior discretisation of real-valued attributes, only definitions of preferences. It means that constructed decision algorithms can operate directly in continuous domain. When conditions included in rules refer to continuous values, they provide very close descriptions for recognised concepts. Yet such precision comes at a cost of reduced generality, possibly more complex calculations, and prolonged processing. Thus any chance at simplification or improvement gives motivation for research. It was of sufficient interest to observe how categorical values of attributes obtained through various discretisation approaches reflect on rule classifier performance to warrant experiments presented in the paper.Decision algorithms are often favoured as inducers. Due to their transparent structure, they directly indicate conditions that need to be satisfied to associate a sample with a class , 33. WheThere are many classification strategies based on decision rules . Taking Minimal cover approach follows the general heuristic strategy, which is used for many known machine learning algorithms e.g., AQ , CN2 3737. It isExhaustive rule induction algorithm constructs all minimal rules that can be generated for given examples . It alloAnalysis of texts leading to tasks of author characterisation (or profiling) and authorship attribution (or verification), is a prominent field of study, with probably the most influential early works due to Mosteller and Wallace . It attrFor content-specific markers, their definitions depend on a subject topic, as they refer to words of a certain meaning in a context (key words). Structural markers are used to describe specific formatting or organisation of text elements. In the case of digital media, they include font types, embedded links or pictures, emoticons, and for a hard copy also page layout, or handwritten or drawn elements can be considered. These two types, together with language-specific descriptors, are also referred to as application-specific features.Lexical features base on employed vocabulary with its richness, and statistical information on linguistic elements present in a text sample. They can specify averages, frequencies of occurrence , or distSyntactic descriptors capture the underlaying patterns in constructed structures of phrases and statements, as evidenced by complexity of sentences, application of passive voice, or included punctuation marks .writer print or author print. As with any other prints we leave behind, there can be more than one such set. A textual analysis with respect to writing styles relies on finding such characteristic features, which form an author-specific base of attributes. Styles are defined by quantitative descriptors referring to such linguistic elements that are used rather semi-consciously or subconsciously. They are hidden inside habitually preferred phrases, in favoured patterns of formulated sentences [A group of features that allow for a unique definition and recognition of an author based on their style, is referred to as a entences . They shentences . The latentences . In ordeMethods and techniques employed in authorship attribution studies often use some statistics-oriented computations, for example by constructing language models based on the probabilities of occurrence for the chosen sequences of characters, words, or phrases . AnotherReliability of style markers and author recognition depends also on the construction of input data sets, obtained samples, and their groups. Descriptors need to be calculated over text samples of comparable and sufficient size, neither too large nor too small . The forComparable sizes of text samples typically imply that larger works are divided into different numbers of smaller parts. In such examples that are based on various parts of the same longer text, similarities can be detected for obtained characteristics, closer than when compared to other samples, based on separate texts. This observation leads to an important conclusion. When authorship attribution is treated and executed as a classification task, in order to evaluate classifier performance we need to use evaluation and test sets, and not popularly employed cross-validation. In cross-validation, even with several folds, it is highly probable to obtain falsely higher classification accuracy. These overly optimistic results are explained by this close similarity of some groups of examples , and lacEvaluation and test sets need to use samples based on works which are separated from training. This independence of input sets gives higher reliability of obtained results. However, when discretisation approaches are employed to these sets, independently executed procedures (which is the simplest way) can result in additional problems with varying numbers of intervals defined for the same variables in different sets .Data pre-processing is one of the important steps preceding the execution of data mining . It consOne of data reduction mechanisms is discretisation. It transforms numerical real-valued attributes into discrete or nominal ones with a finite number of intervals , by mappDiscretisation process simplifies the data and removes possible noise from them. Thanks to this, the data is easier to understand, interpret and use. One of disadvantages of discretisation is that it causes some loss of information. Which is why discretisation process should always be executed with caution, and adjusted to the available data.There are many discretisation methods and approaches, and they can be divided using various criteria . Below pSupervised vs. unsupervised;If a discretiser takes into account class information for samples to find the proper intervals among the ranges of the attribute values, it is called supervised. It uses some heuristic measures, e.g., entropy, to determine the best out of candidate cut-points. An unsupervised discretiser does not consider class information and the number of constructed intervals is provided as an input parameter.Static vs. dynamic;A static discretiser is independent from the learning algorithm, and performs transformation of attributes before the learning task. Dynamic discretisers are the components of the learning algorithm, and they are based on the information exchange between the discretiser and learner units. Most of discretising methods are static, dynamic discretisers are considered to be a part of data mining approaches.Univariate vs. multivariate;A univariate discretiser works with a single attribute at a time, contrary to a multivariate one, which bases on the interactions among the attributes, and simultaneously considers values of all attributes to define the set of cut-points.Local vs. global;The work of local discretisers is limited to some distinctive parts of the attribute domain, for which they are defined separately. To obtain the initial set of cut-points, a global discretiser analyses the full ranges of values for the attributes.Top-down vs. bottom-up;Top-down discretisers initially assign one large interval to represent all known values of an attribute, and then this interval is partitioned into some number of smaller and smaller sub-intervals, until a certain stopping criterion is met. In a bottom-up approach, the processing starts with some number of intervals determined by the set of cut-points, and during execution these intervals are combined together, until a certain stopping criterion is achieved.In general, discretisation can be considered as a four-step process :sorting all values of a discretised attribute, in either descending or ascending order;establishing the cut-points ;splitting or merging intervals, according to an algorithm criterion.In a top-down approach, intervals are split, in a bottom-up approach intervals are merged. For splitting, all candidate cut-points are evaluated, the best one is chosen, and it splits the considered range of continuous values into two bins. Discretisation is continued with each interval until a stopping criterion is achieved. Similarly for merging, neighbouring bins are evaluated to find the best pair to merge in each iteration. Discretisation continues with the reduced number of intervals as long as a stopping condition remains unsatisfied.stopping the discretisation process, which depends on a specific criterion defined in the discretisation algorithm.k disjoint discrete intervals with equal width, where k is an input parameter defined by a user.The two most popular unsupervised discretisation algorithms are equal width binning and equal frequency binning . The forEqual frequency binning also finds the minimum and maximum values of the discretised attribute, sorts all values in an ascending order, and divides the resulting range into a number of intervals defined by a user, so each bin contains the same number of sorted values . For thiThe two methods are simple (which works to advantage), and sensitive with respect to a number of bins defined by a user (which could become a disadvantage). One of drawbacks is that in cases where the values of a continuous attribute are not distributed evenly, some information can be lost after the discretisation process.There is a modification of equal width binning algorithm that is based on leave-one-out estimation of entropy. The obtained numbers of bins and cut-points allow to construct the discrete data set, which better reflects the nature of the input data. This optimised version of the algorithm —the number of instances of the attribute a in each bin for given b.w is the width of the bin for the given attribute a, and k is the number of bins.Entropy for all attributes is estimated by the following calculation:a ∈ A cut-points for bopt(a) are calculated.∀An alternative version exists also for equal frequency binning algorithm. Instead of requiring the number of bins, the weight of instances per bin is set as the input parameter. When the instances are not weighted, this processing delivers such resulting number of bins that contain the assumed number of instances.Due to the definition of constructed intervals provided by the user, the described unsupervised discretisation mechanisms in their standard versions do not cause significant problems when several separate sets of samples need to be discretised. To simplify transformations, these sets can be processed independently. It results in constructing independent discrete data models, which are then compared within the classification process. With this independent processing, the ranges of the intervals, established for the same attributes in different sets, can vary to some extent. Yet the numbers of these intervals will be as required.Two most popular methods from supervised category were proposed respectively by Fayyad and Irani , and KonS contain N instances and t decision classes Cl1, …, Clt. Class entropy H(S) of S is defined as follows:P is the proportion of class Cli instances in S.Let set a, the optimal selection of cut-point Topt is made by testing all possible candidate cut-points T. A cut-point T splits set S into two subsets, S1 and S2, where S1 ⊆ S contains the instances with the attribute values ≤T, and S2 = S\S1. Then entropy for the cut-point T is calculated as:Taking into account binary discretisation of a continuous attribute Topt class information entropy H is minimal.For the optimal cut-point The process of finding cut-points is top-down: it starts with considering one interval including all occurring values of a discretised attribute. Then its partitioning is repeated in a recursive way, until a stopping criterion is met. It is possible that discretisation results in all values of some attribute being grouped into a single interval. It happens when the calculated entropy indicates that this particular attribute does not contribute to discrimination of classes within the confines of the particular set of instances. For the same set for another variable several intervals can be required. Following this line of reasoning, supervised discretisation can be considered a process of knowledge discovery. Information about the numbers of bins constructed for each variable can be treated as a new source of available knowledge, to be mined and used for other purposes , 59.Contrary to unsupervised discretisation methods, for supervised discretisation there can be some serious consequences of independent processing for the input data sets. Not only the cut-points can be different, but, which is far more problematic, their numbers and the resulting from them numbers of the constructed intervals can greatly vary, depending on the local context of each transformed set . It can T, exceeds the value based on MDL principle and defined in t1 and t2 denote respectively the numbers of classes represented in the subintervals S1 and S2.In the case of Fayyad and Irani method, the stopping criterion for the top-down process of evaluating cut-points is connected with information gain—the difference in entropy without and after splitting the interval. Discretisation is applied as long as information gain, resulting from accepting the cut-point In the case of Kononenko method, the process of discretisation is applied recursively until the following inequality  is satiswhereCli,x-th value of the given attribute a,Cli with y-th value of the given attribute a,NT—the number of candidate cut-points.Executed experiments included preparation of the input data sets, induction of decision rules in continuous domain, discretisation of data sets and rule sets, induction of decision rules in discrete domain, evaluation of performance for all constructed classifiers, and analysis of all experimental results. The procedure is illustrated in a diagram given in The proposed methodology consisted of the steps as follows:initial data processingdefining the task of authorship attribution by choosing writers for comparison,selecting specific works for textual analysis,constructing text samples,choosing stylometric features and obtaining their values for all samples;rough data mining with DRSA approach for continuous datainduction of decision rules with exhaustive and minimal cover algorithms,re-classification of training samples for the purpose of establishing hard constraints on rule supports,evaluation of performance for test sets;discretisation through various methods forall input data sets,conditions in the previously inferred decision rules;rough data mining for discrete datainduction of decision rule sets with minimal cover algorithm,re-classification of training samples with discretised and discrete rule sets to obtain hard constraints on rule supports,evaluation of performance for test sets;analysis of resultsproperties of discretised and discrete rule sets,observations on trends in performance for classifiers,conclusions from comparisons of obtained results.All steps are described in detail in the following subsections, and discussion of the experimental results is provided in the next section.To achieve a fair comparison of writing styles, considered authors need to come from some similar time period. Otherwise, the linguistic differences resulting from the constant evolution of any used language could be far too striking. Furthermore, to obtain reliable characteristics, for every author many texts are required, each of sufficient length. It is also best not to compare writers of the opposite gender. Their styles show such discriminating properties that enable to make certain observations on the linguistic preferences of male and female authors, which would cloud the objectives of experiments.Taking all these factors into account, two data sets were constructed, one comparing a pair of male writers, namely Jack London and James Curwood, and the second for a pair of female writers, Mary Johnston and Edith Wharton. The chosen writers are known for several sufficiently long novels, which were divided into groups to provide a base for training, and evaluation and test sets. The selection of studied texts is given in All analysed works were partitioned into much smaller text samples, with a comparable size of around 2 thousand words. For the training sets 25 text chunks were chosen per each of four novels per author, and for two test sets 15 samples per each of three novels per writer. It resulted in constructing the training sets including 200 samples, and two test sets with 90 samples each. For all text samples some statistics were calculated. They reflected the frequencies of occurrence for a group of common function words and punctuation marks, which have proven to be good style discriminators , 42.Function words were selected in the following manner. Referring to the list of the most frequently used words in the English language, firstly for the most common one hundred entries the frequencies were calculated for text chunks designated as a base for the training sets. To these variables the frequencies of regular punctuation marks were added. Then several feature ranking algorithms were employed to the data. For further processing only these attributes were chosen that were never considered as irrelevant. In other words, the set of the selected characteristic features consisted of variables included in the intersection of all rankings. The entire procedure is illustrated in the diagram shown in This feature selection process resulted in the set of 24 attributes, including 22 lexical in nature , and 2 syntactic . The minimum value of any attribute equalled zero (when this attribute was absent in a text sample). The maximum in theory would be equal one, but only in the case when a text sample would contain just this feature , which in practice is next to impossible. So the obtained values of the attributes where always in the range <0, 1). Thus constructed input data sets were examples of binary classification with balanced classes, with continuous condition attributes and nominal class labels. From the point of recognition, both classes were considered to be of the same importance.Dominance-Based Rough Set Approach requires definitions of preference orders for values of all available attributes. In the case of continuous-valued features, recognised in the considered task, the ordering is natural, but preferences unclear. Also, as class labels are nominal and no author is preferred to any other, there is no visible ordering of values for the decision attribute. Defining preferences for criteria means creating associations between the calculated values of condition attributes and certain authors, reflecting characteristics of their writing styles. This information is not necessarily immediately accessible.When preference orders cannot be directly implied from the available domain knowledge, they can be discovered through some auxiliary transformations , but sucFor both data sets the process of generation for one MinC algorithm took more time, and the rule set included more, but weaker rules, while the other showed much better characteristics in all these considered elements. These preference orders were selected that led to these better decision algorithms, gain type in the case of male writer data set, and cost type for female writer data set.With the preference orders assigned to all criteria for male and female writers, all rules on examples were then induced in the exhaustive search . As expected, the process of rule induction took a significant amount of time, in fact it took several days for each rule set, as thousands of decision rules were inferred, each including real-valued conditions on some attributes.Next, both types of rule classifiers (MinC and Exh) were applied to the data. In the case of conflicts, a simple voting strategy was employed, giving each rule a single vote. As a measure of performance classification accuracy was used, understood as a ratio of correctly classified examples to the total number of examples, presented as percentage. Classification accuracy was a correct choice for a score of performance evaluation in the presented case since in all tasks classification was binary and classes balanced, and both classes were considered to be of the same importance. It means that the cost of false positives always equalled the cost of false negatives .Firstly, constructed inducers were used for re-classification of learning samples. This step enabled to impose some additional hard constraints on decision rules. In each case such set was chosen that included rules with supports at least equal the threshold that was the highest of these that guaranteed the maximum classification accuracy for the learning samples. This approach was used for all further processing, for all executed experiments.The constrained decision algorithms were evaluated by classifying samples from two test sets in the next step, and the results were selected as the reference points for comparison in further analysis. From all obtained results average values were calculated, and these are given in The low performance of minimal cover decision algorithms partially resulted from imperfect coverage for test sets . While minimal cover is generated, obviously it relates to learning samples and patterns discovered among them, which can be expressed and classified by some small number of rules. Yet in continuous domain conditions included in rules work against the probability of detecting the same detailed patterns in test data, which was confirmed by obtained results. With much higher numbers of rules provided by exhaustive algorithms also stronger rules were found, giving satisfactory predictions and coverage at 100%.In the presented research works to the training input data sets eight discretisation approaches were applied. They included:* in a standard approach (denoted as duwb),* with additional optimisation of obtained bins which led to their minimisation (denoted as duwo),equal width binning—with varying the input parameter corresponding to the number of constructed bins: from 2 to 9 with a step of 1, from 10 to 90 with a step of 10, from 100 to 900 with a step of 100, from 1000 to 9000 with the step of 1000, and 10000 (36 variants)* in a standard approach (denoted as duf)—with varying the input parameter that corresponded to the number of required bins from 2 to 200 with the step of 1 (199 variants)* in an approach with weighting bins (denoted as dufw)—with varying the input parameter, giving the required number of occurrences included in bins, from 1 to 200 with the step of 1 (200 variants)equal frequency binningunsupervisedFayyad and Irani* in a standard approach (denoted as dsF),* with additional optimisation of obtained bins which led to their minimisation (denoted as dsFo),* in a standard approach (denoted as dsK),* with additional optimisation of obtained bins which led to their minimisation (denoted as dsKo).Kononenkosupervised (a single variant for each set)Discretisation of tests sets was executed in two ways: independently on the learning samples, and by referring to ranges defined for the learning instances. Since the numbers of samples included in the test sets were different than in the training sets, in independent processing not all previously listed variants of input parameters were valid for them. For equal width binning the same parameters were used, and for non-parametric supervised methods the approach was the same.In the case of equal frequency binning in the standard version the numbers of bins were varied from 2 to 90 with the step of 1 (89 variants), and from 1 to 90 with the step of 1 (90 variants) for the version with requirements on numbers of occurrences in bins. Then, in the executed tests such variants of training and test input sets were matched that were characterised by the same numbers of bins defined for the corresponding attributes. If the exact match was impossible to find, the closest variants were selected from the available alternatives.For simplicity of representation and later processing, instead of using the intervals defined as the ranges specified by the established cut-points, all intervals were simply enumerated. Such positive integer numbers were next stored and used as attributes’ values, which allowed for a significant reduction of size in discretised rule sets.Sets of inferred decision rules allow for a direct access to knowledge discovered in mining of data, given as conditions on attributes. Thus, the decision algorithms, induced previously from the continuous data, were discretised for each obtained variant of the discretised training sets. Discretisation was performed in the following manner.For all attributes included in the premise part of a rule, a condition specified was matched with a certain interval to which the continuous value was assigned. Then the value in the condition was replaced with the selected interval, using its representation by an integer number.As the result of such processing groups were constructed that consisted of the following elements: the discrete training sets, the decision algorithms with the conditions discretised using intervals defined for the training sets, and the test sets with as close matching of intervals as it was possible to find. The number of such groups was equal to the number of variants of the discrete learning data sets.Each decision algorithm, translated from the continuous into discrete domain as described above, was then employed for re-classification of the corresponding discretised learning set, and two test sets. Firstly, there was found the highest value of the minimal support required of rules that led to the maximum classification accuracy of the training samples. With this hard constraint on rules, the results for test sets were obtained.For both male and female data sets, for each matching set of elements, two discrete decision algorithms  and two test sets, minimum, maximum, and average classification accuracies were found. There was also noted the coverage averaged for test sets, and obtained reduction of the rule set size, compared with respect to this size in continuous domain. These characteristics were studied separately for both types of decision algorithms, i.e. MinC and Exh. The experimental results are commented in the next section of the paper.Once the input data sets were discretised, it became possible to apply to them such data mining techniques that require discrete attributes, in particular decision rule induction methods. For the sake of comparison, once again rough set approach was considered. With the availability of categorical features DRSA could be replaced with CRSA, however, for both ways of processing inferring rules by exhaustive search was unfeasible due to the high number of prepared discrete variants of the training sets. In discrete domain rule induction is significantly simplified, yet it can still take several hours, as long as a day, and there were required multitudes of such processing time, hundreds. Thus the only type of algorithm, generation of which could be executed with some reasonable time constraints, was minimal cover.Rough Set Exploration System (RSES) , dedicatThe experimental results presented can be divided into three parts. The first was dedicated to performance of the discretised decision algorithms, for all considered variants of the discrete data sets. Unsupervised discretisation methods led to series of tests with varying the numbers of intervals defined for all attributes, while for supervised methods there were single tests. For all of them the observations are given for both types of decision algorithms generated , as different trends in performance were detected.In the second part of given results some properties of the discretised rule sets were studied. The third group of results consisted of scores, obtained in evaluation of performance for minimal cover decision algorithms induced from the discrete input data sets.Discretisation by construction of intervals with equal width is often criticised, as disregarding important data characteristics such as distribution of occurring values. When only minimum and maximum values are detected, and so defined range divided equally, in return some intervals can be established for which there were absolutely no occurrences of values in the considered sets. Nevertheless, since the number of bins is the input parameter, the procedures enforce the same numbers of intervals when applied to separate sets, which makes operations on them much simpler.The classification results for minimal cover (MinC) and all rules on examples (Exh) decision algorithms are displayed in For optimised equal width binning (duwo), the results were noticeably worse. For the relatively small numbers of bins the performance was to some extent acceptable, but for the higher numbers it was degraded too much. This was partially caused by the locality of the executed optimisation—since all sets were discretised independently, each was optimised independently as well. In turn, it caused much greater differences between the intervals defined for the training and test sets. And these differences resulted in the increased numbers of incorrect classifications of examples.Due to the relatively poor performance of the minimal cover algorithms in continuous domain, caused by lower coverage, in discrete domain their observed power was increased for more variants of the discretised sets. The overall performance showed trends similar to those of exhaustive algorithms, i.e. satisfactory results for lower numbers of bins, and degraded for higher.When the frequencies of occurrence of values are taken into account, such parts of the input continuous space, which are characterised by the presence of many points, are divided into many small and closely packed intervals. Other regions, with sparsely occurring values, have much fewer, yet larger bins. Thus this kind of processing more closely follows the distributions of input data points, and still, at least for some range, returns the same numbers of bins for independently discretised sets. Unequal numbers of constructed intervals can be caused by different cardinalities of sets with samples. Typically, the training sets include more examples than the evaluation and test sets. In such cases any attempts at enforcing the same numbers of bins are doomed to failure.A modification of the standard equal frequency binning assigns some weights to intervals, where it is possible to define preferences for some regions of the feature space. When all bins  are treated in the same way, weighting them can be seen as a direct definition of the number of occurrences required for each interval. Hence in the standard version of discretisation algorithm (duf) a user specifies the numbers of bins, and the procedure ensures that in all intervals the same numbers of occurrences are included. For equal frequency binning with weights (dufw), the user defines the numbers of occurrences. Using this input parameter the target numbers of constructed intervals are established.For both MinC and Exh versions of the decision algorithm the classification results are displayed in For the exhaustive decision algorithms discretised by the standard equal frequency binning, almost in the whole presented range the classification accuracy was satisfactory, in several cases exceeding the reference levels of real-valued algorithms. The modification with interval weighting brought the worsening of the average performance, even though some of the obtained maxima were greater than the ones detected for the standard version of discretisation algorithm.For minimal cover decision algorithms, the charts showed seemingly opposite trends in performance for the two versions of the discretisation method. This came from the fact that the trend in classification accuracy was related to the numbers of intervals, which in the standard version increased with the input parameter, and decreased for the weighted version. In both cases the highest correct prediction ratio could be found for the relatively low numbers of bins established.Supervised discretisation is commonly praised for preserving the discriminating properties of values, occurring in the input sets, so intuitively we would expect better results from its application, than for the unsupervised approaches. In the tested cases, however, these supervised methods brought rather disappointing results, in particular for the exhaustive decision algorithms. For both types of algorithms, for Fayaad and Irani, and for Konnonenko method, the classification accuracies are shown in For the second version of Fayaad and Irani method, with optimised encoding, for both types of decision algorithms the observed performance was the same for male writers, and for female it was worse. For Kononenko approach, for all rules on examples for both versions of the discretisation method the results were the same, and for minimal cover some slight improvement could be detected for female writer data set.These counterintuitive lower predictive properties of the discretised decision algorithms, in particular low minima, could be explained to some extent by a close adjustment of the constructed data models to the local context, that is to the particular sets. As they were discretised independently, all calculations and stopping criteria were based on the values occurring in each set. As not only the values but also the numbers of samples included in the sets were different, discretisation resulted in different definitions of intervals, and even in the varying numbers of those intervals for the same attributes in different sets. There were even cases that in one set for all values of some attribute the single interval was assigned, whereas in another set for the same attribute several bins were established.To ensure that in all data sets the transformations of the same attribute were uniform, another attitude to discretisation of the evaluation and test sets could be employed—they could be discretised using the data models obtained for the training samples. Then the values present in the evaluation and test data sets were seen through the perspective of those values which occur in the learning set. The results from such processing are shown in The decision algorithms used in those experiments were the same as the ones obtained before, but they were applied to the modified test sets. It could easily be observed that this modified processing of some sets brought the expected improvement when compared to the previously shown results for supervised discretisation methods. In particular the minimum classification accuracies were significantly higher, which gave also much improved averaged performance for both types of decision algorithms.As in the presented case the recognised classes were considered to be of the same importance, the cost of false positive was considered to be equal to false negative. Therefore, the performance of the tested decision algorithms could be evaluated by specification of the number of correctly labelled samples in relation to the total number of samples, which results in the global classification accuracy, commented before in Sec. Unsupervised discretisation and Supervised discretisation. However, a study of coverage encountered for the test sets gave yet another insight, and enhanced understanding of the detected patterns, which were discretised by transformations of decision algorithms.In the case of exhaustive algorithms, for all versions of discretised sets the coverage was always at 100%, and none of the test samples remained uncovered, so the returned classification accuracy resulted only from the correct and incorrect classifications, for all discretisation methods employed in the research.For MinC algorithms the situation was markedly different, and the coverage highly dependent on the applied discretisation procedures and their parameters. From the four unsupervised discretisation methods, for equal width binning with optimised encoding the coverage was next to perfect—only for 3 and 4 numbers of bins, respectively 7-9 for female and 7-10 for male writers, there were some uncovered samples in the test sets. For the other three methods there were many more such cases, thus the charts presenting coverage are given in It could be observed that for both versions of equal width binning and the standard equal frequency binning, for both data sets coverage was the best for the small numbers of intervals constructed, and then it gradually decreased while the numbers of intervals increased. Also, on average for equal width method for male writers the coverage was higher than for female writers, when the opposite could be stated for equal frequency binning. For equal frequency binning with weights, for both data sets at the beginning the numbers of intervals were high, hence low coverage, increased with the higher numbers of instances required per bins, which meant construction of fewer intervals.For supervised discretisation approaches only in one case coverage was below 100%: for the standard Kononenko method, for female writer data set the coverage for MinC decision algorithm, averaged over test sets, equalled 98.89%.In discretisation of the induced rule sets, continuous values of conditions, included in the rule premises, were replaced with the nominal representations of intervals constructed. In this manner the numerical values, which required several digits to be stored, were replaced with single characters. It caused a significant reduction of size for both types of decision algorithms, shown for all unsupervised discretisation methods in For minimal cover rule sets, as they were smaller, contained fewer rules and through that fewer conditions to be discretised, the size reduction was also smaller than in the case of exhaustive algorithms that included so many more rules. The results are given as the percentage presented by a size of a discretised algorithm with respect to the corresponding original size of decision algorithms, induced in continuous domain.As it turned out, for both supervised discretisation methods employed in the research, and both their versions, for MinC and Exh algorithms the obtained reduction of size was the same, and the two differences observed were between two types of decision algorithms, and the two studied data sets.In the proposed methodology discretisation follows data mining, whereas typically discretisation is considered to be a part of initial data pre-processing, after which data mining follows. To confront the two ways, new rules were induced from all versions of the discrete input data sets, by using minimal cover algorithm in DRSA approach.Due to the high number of the prepared variants of the discrete data sets , induction of rule sets in exhaustive search would be a task of unmanageable proportions, when taking into account the computational costs involved. It was one of the considered factors motivating the proposed research framework. Because of the employed heuristics, minimal cover algorithms were found relatively quickly (in a matter of seconds), thus even if the generation procedure was repeated many times, still the task was feasible.Once all rules were inferred, they were applied for re-classification of the training and test samples, with simple voting strategy employed in case of conflicts, as in the earlier batch of tests concerning discretised rule sets. For all unsupervised discretisation methods, the average classification accuracy obtained for the test sets is shown in the charts included in When these results were compared against MinC decision algorithms working in continuous domain, it could be observed that for female writer data set only for optimised equal width binning the performance was worse, while for other discretisation methods on average it was improved. For male writers also for supervised discretisation the results were degraded. On the other hand, in all cases the discretised decision algorithms achieved better predictive accuracy than the rule sets induced from discrete data.Nevertheless, it is not entirely out of the question that for some variant of the discrete input data set an algorithm induced in exhaustive search could outperform both types of decision algorithms with continuous values, and with the discretised conditions. How to find out which particular approach to discretisation would cause such advantageous conditions remains the open problem.For all tested variants of decision algorithms and input data sets The summary indicates that the widest ranges of power of rule classifiers, evidenced by the relatively high values of standard deviation, were detected for optimised equal width binning (duwo), and for equal frequency with weights (dufw). The lowest values of standard deviation were obtained for equal frequency binning, followed close by supervised discretisation. However, for the latter the accompanying predictive accuracies were mostly worse than for unsupervised methods.As far as the average classification accuracies were concerned, for female authors and exhaustive algorithms only equal width binning (duwb) gave a slight improvement, while for other discretisation approaches the results were worse than for continuous domain. For male writers and Exh algorithms, for all methods on average classification was degraded with respect to considerations of real-valued conditions. On the other hand, for minimal cover algorithms, since their performance for continuous data was relatively poor, it was much easier to find some improvement.Furthermore, it needs to be remembered that The results from experiments show the merit of the reversed attitude to discretisation of data. Instead of executing it as a part of initial pre-processing before data mining, we can mine data first. Then we discretise both data and constructed inducers, and still obtain satisfactory results. With such changed order, in the data mining phase we have access to all available information and do not risk any loss of it, which most often happens during discretisation. On the other hand, later translation from continuous into discrete domain helps to obtain more general and compact forms of constructed inducers, shortens processing time, while keeping or even improving predictions, and enables to observe results for many disretisation approaches at reasonable computational costs. These advantages of the proposed methodology should be weighted against some disadvantages, such as an increased complexity of discretisation, and a lack of guarantee that discretised rule sets perform better than some of decision algorithms which could be induced from discrete data.The paper presents the research on discretisation applied to the decision rules induced from the continuous data, thus changing the standard order of the processing steps. The proposed methodology relied on mining the complete available data, without any attempts at its initial simplification or reduction, which was then followed by such simplification of the learned models through their discretisation.The research framework enabled to test various discretisation approaches with acceptable costs of processing, as the most computationally demanding stage of data mining and knowledge discovery was executed just once, and only discretisation was performed repeatedly. One of the disadvantages of such methodology could be found in more complex discretisation, since not only data sets but also learned models were discretised. Also, no guarantee could be given that a better performing inducer could not be found for some discrete version of data sets. On the other hand, for the typical order of processing, with discretisation preceding data mining, such guarantee neither could be given.The real-valued input characteristic features, used in the research works, came from the application domain of stylometric analysis of texts. For the task of authorship attribution, writing styles were defined by some quantitative descriptors of lexical and syntactic type. They gave the frequencies of occurrence for the selected common function words and punctuation marks.From the prepared continuous learning data sets decision rules were inferred within Dominance-Based Rough Set Approach, for two types of algorithms, minimal cover and exhaustive. The induced rules contained real-valued conditions that meant a very close fit to the training data, and detailed definitions of the concepts. By itself, it could be considered an advantage, but it could cause poorer generalisation, lengthen the classification process, and increase the requirements for data storage.In the next stage of the experiments, all input data sets were discretised, using the selected unsupervised and supervised strategies, with varying their parameters. The previously constructed inducers were transformed as well, by substituting the continuous conditions on the attributes with their discrete forms. So translated rule classifiers were next applied for classification of samples in discretised sets.The results from the performed tests show that for unsupervised discretisation approaches on average the inducers outperformed those constructed for supervised methods. For exhaustive algorithms some improvements in classification accuracies were detected, but for minimal cover algorithms the positive differences in performance were much higher. However, it should be remembered that the results of rule induction and classification depend on data, distributions of features’ values, both condition attributes and decision classes.Some properties of the discretised rule sets were also studied. Coverage obtained for the test sets by the exhaustive algorithms was always perfect, but for minimal cover algorithms highly dependent on a discretisation strategy and its parameters. The sizes of the transformed rule sets were significantly reduced with respect to continuous domain, due to replacing the real-valued conditions with their categorical representations. For comparison sake, from discrete data sets minimal cover algorithms were also induced. Their performance, contrasted with decision algorithms with the real-valued and discretised conditions, turned out to be worse.In the described research works the proposed approach was dedicated to rule classifiers, in which the access to the learned knowledge is relatively straightforward. In the future research, the methodology will be tested for other types of inducers capable of working on continuous attributes while allowing for easy access to their structures. An another research path will be directed at discretisation executed with regard to not the whole data, but only conditions present in the inducers constructed in continuous input space. It would allow to use even more knowledge, discovered in the data mining phase, for the processes dedicated to building the discrete data models. Also, the proposed methodology will be tested in other application domains, for other data sets.S1 File(ZIP)Click here for additional data file.


Objective: In the course of the COVID-19 crisis it became necessary to convert the majority of classroom teaching to e-learning. This should be done in a uniform and transparent way for the study programs of the Medical University of Graz. Methodology: We built on the Virtual Medical Campus, which has existed since 2003. For the summer semester 2020, we focused on an expansion of the automatic lecture recording system, microlearning and the implementation of video conferences as well as the learning platform LT. Results: The number of lecture recordings increased from 170 to more than 700, weekly accesses reached more than 80,000, with nearly 4,200 students. In the Microlearning system, an average of 82,516+-12,071 SEM learning steps per week were completed, which represented a highly significant increase compared to the same period of the previous year . Video conferencing via WebEx was a newly introduced tool that was used extensively for interactive seminars, but also for oral exams. The LT platform from AdInstruments was successfully used as a replacement for practical training, especially in physiology. Conclusions: Based on sufficient preparatory work, a rapid expansion of e-learning ensured that teaching could be continued in the form of home learning despite the exceptional situation caused by COVID-19. Success factors were the provision of selected technical tools, consistent communication of the university management and technical and content support for teachers and students by a central staff unit. When there was an extensive lockdown in Austria in April 2020, we were in the positive position of not having to start virtualization from scratch. The development of e-learning was already initiated in 2002 with government subsidies , was takAt the time of the changeover to Home Learning, we had a Moodle-based comprehensive e-learning portal  with more than 13,000 learning objects. For the period of extensive virtualization, we subsequently relied on four components: Lecture recordings, microlearning and video conferencing and the use of the learning platform LT as a replacement for practical training. Thus, the presentation refers to the special conditions of Med Uni Graz with the relevant previous experience, which may be specifically different at other locations. In addition, a priori attention was paid to the quick and easy implementation, whereby the didactic requirements of the respective subject areas were taken into account when selecting the mentioned formats. https://www.epiphan.com/]. This is permanently installed and always records two streams: Lecturer and PC output. In the course of the corona crisis, all major lectures were cancelled and made available as video recordings instead. We deliberately refrained from live streaming for the major lectures because interaction plays only a limited role even in face-to-face classes and live streaming would result in a loss of time independence for the students. For video recording, the teachers came alone into the lecture halls, gave their lectures and were each technically supported by one person. Subsequently, a professional post-processing and the provision of the finished lectures took place both on the video portal VITAL [https://vital.medunigraz.at] of the Med University of Graz and linked in the eLearning platform Moodle. In view mode, students can choose between pure presentation view, pure lecturer view and the – usual – double view. While 170 lectures were available in the system until the corona lockdown, after a largely virtual summer semester 2020, the number of lectures is now 743. 4,000 lectures are accessed on average each week, reaching peak values of up to 8,556 with almost 4,200 students.In the course of the new construction of the preclinical institutes on the Med Campus, five lecture halls were equipped with a lecture recording system based on Epiphan hardware [Microlearning is learning in small steps, independent of time and place, preferably via mobile devices . Our uniFor several weeks it was not permitted to hold face-to-face meetings. In order to be able to guarantee interactive teaching nevertheless, the University licensed Webex   and made it available to all teachers and students. The staff unit Teaching with Media offered the teachers information material, detailed instructions and personal training as well as active practice units in order to become familiar with the system. The teachers used the tool for live streaming of their presentations and interactive discussion. In addition to holding seminars, this tool was also used for oral examinations.Since the lack of classroom teaching meant that no practical training could be held, LT (AdInstruments) was used as an online platform . EspeciaThe changes in the use of different e-learning formats before and during the corona crisis are shown semi-quantitatively in table 1 With the four central tools – lecture recordings, microlearning as interactive learning support, video conferencing for holding seminars and exams and LT as a replacement for practical training, Med Uni Graz has succeeded in bridging the lockdown caused by corona and continuing to offer students lessons without interruption. It was a great advantage that e-learning as a form of teaching/learning did not have to be taken up again, but was already well established on campus and only required consistent upscaling. The decisive factors for success are a concentration on a few tools, concise communication by the university management, close coordination of those involved, support of the teaching staff by a central e-learning unit and the commitment of the teaching staff. The digital skills and digital equipment of the students, which – in contrast to the “virtual semester” in the academic year 2005/2006 – are taken for granted today, posed no challenge. In the following, however, we will deal with the differentiated didactic evaluation of the various methods of knowledge transfer and the advantages and disadvantages from the perspective of students and teachers. For example, we are preparing a survey on acceptance among teachers and students as well as comparative studies of examination performance after the elearning phases compared to the attendance phases . We assume that the expansion of e-learning offerings as an accompanying teaching and learning tool will be maintained beyond the corona crisis and that it will be possible to react quickly and efficiently in the event of a new pandemic challenge. In particular, we believe that this will affect both lecture recordings and the use of video conferencing, although the latter was hardly used at all in our company before the crisis and has now found high acceptance. The same applies to microlearning, which was further developed during the lockdowns and will continue to be made available to students and expanded in the future.The authors declare that they have no competing interests.


Digital mental health interventions leverage digital communication technology to address the mental health needs of populations. Culturally adapting interventions can lead to a successful, scalable mental health intervention implementation, and cultural adaptation of digital mental health interventions is a critical component to implementing interventions at scale within contexts where mental health services are not well supported.The study aims to describe the cultural adaptation of a digital mental health intervention Step-by-Step in order to address depression among Chinese young adults.Cultural adaptation was carried out in four phases following Ecological Validity Model: (1) stage setting and expert consultation; (2) preliminary content adaptation; (3) iterative content adaptation with community members; (4) finalized adaptation with community feedback meetings. Cognitive interviewing was applied to probe for relevance, acceptability, comprehensibility, and completeness of illustrations and text. Six mental health experts and 34 Chinese young adults were recruited for key informant interviews and focus group discussions.relevance), changing leading characters from a physician to a peer and a cartoon (acceptability), incorporating two language versions  in the intervention (comprehensibility), and maintaining fundamental therapeutic components (completeness).We adapted the text and illustrations to fit the culture among Chinese young adults. Eight elements of the intervention were chosen as the targets of cultural adaptation . Samples of major adaptations included: adding scenarios related to university life (This study showed the utility of using Ecological Validity Model and a four-point procedure framework for cultural adaptation and achieved a culturally appropriate version of the Step-by-Step program for Chinese young adults. Digital mental health interventions leverage digital communication technology to address the mental health needs of populations. These interventions incorporate information provision, screening, assessment, and monitoring, intervention, and social support . Key advIn response to the global need for scalable mental health interventions, the World Health Organization developed Step-by-Step (SbS), a transdiagnostic and guided digital mental health intervention primarily targeting symptoms of common mental disorders, including depression –10. The Culturally adapting interventions is one of the keys to successful scalable mental health intervention implementation . CulturaSeveral models are known for cultural adaptation with distinguishing features , such asFrameworks for cultural adaptation are generally used to inform adaptation dimensions for intervention content and to inform the process of the adaptation . The curWe selected the EVM model developed by Bernal and colleagues  to inforPurposeful sampling was used to invite six Chinese mental health experts from Macao and mainland China for key informant interviews (KIIs). All of them earned advanced postgraduate degrees in mental health fields, including counseling, clinical psychology, and psychiatry and had extensive experience working with Chinese clients. We balanced the sampling to represent both Cantonese speaking experts and Mandarin speaking experts since our aim in the adaptation was to develop programs for these two distinct linguistic and cultural groups. All interviews lasted for 90 to 120 minutes and were audio recorded with consent from participants.Chinese young adults who were 18 years or older and lived in Macao for at least one year were eligible to participate in the focus groups (FGDs). Participants were recruited through purposive sampling in cooperation with the General Association of Chinese Students of Macao, the largest NGO that works for Chinese students in Macao. We composed four homogenous groups based on sex  and origin of birth (Macao Chinese or mainland Chinese) to encourage all participants to speak freely among peers. We conducted 18 FGDs, five each with Macao Chinese women and men, and four each with mainland Chinese women and men. The average number of participants in Macao male group, Macao female group, mainland male group, and mainland female group are 6 (6–7), 6 (5–8), 5 (4–6), and 7 (5–8). There were 41 participants in total, and each FGD lasted about 3 hours. All FGDs were recorded with written consent. The FGDs were open groups, with not all participants attending all sessions. In order to maintain a group size of about eight participants, new participants were recruited by snowball sampling throughout the study.Basic demographic information  was collected before KIIs and FGD, and in addition, data on depressive  and anxiThe study was conducted in four phases from May 2019 to December 2019  started the conversation with a welcome opening, the introduction of the program and the purpose of the interview, and oral informed consent was obtained. After starting the recording, the interviewer began with an open question about their experience with Chinese clients in general, followed by their professional perspectives on specific age groups and about the mental health context in Macao. After questions about major mental health problems, existing mental health services, and client’s typical coping strategies, we focused on the content of SbS with questions related to the appropriateness of the program, delivery method, characters, storyline, illustrations, and expressions. The notetaker (the other graduate student in the steering group) organized the notes after the interview.The content of the original version of SbS was written in English. In phase two, a team member with a minor in translation studies translated the English language into the Chinese language, and a different team member who was bilingual (English & Chinese) conducted backward translation to check the accuracy of the translation.We recruited a creative story writer with a background in Chinese education to change the storyline for Chinese students based on the feedback in Phase One from KIIs with the mental health experts. We also hired a local professional illustrator with a background in visual art & art education to design the main characters and the illustrations in session one for future discussion. A simplified Chinese version was adapted from the traditional Chinese version to ensure that both Macao Chinese and mainland Chinese could utilize the program materials.For phase three, the study team worked in conjunction with AECM, who was responsible for participant recruitment. We had a Mandarin working group with one facilitator and three note-takers who spoke Mandarin as their first language as well as a Cantonese working group with one facilitator and two note-takers who are Cantonese speakers. They were all trained in properly organizing focus groups and in avoiding leading questions or intimidating questions by the PI and co-PI. The note-takers also prepared PowerPoints with all materials in one session before the FGDs, which would be shown to the participants during the FGDs. The main purpose of the FGD was to adapt the storyline and to apply a bottom-up approach to generate the illustrations with participants, meaning that participants would provide ideas for the illustrations based on the texts and discussion, and then the illustrator would develop the illustrations based on ideas generated from the FGDs. Illustration adaptation and FGDs discussions were iterative and concluded when FGD participants were satisfied with the illustrations.All focus groups were conducted in a private meeting room with a projector at a service center of AECM. The working groups were matched with participants by language. During the FGD, the facilitator presented the program introduction, the purpose of the study, and basic rules for the discussion and started the discussion with participants’ short self-introduction as an ice-breaking activity. After that, facilitators focused on the materials using cognitive interviewing strategies . ParticiFor phase four, we had two more community meetings with the local students who participated in the FGDs when we presented the new illustrations and suggested changes from the results of FGDs. Feedback was collected and analyzed to inform the further changes. Adaptation decisions were finalized during the community feedback meeting. Finally, the adaptation results were documented according to the eight elements of the EVM .The notetaker of the KIIs took detailed notes and organized the notes with audio recordings during and after the KIIs. The notes were synthesized by the notetaker and the interviewer using excel. The main thematic codes were Chinese mental health, cultural considerations, potential target tailoring groups, and the SbS digital intervention. The eight elements of the EVM framework were used as sub-theme codes under SbS digital intervention to further synthesize the adaptation of materials of the intervention.Two working groups led by the graduate students in the steering groups took detailed notes during all FGDs. Suggestions for changes of text & storyline, the ideas for illustrations, and rationale for changes were recorded. After the FGDs, we organized steering group meetings to discuss the suggestions to ensure they were clear and consistent with the theorized treatment mechanism of symptom improvement. The results of the FGDs and steering group meetings were coded using excel through the lens of the Bernal’s EVM model . The resIRB approval was granted by the University of Macau.A total of 41 Chinese young adults participated in the FGD (26 Macao Chinese). Among local students , ten of them are male, and 23 were born in Macao. Among nonlocal students , seven of them of male. Descriptive statistics and means and standard deviations of depression, anxiety, and subjective well-being are shown in Eight dimensions within the EVM  were appSeveral issues related to comprehensibility and acceptability were identified in terms of language during FGDs. In terms of comprehensibility, several FGDs pointed out that the translations were redundant across sessions. This was improved by either combining similar expressions or skipping redundant content while maintaining the coherence of the storylines and therapeutically meaningful components. Examples of changes are shown in One major adaptation was made to increase the acceptability of the language. The use of encouragement and positive feedback such as “Well done!” were not seen as culturally consistent and were not acceptable. Participants reported that Chinese seldom express compliments verbally. Since these messages are important and designed to motivate users to continue the program, alternatives were needed. In order to enhance the acceptability of the use of encouragement and compliments, we generated illustrations that expressed encouragement, and modified messages to be acceptable if displayed with the illustration (Shijie). As a result, both male groups adopted a cartoon character, and both female groups adopted a young and experience female figure as the doctor figure in the original SbS to guide them through the program and to enhance the acceptability of SbS. Details of modification are shown in A large difference between preferences for the service provider character between KIIs and FGDs was noted. The characters first suggested by the experts in KII, were a kind mother character, a middle-aged female professional, and a cartoon  to show her professionalism. The adapted and finalized senior leading characters are shown in The cartoon figure was named “elf” in the local male version and “Xiao Q” in the nonlocal male version. The local female group picked the young female character . Generally, all groups agreed that the free-rider issue was common to college students, and they felt it was hard to talk about the issue with the team member while not hurting their relationship. Thus, we included the free-rider issue in the story and modified it from our original idea, which was a worry about grades and falling behind in studies. We also adjusted the narratives by modifying some of the elements, such as transportation, interaction with friends, and places (see The junior leading characters (the former service recipients) were created by following several criteria: matching the target population’s gender, age, and style of dress , which is matched with the aim of the designed program. Nevertheless, experts and laypersons suggested framing the program as a stress management tool or academic performance tool instead of mental health intervention in order to reduce the stigma of using a mental health intervention, which is a common treatment barrier among Chinese young adults . AccordiSeveral features were designed to achieve the goals of the program within the Chinese cultural context. First, the design of the digital program allowed potential service-users to use it anytime wherever they are to reduce stigma about using mental health services. Second, although some mentioned that positive feedback in the exercise was redundant, we kept the positive feedback for all activities or adapted to a shorten version to emphasize their strength , which related to the male core values and is also a motivation enhancing therapeutic strategy. Third, we adapted the planned list of activities for all groups to make the list more related to each stratum. For example, we added “go out, watch a movie” for nonlocal females, “go for a walk” for nonlocal males, “plan in some time to run/jog” for local females, and “go out with friend, go for a ride, watch Facebook video” for local males to the list of things one could do. The adapted list contained specific activities that are more appealing to one group and offered more suggested activities for potential scheduling.Considering context is important in the adaptation of this program to Chinese young adults because of the unique needs and experience at their developmental stage. With the partner NGO support, this program has the potential to be disseminated to most Chinese young adults and increase the accessibility of this intervention. During the FGDs, we were aware that they had concerns more than merely psychological and emotional health. They believed some of the worries were related to their academic and career development, but they had few resources for these concerns. Thus, we modified the list of local services to meet the needs of young adults, such as mental health care and career development to make the program fit the local context in this population.We applied the Ecological Validity Model  and follSeveral issues should be highlighted to enhance cultural adaptations. First, a proper framework and a rigorous process are essential to cultural adaptation. We employed the EVM  to inforSecond, a successful cultural adaptation required the contribution of both experts and laypersons , 12. FroThird, including layperson with proper characteristics in cultural adaptation can enhance the validity of the intervention. The current version of SbS was adapted for Chinese young adults with mood symptoms and other psychological difficulties, so incorporating participants who were Chinese young adults and with a certain level of symptoms would be beneficial to the cultural adaptation. The recruited participants in this study demonstrated minimal symptoms, since it is difficult to engage people who are actively depressed to engage in the rigorous and time-consuming cultural adaption process, so there may be a gap between what people with depression and nondepressed participants might prefer in the program and how they might perceive the text and illustrations . Given tFourth, community engagement is key to a successful cultural adaptation as well as intervention implementation . StudiesLast but not least, culturally adapted interventions need to be examined. Well adapted interventions enhance the treatment effect . Thus, aThe study has several limitations. First, the FGD participants reported minimal depressive symptoms and might perceive the program differently from those with higher levels of depression. However, we also noticed that a majority of participants from the two communities reported a low level of wellbeing, which indicates that while they did not mean cut-off scores for depression, they nevertheless reported distress. The next phase in our research process is to conduct a feasibility study with adolescents who report significant depressive symptoms. This study will provide the opportunity to evaluate how the intervention is perceived and ensure that the content is optimized for the target population.Second, the FGD participants were mainly from three major universities/colleges out of the ten inMacao (approximate 70% of Macau university/college students attend one of these schools) and no out-of-school young adults were included. The acceptability of the program remains unknown for students in other colleges (e.g. nursing college) and working young adults, who might have different life experiences from the FGD participants. This may affect the generalizability of the adapted materials. However, this Chinese tailored version may be applied to other Chinese young adults who study abroad and only minimal adjustment would be expected when needed, which might increase the coverage of the intervention.Third, the stratums of local and nonlocal groups might not fully capture the variability in the sociodemographic characteristics of young adults in Macao. We also attempted to divide the focus groups into Macao local Chinese and mainland Chinese groups, since we believe that there are cultural differences between these Chinese populations, which may influence the preferred adapted content. However, we noted that there were 3 participants not born in Macao joining Macao Chinese groups, and 1 participant born in Macao joining the mainland Chinese group. The 3 Macao Chinese lived in Macao for 7, 13, and 17 years and perceived themselves as a member of the local community. However, the participant from mainland Chinese group was actually a Macao resident but perceived himself as a member of the nonlocal community. He did not consider himself a member of the local Macao Chinese community because he was raised and educated in mainland China and had lived in Macao for 5 years. This highlights the complexity of place-based identity in cultural studies.Fourth, the size of the steering group was small. It included members with clinical psychology, communication, anthropology, and Chinese cultural background. Although the steering group did not include experts who developed the intervention, they were consulted at various stages in the adaptation process and provided additional feedback on the modifications. Guidance on the optimal number of people and types of expertise to include in the steering group is not currently available. Future studies should strive to include even more diverse perspectives, including people with expertise in human-centered design, which may complement expertise in psychology, communication, and public health for cultural adaptation studies of digital mental health programs.Fifth, although the bottom-up approach allowed participants to generate ideas for the illustrations on their own, the participants appeared to be exhausted in the repetitive imagination process, which may reduce the efficiency of the focus group discussion. Future studies should consider including participants with proper clinical characteristics and diverse backgrounds to enhance the validity of adapted materials and the program for the target population. Using novel participatory strategies to enhance the dynamic of the discussion is recommended and might include games and mixed media interactive exercises.This study demonstrated the utility of two guidelines for cultural adaptation of SbS. It achieved a culturally appropriate version of the SbS program for Chinese young adults by adapting the elements of the program . The current cultural adaptation study provides an example for future cultural adaptations of digital mental health interventions.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by University of Macau Research Ethics Panel. The patients/participants provided their written informed consent to participate in this study.HS managed the data, performed formal analysis, conducted investigation, and drafted the manuscript. RL assisted with investigation, analysis, and drafting paper. AL provided resourses, assisted investigation, reviewed and edited the draft. WC reviewed and edited the draft. CL provided resourses, reviewed and edited the draft. BH conceptualized the project, handled project administration, provided resourses, drafted the manuscript, and supervised the study. All authors contributed to the article and approved the submitted version.Funding was provided by the Macau Foundation, the University of Macau grant MYRG2018-00241-FSS, and the Johns Hopkins University Center for Global Health.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


On 3 March 2020, the first case of coronavirus was confirmed in the country, approximately five months after 18 October 2019, one of Chile’s biggest episodes of civil unrest since its return to democracy in the 90s [The perennial inequalities in social rights that have had deleterious effects on the quality of life of the most vulnerable groups in Chile, gave rise to the neoliberal model, developed during the Dictatorship in 1973. Although this model positioned the country as one of the fastest growing economies in the continent, it also led to a deep social discontent, especially among Chile’s lower and middle class. These groups have been advocating for reforms in the minimum wage, pension funds, public health, access and cost of higher education in addition to achieving legitimate rights to public goods such as water, improved public transportation and ending corporate collusion and tax evasion .It is in this complex sociopolitical milieu that the COVID-19 pandemic reached Chile. Since 3 March 2020, COVID-19 has magnified profound social gaps in the country and disproportionately affected the low- and middle-class workers, who account for the great majority of the Chilean workforce. Due to their lean financial resources these workers have not been able to afford the luxury of physical distancing strategies such as remaining at home and have had to go to work every day. Given that this group is likely to function in frontline roles as cleaners, public transport drivers, carer, and health workers, this higher exposure risk to COVID-19 is likely to be contributing to their disproportionate affectation by COVID-19. Key governmental institutions such as the public health sector, economic agencies and law enforcement bodies have been called in to steer the Chilean response to COVID-19. However, the credibility of these institutions has been severely damaged by the preceding wave of social unrests and this has undermined their capacity to mount a robust response to the existential threat posed by COVID-19.Given the scenario outlined above, there is palpable mistrust of the main authorities in Chile such as law enforcement, army, senate, the justice system, political parties and Catholic Church . This miAccording to the 2018 National Transparency Study, 61% of the respondents drawn from across the nation felt that the current political parties were too corrupt .Despite the perceived issues with corruption, Chile has a Gini coefficient of 47.7  which isThe Chilean health care system is divided into private and public sectors; this is based on a mandatory contribution and voluntary private health insurance . While tnovel strategy, based on small lockdowns and dynamic quarantines, was proposed by the already highly questioned government. This strategy was mostly implemented at the capital of Chile, Santiago. This city is divided into 32 administrative units, called communes. A dynamic quarantine then, was defined as lockdown of certain communes, based on the number of active cases in that area. These quarantines were then reassessed weekly and lifted or prolonged based on the state of the outbreak in that specific commune. However, decision taking and cut off points were highly dependent on the Ministry of Health, and the methods to determine such measures were not publicly available.It is under these complex sociopolitical circumstances that the discredited authorities are generating and implementing a public health response to the COVID-19 pandemic. The main strategy of the government is to implement large scale COVID-19 testing coupled with quarantine measures to active COVID-19 cases . In addiThis strategy was built on the premise that Chile has the capacity to test and diagnose most COVID-19 infected individuals, so that they can be properly isolated. This way, the economy of the country is protected from the damaging effects of imposing a nationwide lock down. Under these circumstances the low- and middle-income workers who continue to go to work have a higher exposure risk for COVID-19 compared to the wealthy who can afford to properly isolate at home during this period. This inherent inequality further disadvantages the already vulnerable low- and middle-income class.estimations and it is unclear whether these recovered patients have actually cleared the infection and are no longer an infectious risk [Another contentious issue involved the patients who have recovered from COVID-19. Although Chile has a fairly low mortality rate and one of highest recovery rates from COVID-19 worldwide , it has Despite the imposition of dynamic quarantines and partial confinement in an attempt to preserve the national economy, the financial situation appears to have worsened with increasing unemployment. Although the dynamic quarantine approach did show to have an effect on lowering mobility and viral transmission , it was The economic crisis that has ensued led opposition parliamentarians to present a bill that would allow the withdrawal of 10% of the workers' pension savings . The govThese complex issues have combined to make the Chilean response to COVID-19 incredibly unique and challenging task. Some of the measures adopted by the government have not been favorably received by various sections of the community. As the debates continue to rage on the safest and most acceptable approaches to tackling COVID-19 in Chile, the number of COVID-19 cases continue to rise, and the preexisting inequalities continue to grow. The COVID-19 pandemic may be the opportunity for Chile to begin to address the yawning gap in the socio-economic determinants of health such as education, housing, income and access to public goods and services.