Here, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. We used Morlet wavelets to convert strain time series to time-frequency images. Moreover, we only worked with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand, in order to reproduce more realistic experimental conditions. After hyperparameter adjustments, we found that resampling through repeated  GWTC-1 [GWTC-2 [Beginning with the first direct detection of a gravitational wave (GW) by LIGO and Virgo collaborations , the obsGWTC-1  of CBCs  [GWTC-2  of CBCs, [GWTC-2 .The sensitivity of GW detectors has been remarkably increased these last years and, in this improvement process, GW data analysis had a crucial role quantifying and minimizing, as much as possible, the effects of non-Gaussian noise—mainly short-time transients, called “glitches” , for whiMF has shown be a powerful tool having a crucial role in all confirmed detections of GWs that are emitted by CBCs. However, there are significant reasons for exploring alternative detection strategies. To begin, noise outputted by interferometric detectors is non-Gaussian, conflicting with assumption of Gaussian noise in MF. Generally, and also included in LIGO search pipelines, supplementary tools are used for facing this issue as whitening techniques . HoweverElsewhere, one would want to perform more general GW searchings. The natural next step is to include precessing spins , orbitalActually, if we are not interested in replacing MF, still it is pertinent to have alternative algorithms just for independent verifications and/or increasing the confidence level of GW detections.In this context, Machine Learning (ML)  and its The implementation of ML algorithms in GW data analysis has no more than a few years. Biswas et al.  contribuApplications of DL, in particular Convolutional Neural Network (CNN) algorithms, are even more recent in GW data analysis. Gabbard et al. , George From a practical point of view, previous works of CNN algorithms that were applied to GW detection and characterization reach competitive performance results according to standard metrics, such as accuracy, loss functions, false alarm rates, etc., showing feasibility of DL in GW data analysis in the first place. However, from a formal statistical point of view, we warn that, beyond just applications, we are in need of deeper explorations that seriously take the inevitable uncertainty that is involved in DL algorithms into account before putting them as real alternatives to standard pipelines in LIGO and Virgo.For this research, our general goal is to draw on CNN architectures to make a standard GW detection. In particular, beginning from a training set containing single-interferometer strain data, and then transforming it into time-frequency images with a Morlet wavelet transform, the aim is distinguish those samples that are only non-Gaussian noise from those samples that contain a GW embedded in non-Gaussian noise—in ML language, this is just a binary classification. Moreover, as a novel contribution, here we will take two ingredients into consideration to advance towards a statistically informed understanding of involved uncertainties, namely: resampling, and a white-box approach. Resampling consists of repeated experiments for training and testing CNNs, and the white-box approach involves a clear mathematical understanding of how these CNNs internally work. See Our choice of Morlet as a mother wavelet is because we want to capture all time-frequency information of each sample at once rather performing multiresolution analyses for detecting closely space features, given that our CNN algorithms, precisely, will be in charge of performing feature extraction for our image classification. Subsequently, this setup removes, beforehand, the need of working with compact support wavelets for multiresolution analyses such as Haar, Symlet, and Daubechies, among others . Our MorIn this work, we are not interested in reaching higher performance metric values than those reported in previous works, neither testing new CNN architectures, nor using latest real LIGO data and/or latest simulated templates, but rather facing the question about how to clever deal with uncertainties of CNNs, which is an unescapable requesite if we claim that DL techniques are real alternatives to current pipelines. Moreover, as a secondary goal, we want to show that CNN algorithms for GW detection, even when considering repeated experiments, can be easily run in a single local CPU and reach good performance results. The key motivation for this is to make reproducibility easier for the scientific community. We also released all of the code used in this work .With regard to data, we decided to use recordings from S6 LIGO run, separately from H1 and L1 detectors, considering GW signals of CBCs that were only generated by hardware injections. This choice is ultimately motivated by the fact that for reaching stronger conclusions, CNN algorithms should be tested in conditions as adverse as possible. To work solely with hardware injections means to eliminate freedom to set SNR values by hand and, therefore, to remove a choice that can influence performance of CNN algorithms. Subsequently, we draw on S6 data because they have a greater amount of hardware injections than more recent data. See N dimensions,As starting point of our problem, we have a https://www.gw-openscience.org (accessed on 1 April 2019) [For this study, we use real data that were provided by LIGO detectors, which are freely available on the LIGO-Virgo Gravitational Wave Open Science Center (GWOSC), il 2019) . Becausemplitude . Each doD to the source in Mpc, and the expected and the recovered signal-to-noise ratio, namely S6 contains hardware injections that were already added to the noise strain data. These injections are generated by actuating the test masses (mirrors) and, therefore, changing the physical length of the arms of the detectors. This procedure simulates the effects of GWs, and is used for experimental tests and calibration of the detectors . For thiBecause S6 has a much greater mount of hardware injections of CBC GW signals than in later public data, this is the best option for our study. Besides, if we reach good results with S6 data, we are providing highly convincing evidence for GW detection by using CNNs because the sensitivity of S6 is less than in later public data and, even, we could expect that working with later public data our algorithms will work much better.Take in mind that our approach allows for advancing towards more realistic experimental conditions regarding the evaluation of the CNNs. We are showing, beyond any doubt, that we do not have control on distribution of recorded real signals and, therefore, we do not have any ability to influence the performance results. It is known that the SNR values of recorded GW events and their frequency of occurrence are given as (not handleable) facts. Along a LIGO run data, both in hardware injections set and real observing signals set, each event consecutively occurred in real time once and never again. Therefore, if our CNN algorithms are able to deal with this starting adverse evaluation constraint, then we are advancing towards the more general purpose of analyzing arbitrarily distributed data without drawing on ad hoc choices that could generate excessively optimistic performance results.The above advantage does not lie on the set of parameters that characterize hardware injections, which is to say, data populations. In general, hardware injections has been used to perform end-to-end validation checks , and theIt should be stressed that problem of bias and fairness  is an open and highly debate issue in the ML community , even wiIn any case, even if the above advantage is not considered, our approach still is scientifically relevant, because it lays the groundwork for future calibration procedures with CNN detection algorithms inputting hardware injections.D to it. These injections simulate stellar BBHs that are located at the scale of our galaxy supercluster, covering distances from 1 Mpc to 100 Mpc and masses from D increases from 4 Mpc, GW events are less frequent.We downloaded 722 slices of strain data from the H1 detector and 652 slices of strain data from L1 detector, to be pre-process as explained in With LIGO raw strain data segments at hand, the next methodological step is data pre-processing. This has three stages, namely data cleaning, the construction of strain samples, and application of wavelet transform.Data cleaning or data conditioning is a standard stage for reducing the noise and flattening the power spectral density (PSD). It consists in three steps. Firstly, for each Equation , segmentCNNs see , namely 00 s see . Here, fN is the number of points of data in the 128 s raw strain segment, and i the imaginary unit. In theory, PSD is defined as the Fourier transform of the raw data autocorrelation. Subsequently, we implemented the Welch’s estimate [Secondly, a whitening is applied to each estimate , where w sources .k appearing in Equation =Some works in GW data analysis have used raw strain time series directly as input to deep convolutional neural networks ; howIn general, there are several wavelets that can be used as kernel. Here, we decide to work with the complex Morlet wavelet , which, It is important to clarify that Morlet wavelet has a Gaussian shape, then it does not have a compact support. For this reason, the mesh in which we defined Equation , i.e., tSubsequently, to perform the WT of the strain sample Equation ) with reEquation ), we jusIn general, the grid of pixels that are defined by all values Although the size of output images is not too large, we decided to apply a resizing to reduce them from x axis vs. y axis), In summary, after applying the WT and the resizing to each strain data sample, we generated an image dataset In A CNN algorithm does not input waveform templates as isolated samples, but also the distribution of these templates considering the whole training and testing datasets, as Gebhard et al.  criticalk-fold cross-validation (CV) routine. This stochasticity is different to that usually introduced in each learning epoch by taking a mini-batch of the whole training set for updating the model parameters  and, therefore, minimizing the cross-entropy. For this research, we will consider the above two sources of stochasticity.The full problem of dealing with arbitrarily imbalanced datasets, for now, is beyond of our research. Nonetheless, even though working with balanced datasets, a good starting point is to include stochasticity by resampling, which in turn we define as repeated experiments of a global k folds is totally random and each whole k-fold CV experiment is not reproducible in a deterministic fashion. Besides, this approach implemented an experimental setup in which uncertainties are even more evident and need to be seriously treated beyond of just reporting metrics of single values. Indeed, in most common situations with really big datasets , stastistical tools for decreasing system resources, and data changing over time, among others; CNN algorithms are generally set such that their predictions are not deterministic, leading to distributions of performance metrics instead of single value metrics—and demanding formal probabilistic analyses. Given these distributions, with their inherent uncertainties, a statistical paired-sample test is necessary for formally concluding how close or far our CNNs are to a totally random classifier, and that we performed in this work.Stochastic resampling helps to aliviate artificiality that is introduced by a balanced dataset, because the initial splitting into On the other hand, our white-box approach works as a complementary tool to understand how uncertainties influence performance of CNN algorithms. In principle, white-box testings depend on the complex and still open problem of explainability in ML/DL. Nonetheless, for our purposes it is enough to define “white-box” as a methodology that appeals to the information regarding how the CNN algorithms internally behave. A crucial aspect here is to explicitly  describe how each layer of a CNN works and why to choose them. This is actually the most basic explanatory procedure when considering that, from a fundamental point of view, we still do not have analytical theories to explain low generalization errors in DL or even, to establish analytical criteria to unequivocally choose a specific architecture for performing a particular task —in practOur CNN algorithms consist of two main stages in terms of functionality: feature extraction and classification. First stage begins inputting images from a training dataset and the second stage ends ouputing predicted classes for each image sample. Classification is our ultimate goal, consisting in a perceptron stage plus an activation function as usual in ANNs. Feature extraction, on the other and, is the core of CNNs, because it provides the ability of image recognition, consisting in three substages: convolution, detection, and pooling. These substages are implemented through layers that, as a whole, define a stack. Subsequetly, this stack can be connected to a second stack, and so on, until last stack is connected to the classifier. For this work, we tested CNNs with 1, 2, and 3 stacks.MATLAB Deep Learning Toolbox [Knowing that general functionality of our CNNs is a must, but, for contributing with a white-box approach, we need to understand what each layer does. For this reason, we proceed to mathematically describe each kind of layer that were used—not only those that are involved in the already mentioned (sub)stages of the CNN, but also those that were required in our hands-on implementation with the  Toolbox . Take inImage Input Layer. Inputs images and applies a zero-center normalization. Denoting an b is a bias term, and indices p and q run over all values that lead to legal subscripts of m and n, dimension of images Convolution Layer. Convolves each image osen see ; then, oReLU Layer. Applies the Rectified Linear Unit (ReLU) activation function to each neuron (pixel) of each feature map In practice, this layer detects nonlinearities in input sample images; and, its neurons can output true zero values, generating sparse interactions that are useful for reducing system requirements. Besides, this layer does not lead to saturation in hidden units during the learning, because its form, as given by Equation , does noturation  as, for r and s depend on the spatial sizes of outputs maps; and these sizes, i.e., width Max Pooling Layer. Downsamples each feature map Fully Connected Layer. This is the classic perceptron layer used in ANNs and it performs the binary classification. It maps all images j of vector scores of the CNN.Softmax Layer. Applies the softmax activation function to each component rding to , output Classification Layer. Stochastically takes approach , Equatiounction:Eθ=−lnLθ|E the cross-entropy, in addition to two empirical quantities to be set by hand, namely the learning rate As detailed, model parameters are included in vector function . StartinWith regard to the initialization of weights, we draw on Glorot initializer . This scFinally, whether we work with data from H1 or L1 detector, k-fold CV technique [k nonoverlapping subsets to perfom k trials, then (ii) for each For this research, we only used real LIGO strain data with a given and limited number of CBC hardware injections, removing beforehand the instrumental freedom to generate an arbitrarily diverse bank of GW templates based on software injections with numerical and/or analytical templates. Under this approach, we intentionally adopt a limitation in which we cannot generate big datasets, and global validation techniques that are based on resampling are required to reach good statistical confidence and perform fair model evaluations. Even when using syntetic data from numerical relativity, and because of current system resources limitations , the ordechnique , that cok in k-fold CV defines a trade-off between bias and variance. When k is small, the estimation will have lower variance, but bias could be a problem [It is known that the value of  problem . For sev option ,65,66), ,66, k in option , and even much more far from a naive perspective of trivializing model decision as a superficial detail and/or something opaquely given without showing a clear exploration.The first hyperparameter adjustment is shown in t panels b,d show Consider the top panels of Here, it is crucial to assimilate that the optimal setting to choose actually depends on what specifically we have. Let us focus on From In any case, given our current L1 data and based on scatter distribution plot in To find the optimal mount of kernels in convolution layers, we perfomed the adjustment that is shown in Let us concentrate on kernels adjustment for H1 data in the left panel of Kernels adjustment for L1 data is shown in right panel of In summary, based on all of the above adjustments, the best time resolution is Now, let us finish this subsection reporting an interesting additional result. We can ask to what extent magnitude of perturbations from the mini-batch SGD algorithm influence the dispersion of mean accuracy distributions, as was mentioned at the end of nstance, ,39), thiIn general, accuracy provides information regarding the probability of a successful classification, either if we are classifying a noise alone sample .Because the F1 score has the limitation of leaving out true negatives samples, it is recomendable to report it together with G mean1. MATLAB Statistics and Machine Learning Toolbox [As it was mentioned in Equation . In gene Toolbox —for theo Toolbox  and/or [ Toolbox .The NB and SVM models need vectors as input, then we apply a reshaping operation: each image sample Take in mind that there is not definitive criteria to generate ROC curves under the resampling regime. Subsequently, following the same approach that was taken in Notice that, on all ROC curves, a specific point have been highlighted. This is called an “optimal operating point” (OOP) and it corresponds to the particular optimal threshold (OT) in which a classifier has the best trade-off between the costs of missing noise aline samples, ed cost: costexp=PIn general, the relative performance between models can change depending on whether their ROC curves intersect. Because of this, we would like to have a metric for summarizing, regardless of thresholds, the performance of a model in a single scalar. Here, we used the total area under the ROC curve (AUC) ; this isTwo related analyses were conducted to ensure that the results are statistically significant were performed, as mentioned in k-fold CV experiment. Subsequently, given that, here, we obtained medians that were slightly below of Moreover, whether shuffling is present or not, the boxplot of mean accuracies has positive or negative skewness, respectively. This makes sense, because, without shuffling, the higher mean accuracies, the greater the effort of the CNN for reaching performances with those accuracies; there is not free lunch and we expect to have a higher concentration of samples below the median than above the median and, therefore, a positive skewness. On the other hand, if we have shuffling, we know from the basics of probability that adding new points, one-by-one, to the mean accuracy distribution, is actually a stochastically symmetric process around Descriptive statistics is a reasonable analysis, but, to make a formal conclusion regarding the significance of our results, we performed a sample-paired t-test. Therefore, we first define the mean accuracy datasetsSubsequently, assuming a significance level As final analysis, we focus on output scoring of the CNNs. Our CNNs output scores that are probabilities generated by the softmax layer, as explained in Firstly, in both panels of For The above results actually mean that, given our datasets, our CNNs are more optimistic predicting GW samples than predicting noise alone samples or, equivantly, more pessimistic predicting noise alone samples than predicting GW events. Hence, even though our input datasets are exactly class-balanced, predictive behavior of our CNNs is highly class dependent. Under a frequentist approach, the asymmetric shape of distributions for It is also important to notice how, depending on data, the distribution of occurrences, either for In this work, we applied CNN algorithms for the detection of GW signals from CBCs, through a binary classication with samples of non-Gaussian noise only and samples of non-Gaussian noise plus GW injections. Being a crucial part of the data pre-processing, we applied a Morlet wavelet transform, to convert our time series vectors  in time-frequency matrices . Additionally, the resulting images were automatically decoded in the convolutional stacks of our CNNs. Besides, images in time-frequency representation were reduced in such a way that all of our CNNs were easily run in a single local CPU, reaching good performance results.The significant novel contribution of our work is adopting a resampling white-box approach, being motivated by the need to advance towards a statistically informed understanding of uncertainties that are involved in CNNs. Moreover, as a manner to reproduce a more realistic experimental setting for testing our CNN algorithms, we draw on single-interferometer data from LIGO S6 run, considering raw strain data with noise and hardware injections of GW signals solely; that is to say, we removed the instrumental freedom of generating distributions of simulated GW signals as intensely and/or frequently as one would want.Because of introducing stochasticity by repeated 10-fold CV experiments, almost all of the tasks were more complex than in a simple deterministic fashion, but it also forced us to acquire useful tools to overcome too much optimistic or too much pessimistic evaluations of CNN algorithms. Hyperparameter adjustments required careful interpretations of mean accuracy distributions. We tested several CNN architectures and found two that achieve optimal peformances, one with 3 stacks and 32 kernels for data from H1 detector, and other with 2 stacks and 16 kernels for data from L1 detector, and in both cases with a resolution tentative, because the mentioned conditions for SNR values actually depend on the nature of initial datasets that are inputted by the CNNs that, in our case, are still class-balanced. At this point, it is evident that more research is necessary to begin dealing with arbitrarily class-imbalanced data.From analyses of confusion matrices and standard metrics, we found that whether working with H1 data or L1 data, CNN algorithms are quite precise to detect noise samples, but are not sensitive enough to recover GW signals. These results mean that, in the context of their two-label predictions, CNN algorithms are better suitable for noise sample detection than the generation of GW triggers—the results that can serve as motivation to explore alternative strategies in which CNNs cab be used as complementary pipelines for noise detection and be systematically compared with current noise reduction techniques used in LIGO pipelines. However, these conclusions regarding noise detection are not totally definitive, because if we considering only GW events of With ROC curves, we also compared the CNNs with other two classic ML models, NB and SVM, reaching that CNNs have much better performances than the mentioned classic ML models—a result that is consistent with what has been reported in recent years in most of the works of DL applied to GW detection. From ROC analyses, we also found optimal thresholds, which are very useful parameters in establishing a statistical decision criterion for the classification, namely In order to elucidate whether our predictions are statistically significant, we performed a paired-sample t-test, obtaining that the performace of CNN algorithms is significantly different to that of a totally random classifier, with a confidence level of We presented a detailed cross-disciplinary exploration to seriously deal with the uncertainties of CNN algorithms. In particular, we show that, for achieving this goal, it is fundamental to have clear statistical information regarding the advantages and disadvantages of CNN algorithms. In general, we highly recommend that future works focused on testing DL algorithms in GW analysis should focus on the problem of how to establish more realistic experimental settings in their metodologies, and how to deal with difficulties that arise from these settings, paying greater attention to uncertainties. At the end, this is one of the main pathways to claim that DL techniques are real alternatives to standard GW detection algorithms.


Digital health agents — embodied conversational agents designed specifically for health interventions — provide a promising alternative or supplement to behavioral health services by reducing barriers to access to care.Our goals were to (1) develop an expressive, speech-enabled digital health agent operating in a 3-dimensional virtual environment to deliver a brief behavioral health intervention over the internet to reduce alcohol use and to (2) understand its acceptability, feasibility, and utility with its end users.We developed an expressive, speech-enabled digital health agent with facial expressions and body gestures operating in a 3-dimensional virtual office and able to deliver a brief behavioral health intervention over the internet to reduce alcohol use. We then asked 51 alcohol users to report on the digital health agent acceptability, feasibility, and utility.The developed digital health agent uses speech recognition and a model of empathetic verbal and nonverbal behaviors to engage the user, and its performance enabled it to successfully deliver a brief behavioral health intervention over the internet to reduce alcohol use. Descriptive statistics indicated that participants had overwhelmingly positive experiences with the digital health agent, including engagement with the technology, acceptance, perceived utility, and intent to use the technology. Illustrative qualitative quotes provided further insight about the potential reach and impact of digital health agents in behavioral health care.Web-delivered interventions delivered by expressive, speech-enabled digital health agents may provide an exciting complement or alternative to traditional one-on-one treatment. They may be especially helpful for hard-to-reach communities with behavioral workforce shortages. Adaptations of MI have bourgeoned to meet the need for motivational interventions that are brief and thus compatible within primary care settings [Alcohol use disorder (AUD) affects 10%-20% of men and 5%-10% of women over their lifetime, and 26.4% of adults engage in binge drinking. AUD is the third leading preventable cause of death . It is asettings -5. Despisettings .A number of barriers prevent individuals from accessing the treatment they need, including acknowledging the need for treatment , availabDigital health interventions (DHIs) are interventions that are delivered via digital platforms . Unlike telehealth (where a live provider meets with a consumer via a video chat), DHIs do not rely on a human provider to deliver services. As such, they have the potential to reduce a number of barriers associated with location (can be accessed from anywhere), scheduling (can be accessed at any time), stigma (can be accessed anonymously from the privacy of one’s home), and cost (most are affordable or free). Past research indicates that consumers tend to be more truthful when disclosing possibly stigmatizing information such as a drug or alcohol disorder to a computer versus a human -16 — proA review of DHIs  indicateA review of DHIs designed specifically for MI  further The review by Shingleton and Palfai  also fouECAs  are simulated human characters that may have the potential to increase consumer engagement in DHIs . Unlike In spite of their success, however, ECA development did not scale with the now abundant internet devices  and the latest progress in 3D graphics. Some attempts have been made to build web-based, 3D ECAs -53. Howeassessment of, followed by normative feedback, then menu of change options), making them amenable to delivery via DHI [This study aimed to fill the gap in knowledge of using ECAs in behavioral health contexts by establishing the acceptability, feasibility, and utility of using ECAs by consumers undergoing a BMI intervention for alcohol abuse. BMIs are highly structured  ,55 to guThe intervention is based on DCU — an evidence-based intervention that uses strategies from MI . First, Feedback about client’s individual status is personalized and stored in a user model database, keeping a record of users’ answers for the next session(s); Responsibility for changing is left with the individual, and the language used throughout the intervention reflects this ; Advice is provided in a supportive manner, with empathic choice of words and supportive body gestures ; Menus of different options for changing that respect an individual’s readiness to change are offered; Empathic style of communication is central to the individual-clinician relationship, and it is conveyed by the ECA’s verbal utterances , nonverbal behavior , and empathic choice of wording ; and Self-efficacy is nurtured and emphasized throughout, including with choice of words and positive facial expressions .In this study, the DCU was delivered via an ECA (namely eEVA) rather than via its traditional textual interface. As a BMI, the eEVA intervention combines MI style of communication with the common underlying elements of effective brief interventions characterized by the acronym FRAMES : FeedbacOur eEVA framework shown in  providesTechnically speaking, the framework consists of 3 main components. First, the application layer consists of a modular client-side JavaScript mainframe that controls the multimodal user interface, audio and video input, graphical user interface (GUI) interaction, and services such as speech recognition and speech synthesis. Second, the JavaScript mainframe handles execution of a scenario (the content of the DCU in this study) — a collection of state machines that are created by developers. Third, the scenario states can be constructed to pull information  from the data layer — a database of content.The backbone of the client-side application is a JavaScript framework that handles the formation of a group of modules and the communication between them. Each module then implements various functionalities, including gaining feedback from the user  and processing input information ; determining how to answer the user ; and answering the user through a multimodal 3D-embodied ECA, with speech synthesis, NVBs, and multimedia content .This results in an interactive online application that can run on numerous platforms such as desktop, cell phone, autonomous robotic agent, and potentially smartwatch integrations . In addiUnlike traditional ECAs, the eEVA design follows common modularity patterns found in robotics platforms such as ROS , allowin, including an ECA behavior module, vocal command interpretation, and the scenario controller; and output/actuator modules for actually expressing verbal and nonverbal cues to the user. The functionalities of the main modules used in the current version of eEVA are listed in To model social interaction, 3 main categories of module functionality are necessary, namely input/sensing modules ; social interaction decision-making modulesWe tested 2 types of network connections: broadband and 4G mobile data. The majority of the launch time consists of loading the 3D character and surrounding virtual environment, which takes about 30 seconds and 25 seconds on 4G and broadband, respectively. The experiments  showed tTo simulate some of the communication psycholinguistic signals of a counselor delivering a BMI, we first videotaped BMI sessions between a live licensed counselor and a client. Then, a clinical expert reviewed the videotapes to code verbal reflections and NVB. From these, the expert generated a set of rules for basal behaviors of the health agents. Based on the codes of verbal and NVBs, eEVA was implemented with the following verbal reflections: “Ah.” “Alright.” “Okay.” “Good.” “Sounds Good.” “Oh, okay.” “Great!” “Thanks for letting me know.” “Oh, I see.” “Okay, thanks!”In addition, the following NVB animations were synthesized on the agent’s face: smile, facial expressions, hand gestures , body leans (forward), head gestures , and eyebrow movements , which our results (discussed later) showed are conducive of a positive experience for the user with the agent in the given health care context.Since it was determined that head nods are critical to portraying (some level of) active listening, we sought to offer 3 variants depending on the user’s chosen responses. We created a set of nods using established emotional expressions governed by activation of specific individual facial movement animations. All 3 of the head nods included activation of head down and eyes down. However, depending on the type of reaction required, these also included facial expressions .The patterns observed in the videotapes of the counselor-client session also directed us towards creating rules about when certain statements should be made, to ensure the counselor did not appear judgmental and to make the interaction appear more natural. In all scenarios, the counselor began seated at the desk while typing on the computer, then looked up and moved to the chair closer to where the user perceives they are sitting during an initial greeting. Once seated, the counselor began with a greeting introduction and then moved on to delivering the DCU. Verbal responses to user responses were applied based on the “score” of each question to provide nonjudgmental reactions for higher scores that might indicate a problem and positive reactions for scores that might suggest healthy consumption levels. For example, “Sounds good” was used in response to a user mentioning that they wanted to change.Neutral state, the counselor spoke and used a smile, a gaze, facial expressions, head gestures, or eyebrow movements. In the Explaining state, which was activated during long periods of speaking, the two-handed flip and two-handed contrast gesture were applied. When the counselor posited a question to the user, the Asking state was initiated, which included a single hand formless flick. While the counselor waited for the user to respond to a question, the Listening mode was initiated, which included a leaning forward gesture. The leaning gesture remained in effect until a choice was made, at which point the body resumed an upright sitting position.In parallel, NVBs were applied using a set of states that were determined as appropriate given the context of the interaction as shown in Participants were alcohol users aged 21 to 55 years and recruited online to participate in the intervention. Participants had to have engaged in heavy drinking (consumed 5 drinks in one sitting at least once in the past year), not currently be receiving treatment for their AUD, and not have a medical condition for which alcohol use would be contraindicated. Users were also screened for severe mental illness. We recruited 51 participants as part of a larger RCT of the effectiveness of this program in reducing alcohol abuse. Participants were 62% (32/51) male, were 32% (19/51) female, and had a mean age of 28 (SD 15.8) years. Participants reported their race as White , Black , Asian , Other , and no response . Participants reported their ethnicity as non-Hispanic , Hispanic , and no response . Marital status was reported as married , single , divorced , widowed , or no response . Their education level was reported as high school , some college , Associate’s degree , Bachelor’s degree , and Graduate degree .Participants were recruited online via targeted advertising on Facebook, offering free treatment for alcohol users and compensation for research participation. When users clicked on the ad, they were redirected to an online screener  to ensure they were eligible for the study. If they were eligible, users were randomly assigned to receive the same DCU MI intervention delivered online either by an ECA (eEVA) or a textual interface. Participants in this study were those who were assigned to eEVA, since the control group assigned to the text-only interface could not comment on the ECA’s social features that they did not see. Once randomized, participants were provided with a username and password to enter the DHI platform eEVA. Participants had to log on, enter demographic information, and begin the intervention. After completing the intervention (participants were given 1 week to complete), they were redirected to an online survey on Qualtrics to provide feedback about their experience with the intervention.After completing the intervention, participants were asked to provide feedback on the feasibility, acceptability, and utility of the technology. Specifically, we sought feedback regarding their engagement (acceptability), perceived utility, and intent to use the technology (feasibility). Participants completed a questionnaire developed for this study. Since there does not exist, to date, a standardized instrument to evaluate interaction with ECAs of various levels of complexities, we used and adapted relevant existing questionnaires commonly used for the evaluation of human interaction with technologies involving some social cues, whether embodied with graphics or with robot technologies. Questions were based on a combination of the engagement model by O’Brien and Toms , Almere The engagement model by O’Brien and Toms  exploresThe Almere model evaluates the user’s acceptance of the digital health agent by relying on constructs from the Unified Theory of Acceptance and Use of Technology . Users rThe Godspeed questionnaire consists of 12 questions that capture 5 constructs that measure human-like traits of robots, which we adapted for ECA : (1) anthropomorphism , (2) animacy , (3) likeability , (4) perceived intelligence , and (5) perceived safety . Each trait could be rated as being very human-like to very unhuman-like on a 7-point scale.All questions included a fill-in option for participants to expand on their numerical responses with qualitative feedback if they wanted to. We highlight some of those comments in the Results section.Participants reported high acceptability and utility of the technology, as indicated by their scores on the Almere model questions . Most participants reported enjoying their interaction with the agent  stating “The questions she asked me, no one had asked me before and helped recognize my drinking problem.” They thought the agent was both physically appealing  and had a pleasant voice , stating:He is neat, he has a good haircut, he is well dressed.He appears wise, intelligent, and healthy.The voice was adequate, calm, and confident.Furthermore, participants indicated that they found it easy to interact with the health agent  and they learned to do so quickly , stating:I just followed the instructions and voila!I just waited and followed his instructions; it was easy.Participants had more difficulty with the voice feature of the technology, with only 69% (35/51) reporting that they felt like the agent understood them when they spoke into the microphone. Based on the qualitative feedback, this may have been due to issues with participants’ microphone setting rather than the technology itself:I set my microphone up, but it seems there is a problem with it.I tried to speak my answers, but it never worked so I ended up typing them.Those who were able to get their microphones to work seemed to have no difficulty speaking to the agent as echoed by their qualitative feedback . Despite some difficulties with the microphone, 78% (40/51) felt like they could have a conversation with the agent, stating “I felt that he knows me, knows what I want” and reported that they sometimes felt like they were talking to a real person , stating that:…his voice sounded quite real……the gestures he made, way he moved around…he answered me like a real person…Participants further reported that the health assistant was friendly  and they found it to be useful  because:…he explained things that I did not know……it helped me recognize that I have a problem…I learned a lot.more comfortable disclosing their drinking to the digital health assistant over their medical doctor. Indeed, participants highlighted:Participants reported that they were comfortable disclosing information about their drinking to their digital health assistant , with 83% (41/51) reporting that they were The assistant gives me a level of trust that I don’t have with other humans.It is more easy talking to  than to a real person.I don’t feel like they are judging me.Finally, participants reported trusting the advice the health agent gave them  and that they planned on following that advice , stating:I think she is sincere and wants to help me with my problems.…because it is based on facts and studies and that is real and valid information for me.— perfectly capturing the intent of MI.One participant noted “I didn’t feel like I was given advice, more like information to be able to make my own decision. I was the one with the power to give myself advice.” Participants were highly engaged with the DHI, as indicated by their score on the engagement questions , indicating that the majority of participants agreed with statements around how engaged they were. Specifically, 69% (35/51) were not worried about making mistakes while using the technology, stating:At first, it was a little bit intimidating, but then I felt confident.The assistant feels understanding, attentive, very friendly.A majority  thought it was a good idea to use the health assistant, reporting:He is kind of like a home counselor who works with reliable information and statistics.It is practical, easy to use, and guides the person on what to do without forcing us to make a final decision..”Participants felt that the system could be adaptive to their needs , stating that “it could be adapted to other health problems like smokingFinally, 88% (45/51) found the health assistant to be interesting, indicating “I was impressed by the way it converts my answers into figures and important information for my health” and said they would interact with the agent again :Setting a new exchange with the health assistant would help me to reach my goal.Participants reported a high number of human-like traits on the Godspeed questions . Participants reported that the agent moved appropriately  and seemed warm , responsive , knowledgeable , relaxed , flexible , honest , respectful , confident , interested , open-minded or nonjudgmental , and supportive .Overall, participants’ responses to the questionnaires and qualitative feedback indicated that they found the delivery of an MI intervention by a digital health agent over the internet to be acceptable, be engaging, and have features that are close to human-like.Our goal with this study was to understand if the technology we developed was feasible (able to be implemented online), acceptable, useful, and easy to use by consumers. As such, we focused largely on the technology aspects of the intervention.This study provides an optimistic outlook for the use of digital health agents to deliver brief online interventions in the future. Consumers overwhelmingly reported positive experiences in their interactions with the agent, with many reporting that they trusted the agent and felt that they could more comfortably disclose information that they may not have disclosed to a human provider. This echoes what has been found in the literature around disclosing to computers versus humans ,14,64,65Further, participants tended to attribute many human-like traits to their agent  and commented positively on the physical appearance, voice, and physical gestures of the agent. Our team engaged in coding of verbal reflections, hand gestures, and facial expressions of a real therapist to enhance the digital agent’s nonverbal communication to resemble what a therapist might do in session .Implications for this work are important. Given the shortage of mental health workforces in many locations, digital health agents may provide an acceptable complement to traditional face-to-face therapy, reducing demand for higher levels of care, where a digital health agent can act as a clinician “extender” to deliver booster sessions. Similar to telehealth services, digital health agents resolve a number of barriers to care such as transportation and scheduling.In addition, digital health agents reduce stigma around mental health care, are less costly than one-on-one therapy, and can be scaled out and disseminated. For individuals with high levels of social anxiety, digital health agents may provide them with a unique opportunity to get help. Given the digital nature of the agent, it is possible to adjust the programming to make the agent able to speak in multiple languages, reducing language barriers for minority and refugee populations. Already, consumers can pick a digital health agent from a library of diverse options of physical visual features . Other interventions could be delivered to screen or treat a variety of problems. Furthermore, our team is working on integrating natural language dialog management features such that the agent will respond to the consumer’s answers without needing specific input from which to read. We conducted preliminary work  for a spDespite all these exciting potential developments, it is critical to evaluate DHIs with high levels of rigor before they can be deployed for the population at large. As pointed out by Carroll , very feWe furthermore consider that these digital health agents and DHIs can only complement the unique experience of psychosocial therapy and serve as “clinician extenders” . As sugg


Emphasizing the importance of language is a key characteristic of philosophical reflection in general and of bioethics in particular. Rather than trying to eliminate the historicity and ambiguity of language, a continental approach to bioethics will make conscious use of it, for instance by closely studying the history of the key terms we employ in bioethical debates. Continental bioethics entails a focus on the historical vicissitudes of the key signifiers of the bioethical vocabulary, urging us to study the history of terms such as “bioethics,” “autonomy,” “privacy,” and “consensus.” Instead of trying to define such terms as clearly and unequivocally as possible, a continental approach rather requires us to take a step backwards, tracing the historical backdrop of the words currently in vogue. By comparing the original meanings of terms with their current meanings, and by considering important moments of transition in their history, obfuscated dimensions of meaning can be retrieved. Thus, notwithstanding a number of methodological challenges involved in etymological exercises, they may foster moral articulacy and enhance our ability to come to terms with moral dilemmas we are facing. Emphasizing the importance of language is a key characteristic of philosophical reflection in general and of bioethics in particular. Analytic philosophy is known for its emphasis on clear definitions and rigorous arguments, up to the point of trying to create a logically perfect language, neutral vis-a-vis metaphysical world-views and freed from the ambiguities and historical heritages pervading everyday grammars and vocabularies Beaney . Contineterminus, indicating that a “term” is the temporary end-point of a long linguistic journey.A continental approach to bioethics will pay due attention to the history (the diachronic dimension) of moral language, I will argue. Continental bioethics entails a focus on the historical vicissitudes of the key signifiers of the bioethical vocabulary, urging us to study the history of terms such as “bioethics,” “autonomy,” “privacy,” and “consensus.” Instead of trying to define such terms as clearly and unequivocally as possible, a continental approach rather requires us to take a step backwards, tracing the historical backdrop of the words we currently use. In order to grasp the meaning of the term “ethics,” for instance, it makes sense to start with the original meaning of words such as ἦθος and ἠθικός in ancient Greek. Notice that the word “term” itself comes from the Latin noun Studying the history of key signifiers entails an etymological disambiguation or even “reduction,” albeit in the literal  sense, for “reduction” literally means “bringing back” (“reducere” in Latin) multiple pathways of meaning. The historical development of key bioethical signifiers is not necessarily seen as progress (resulting in increased clarity for instance). Rather, etymological exercises will often reveal instances of loss, forgetfulness, and erosion of meaning. What is obfuscated in contemporary usage may actually prove quite valuable and point to lost aspects of meaning which may help us to come to terms with contemporary dilemmas. Thus, the etymological turn in bioethics often entails an exercise in retrieval. The ultimate aim of an etymological detour is not to contribute to language studies but to enlighten current moral challenges, allowing us to address normative dilemmas emerging today more adequately, by enhancing our sensitivity to linguistic nuances and complexities. Thus, the etymological argument substantiates the claim, put forward by Mills , that coWe may start our endeavour by applying the etymological argument to (the history of) the (fairly recent) term “bioethics” itself. Introduced in 1927, the term initially referred to responsibilities of humans towards animals, plants, and the biosphere Sass . When thGenealogy of Morals, for instance, Nietzsche  between thinking and being, or, in more modern terms, between theoretical conjectures and empirical observations. Heidegger points out that the Greek term for truth (ἀλήθεια) had a different meaning. The privative α- suggests that something had been forgotten which is now being revealed, retrieved, or disclosed, resulting in Heidegger’s rival understanding of truth as “un-forgetfulness.”Martin Heidegger is likewise a prominent example of a continental philosopher who made ample use of etymology in his work. In What is called thinking? Heidegger . From an etymological point of view, method means openness, a willingness to reconsider the path we have explored in retrospect  and bad (which has the opposite meaning in contemporary English). According to Freud, in phenomena such as dreams, this basic ambiguity of primordial words is still retained and exploited. This line of research was taken up and elaborated further by Jacques Lacan, who combined Freudian psychoanalysis with structural linguistics  and the concepts signified by them  via Nietzsche and Heidegger up to Freudian and Lacanian psychoanalysis. Freud /1943 for Gillett .con-struction), while différance is a deliberate misspelling of difference , indicating that words can never be defined exhaustively, but only by appealing to other words, from which they differ, so that their meaning is forever deferred through endless chains of signifiers. A similar attitude to language can already be encountered in Jacques Lacan, who translated Heidegger’s essay Logos (Heidegger nom-du-père (pronounced in the same way as non-du-père) to emphasize how the prohibitive function of (God) the father inaugurates the symbolic order. Another example of Lacan’s use of language is extimacy  in the context of organ transplantation and intimate technologies , thereby questioning the binary logic of traditional ontologies in a playful manner. Thus, although the motives are similar , the technique employed by the etymological argument is a slightly different one (tracing the history of terms back to the moment of commencement). Yet, the etymological argument is not absent in their work. In a treatise on religion for instance, Derrida (to bind to (to hold back) versus to relink , an etymology which reveals two apparently contradicting roles (conservation versus disruption) which religion may have.1Building on Heidegger, chronic discontent in established discourse, seeing it as profoundly questionable, was taken up by later authors, for instance Jacques Derrida, who introduced key terms such as “deconstruction” and “différance” in order to move way from a language he considered tainted by a logic of binary oppositions. The term “deconstruction” plays on Heidegger’s concept of “destruction,” i.e., the effort to reveal the original meaning of terms by eliminating the sediments of established ontological interpretations , namely: naturalness, autonomy, consensus, and privacy.I will now elucidate the relevance of etymology for bioethics, focusing on a number of key signifiers  aimed to segregate the natural from the normative. As nature became disenchanted, the empirical and normative dimensions of the verb “to observe” became disconnected. Observing natural processes seemed quite different from observing rules. In bioethics, this segregation was endorsed by many, and naturalness has been vehemently rejected as a valid argument in moral deliberations. Trying to derive norms from nature allegedly equalled falling into a trap known as the naturalistic fallacy Zwart .In everyday deliberations, however, the idea that what is natural is good can still be encountered, up to this day. Outside bioethics, in everyday discourse, the label “unnatural” is often used to indicate that something  is morally questionable. This evidently results in a tension between expert bioethical discourse  and public debates .An example of this, taken from the debate about genetic modification of animals and crops, is the argument that traditional forms of hybridization (employed by humans since the Neolithic revolution) are allegedly more “natural”  compared to genetic modification techniques . Yet, etymology informs us that the word “hybrid” already entails an element of caution or even normative disapproval. It is derived from the Greek term hubris (ὕβρις) which means excessive self-confidence: an insolence against the divine order. Apparently, there is already a signal of warning against hybridization embedded in language itself (Meilaender Antigone is the text where the signifier “autonomy” (αὑτ ό νομος) is used for the very first time  it is clear that nowadays the term no longer has the same meaning  as it did in ancient Greece  dimension, for autonomy also means responsiveness to a norm, the conscious endorsement of a norm, a world-view even.For contemporary readers, it has become difficult to fathom the precise nature of Antigone’s divine calling because the signifier ce Zwart . For Kance Zwart /1974. Ince Zwart  explainscalculating subject. The voice from “elsewhere” which addresses her may not be so readily articulatable in terms of standard bioethics discourse. It may be something quite difficult to accommodate from a standard bioethical view. The etymological detour increases our sensitivity to the fact that there may be more to autonomy than mere self-determination.2Yet, this forgetfulness is far from absolute. In contemporary moral collisions, in bioethical tragedies, certain unsettling aspects may resurge which suggest that there is more to autonomy than mere self-determination and that even in the secular present, human individuals may be driven by opaque imperatives which may prove harmful or even detrimental  to themselves. Antigone may be considered an “autonomous” subject but not in the sense of a modern This explains what in linguistics  is referred to as “displacement,” i.e., the tendency to replace loaded terms (such as “autonomy”) with substitutes which seem less burdened by cultural heritage, such as “self-determination”: apparently a smoother, less commanding alternative for “autonomy.” The latter is a “heavier” term, “tainted” if you like by history. According to Nunner-Winkler , for insconsensus is not as neutral as it may seem. Historically, a connection can be discerned between the  signifier “consensus” and a particular world-view, a particular moral tradition, in this case Protestantism  and the Consensus Pastorum Genevensium , for instance, refer to crucial meetings organized during the early days of the Reformation in order to achieve consensus among theologians in challenging and potentially disruptive disputes. These consensus conferences were deemed necessary because a central magisterium was no longer available. For protestant theologians, the papal Holy See could no longer play that role. This historical legacy  seems to reverberate in bioethics, for instance in the case of the Appleton consensus (1988), involving a group of physicians and bioethicists from ten different countries who met at Appleton, Wisconsin, to discuss guidelines for decisions to forego medical treatment  manner of posing questions, of structuring problems (“questionable” in the non-pejorative sense of the term). The etymological argument allows us (and urges us) to become sensitive to this. One of the performative implications of adopting the term “consensus” is that we sacrifice the quest for truth  to opt for “mere” consensus instead. Consensus means consensus among experts, attained in accordance with a particular methodology  and is by definition of a temporary nature.The genealogy of the term does not disqualify its current use or relevance as such, of course. What is questionable is not the validity of the concept  but its alleged neutrality. The signifier privacy is regarded as something positive, a basic human right, something good which deserves to be protected. The etymology of the term, however, should raise some suspicion. Etymologically speaking, privacy comes from “privation” , a pejorative term, a negative signifier, indicating a lack. The term privation suggests that the individual in question misses something, is deprived of something, e.g., care and attention by others. It suggests living in seclusion, outside the circuits of solidarity and sharing. Somehow, a transvaluation of values has occurred, changing a negative term into a positive one because nowadays, by replacing privation by privacy, this negative term, indicating absence (privation), has been reverted into something positive (privacy), a right or good which ought to be protected. In short, a displacement has occurred from privation  to privacy .Let me add another example, namely the signifier “privacy,” to elucidate the relevance of the etymological approach. In mainstream bioethics, image of human beings, seeing them as disconnected autonomous atoms, rather than as interconnected elements in symbiotic sociocultural ecosystems. This is precisely where privacy differs from confidentiality, I would argue. For although both terms at first glance may seem quite similar, confidentiality literally means to entrust sensitive information to someone  whom we trust . Thus, while confidentiality presupposes a personal trust relationship, privacy is a more abstract and anonymous term, connected with responsible data management, for instance in the context of genetic testing, where regulating the relationship between insurance companies and their clients is based on distance or even distrust rather than trust  The starting point is the intuition that a particular term is used improperly or inauthentically in current discourse, in the sense that it has drifted away from its original meaning, even up to the point of acquiring connotations that seem juxtaposed to the initial ones. Important aspects of meaning seem to be neglected. In other words, the etymological detour is often fuelled by a sense of discontent in contemporary deliberation: the awareness that we somehow lack the moral articulacy to capture the true complexity of a moral problem situation. Something of importance may have been lost or may have been neglected.(b) Subsequently, the etymological argument builds on the conviction that the proper (genuine) meaning of the term can be retrieved by tracing its history, notably by recollecting it original meaning.(c) This original meaning has been dissimulated and obfuscated  in current usage. In order to retrieve the original meaning, we must bracket the current  meaning and investigate the origin and history of the term.(d) Finally, by comparing the original term with its current meaning, and by considering important moments of transition in the history of the terms we use, these obfuscated dimensions can be recollected.The etymological argument consists of a number of steps:per via di levare, i.e., by taking away (by consciously removing ambiguous layers of meaning), a continental approach rather uses etymology to expose and explore the basic ambiguities at work in the terms we employ, often without being conscious of them. In principle, awareness of multi-faceted aspects of meaning is considered a strength, for it will make us better listeners and readers. In short, it will enhance our basic ethical skills.Thus, by studying the history of terms (seen as temporary end-points), eroded aspects of meaning (discarded or obfuscated during previous stages of deliberation) may be retrieved. While an analytic philosophical approach aims to reduce meaning tool or instrument in ancient Greek. The identification of bodily organs with “tools” seems supportive of understanding the human body as an aggregate of replaceable parts: of partial objects (organs) rather than as an organic whole . Even if we remember the term, relying on “dead” written documents, we will probably miss something . This does not imply that etymology is an impossible objective. Rather we must see etymological “reduction” as an interminable analysis, to use the Freudian phrase.In short, etymological exercises may prove a complicated challenge. First of all, etymology often guides us back to ancient Greek and Latin, as we have seen. And although many contemporary scholars may still possess some basic knowledge of these languages, the vast majority will no longer be fluid in them and therefore no longer sensitive to all the nuances of meaning once at work here. Heidegger himself already emphasized this when he desperately tried to retrieve the meaning of pre-Socratic sayings. We no longer If etymology is such a difficult or even “questionable” exercise, in the non-pejorative sense of the term—namely in the sense that, eventually, completion may never be achieved—the question may rightfully be asked why we should dive into this at all? Isn’t there an easier or more reliable alternative to care for language? Shouldn’t we opt for unequivocal definitions, for instance, as analytic philosophers would argue, allowing us to rid ourselves of all historical sediments of meaning? Various strategies can be applied to forgo linguistic ambiguities, such as formalization, replacing everyday language  by rigorous logic, complemented perhaps by quantification, by an empirical  approach (providing us with verifiable facts). Indeed, combining clear definitions with hard facts, wouldn’t that be more helpful to solve our problems?The etymological argument entails, however, that eventually, such alternatives will not really help us. Genuine dilemmas and tragic collisions will continue to fuel discontent in linguistic straitjackets, bent on eliminating complexity. The removal of ambiguity will eventually give rise to a return of the repressed. The straitjacket of rigorous definitions will result in discontent in apparently consistent argumentations. When faced with tragic dilemmas, we are bound to experience the poverty of our language, our lack of moral fluency and literacy, hindering us to articulate important experiences that seem to escape us, so that logical analysis, consistent as it may be, may be experienced as a violation of the complexity of the situation we are facing. This is a debate which has haunted bioethical discourse from the very start (from the 1970s onwards), and my aim is not to solve it in this paper. What I do want to point out is that the analytic approach (relying on clear definitions and rigorous argumentation) is not the only alternative available. There is another, more “continental” approach to this, where ambiguity and complexity are celebrated rather than eliminated.reduction in the literal, etymological sense of the term) questions the modernistic idea that history equals progress. Tracing the history of terms from primordial commencement to present meaning does not necessarily foster a conservative attitude, however. Rather than entailing a nostalgic craving to return to the past, the etymological argument summons us to reconsider the meaning of the terms we use under present circumstances. Rather than seeing history in terms of linear progress or decline, the vicissitudes of moral signifiers will often reflect a fairly dramatic curve. Initially, a word may be introduced without consciously and explicitly exploring and addressing all the ambiguities involved. At a certain point, internal tensions may increase, giving rise to confusion or even conflict, and this may force us to redefine the term at hand, resulting in a process of catharsis (linguistic cleansing). The etymological argument entails, however, that the terms we use will continue to carry the scars of these previous events, these previous conflicts and cleansings. Their history is somehow retained in them. This explains why the etymological argument is notably meaningful when facing complex dilemmas. In a critical situation, the inherent conflicts embedded in our language tend to be re-invoked.Another possible objection against the use of etymology in bioethics is that it seems inherently conservative. The act of guiding us backwards into history  and populus (Latin) respectively  that the words we use are more open to the inherent complexities of the dilemmas we are facing than their straightforward definitions seem to suggest. Etymology will reveal rather than reduce complexity. After an etymological detour, we may decide to adopt one possible meaning rather than others but now in the form of a conscious decision. Etymological detours inform us that established definitions of bioethical signifiers benefit one particular interpretation at the expense of others. These decisions and interpretations are inherently questionable, however, so that etymology enables us to question them, by proposing alternative options. Etymology reveals the intimate relationships between words and actions. We use established definition to legitimize established courses of action, and etymology may come to our aid in situation of reluctance, discontent, or doubt, by fostering moral literacy and diversity and strengthening our fluency in multiple moral dialects.It has been argued that continental approaches are under-represented in contemporary bioethics. This paper presents the claim that a continental approach to bioethics notably entails fostering our sensitivity to the historical dimensions of moral language. I have argued that an etymological detour reveals (retrieves) the inherent complexities and ambiguities of our key bioethical signifiers. Etymology may be considered a bioethical “method,” albeit not in the sense of a strict protocol but rather as a path to follow. Etymology may be considered as a bioethical “argument,” albeit not in the propositional sense of the term but in the sense of


Words can change the way a patient thinks, feels, and performs. Swearing, or uttering a word that is considered taboo, is an often-ignored part of our language, even though over 50% of the population swears “sometimes” or “often”. If used correctly, within a biopsychosocial approach to care, swearing has the potential to significantly improve patient outcomes.Swearing can create tighter human bonds and enhance the therapeutic alliance between a patient and a physical therapist. Improvements in social pain, physical pain tolerance, and physical pain threshold can occur by strategic swearing by our patients. Even physical performance measures, such as power and force could be enhanced if patients swear.Although the mechanism by which swearing is effective is unclear, swearing deserves attention in the physical therapy setting based on evidence indicating potential positive effects on patient outcomes. We were first introduced to the quote, “words are, of course, the most powerful drug used by mankind”, in a previous Viewpoint, which discusses how the words used by physical therapists have the capacity to either heal or cause harm . Like drSwearing, or uttering a word that is considered taboo, is an often-ignored part of our language due to the controversial nature of the topic and the potential negative consequences of swearing. In 1901, Patrick  concludeSwearing may also be a sign of intelligence, is associated with less lying and deception at the individual level and higher integrity at the society level, and may be a sign of creativity . The offF-word is a well-recognized swear word considered “very severe” by 71% of the population [Humans have been swearing since the emergence of language  and is qpulation .Swearing in the physical therapy setting should be used to accomplish specific goals, such as relief from pain or stress. When swearing is based on biopsychosocial utility, it may add significant value if used correctly. Swearing tends to be more tolerated in private settings and among peers as opposed to a more formal and public setting. Swearing can lead to tighter human bonds and create informal environments where people are more likely to be themselves . Social Language used by the physical therapist and the patient can impact social, psychological, and biological factors, all of which heavily influence symptoms presentation and prognosis , 10. SocSwearing has also been found to decrease physical pain. Repeating a swear word while your hand is immerged in ice-water will allow you withstand the cold for 40 s longer, on average, compared to repeating a non-swear word . This icThere is evidence that swearing out loud can also increase physical performance. Uttering a swear word every three seconds for the entire 30-s Wingate Anaerobic Power Test allows you to exert greater levels of peak power and average power compared to repeating a non-swear word . EvidencSwearing can modulate physical and social pain, and increase physical performance; however, we don’t yet know the mechanism by which swearing works. Stephens et al.  did not S-word and F-word are the two most common swear words [F-word, but pain metrics did not improve when subjects repeated the made up swear words “fouch” or “twizpipe”.What swear word should be used to get these pain and physical performance improvements? It is advised to use a swear word that you would use in response to banging your head accidentally . If no car words , 9 and war words  discoverTo elicit the positive effects on pain and physical performance, without negative consequences, patients should not swear at the physical therapist. The research showing positive effects on pain and physical performance had their subjects swear out loud, not at a specific individual. Being the target of verbal aggression by asking a patient to swear at a clinician appears to lead to a high degree of distress among health care workers .Due to the potential negative effects of swearing, physical therapists should carefully determine which patients are likely to experience the greatest benefit from swearing, without risking negative consequences. The patients most likely to benefit from swearing are those who have strong rapport and therapeutic alliance with their clinician , those wPatients’ dysfunctions develop from a combination of biological, psychological, and social factors that may also influence how their dysfunctions progress and their prognosis. Swearing deserves attention in the physical therapy setting based on evidence indicating positive effects on physical pain, social pain, and physical performance Table . Swearin


Wellness Recovery Action Planning (WRAP) is a structured recovery education program used internationally for anyone who wishes to create a positive change in their life. It was developed by Mary Ellen Copeland and Jane Winterling in the late 90s, resulting from a search of programs or initiatives that could support their wellness. Since then, many studies have been conducted into the efficacy of the program for those with a mental health/addiction challenge. However, to date, there has been little to no synthesis of the evidence base for WRAP outside of the Copeland Center’s internal reviews. The proposed systematic review and meta-analysis aims to investigate the evidence base for Wellness Recovery Action Planning within a mental health context. To do this, a PRISMA 2020 compliant review is proposed using the PRISMA-P statement as a guide to demonstrate the methodology to be undertaken. It outlines the inclusion/exclusion criteria, search terms, and databases while informing the readership of timescales to complete such actions. Finally, this protocol also addresses issues of bias and quality within included studies. This proposed literature review and meta-analysis will synthesize and examine the evidence base for WRAP using a systematic review methodology. It is hoped that this proposed review will identify gaps in the current literature regarding the program, and provide recommendations that will support the reviewers in their further studies while also strengthening the argument for a whole system evaluation of WRAP within an Irish context. Mental illness accounts for over 10% of disorders worldwide . ProvidiA Vision for Change” [the quality or state of being in good health especially as an actively sought goal” [Recovery for those with mental health challenges has traditionally been seen as clinical in nature, resulting in the alleviation or elimination of disease or illness. It is now seen from an individual perspective, and there is an expectation that each person will have a recovery journey that is unique to their experience . However Change” , mental  Change” , which i Change” . Recover Change” . This id Change” . Wellnesht goal” . A recovht goal” . One sucWRAP is an acronym that refers to a structured wellness program for individuals who wish to make positive changes in their life towards recovery, including their own self-defined goals. The program was conceptualized by Mary Ellen Copeland and Jane Winterling in 1997. To deal with increased demand for this program, the Copeland Center was created and became a governing body for WRAP in 2003 . The proWRAP has been well researched both on a national and international level ,18. HoweTo examine the utilization of WRAP in different cultures and health services internationally.To identify if an evaluation of WRAP delivery in Irish services is required, given the changes in services to recovery orientation.To identify gaps in the current literature base as it pertains to the delivery of WRAP.To make recommendations for further study and research with WRAP, particularly from an Irish context.The proposed systematic review and meta-analysis aims to examine the evidence base for a recovery educational program: WRAP within the available, peer reviewed literature. To achieve this, the proposed systematic review and meta-analysis will aim to achieve the following objectives:This proposed systematic review and meta-analysis will utilize the newly updated Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) standardized reporting guidelines . HoweverAs this review seeks to understand the entire literature base for WRAP, no research question was developed. However, the PICO framework ,25 was uAs stipulated in The following search will be undertaken to explore the published peer reviewed literature into WRAP within a mental health context. The search will be undertaken using the following databases: CINAHL, JSTOR, OVID SP, PsycARTICLES, PsycINFO, PubMed, Science Direct, Web of Science, Wiley Online Library, and EBSCHO host. To ensure that all available peer reviewed documents are collected, a reference search will also be undertaken on included articles.The following search terms will be used in the proposed systematic review and meta-analysis: “wellness recovery action plan” OR “WRAP” OR “wellness recovery action planning” OR “wellness plans” OR “advanced directives” OR “crisis plans”AND“online” OR “internet” OR “in-person” OR “face-to-face”AND“experiences” OR “perceptions” OR “thoughts” OR “opinions” OR “views” OR “feelings” OR “judgements”AND“mental” OR “psychiatric” OR “mental illness” OR “psychiatric disorder” OR “mental ill health” OR “mental health” OR “mental wellness” OR “recovery” OR “rehabilitation.”This systematic review and meta-analysis will employ three rounds of searching. Round one will focus on the title of papers. Any papers that have WRAP or any of its synonyms (see above) in its title will be extracted and saved in the round one folder. Once this is complete, a second round will occur. This comprises two processes. Firstly, duplicates will be removed. After which, the abstracts of saved papers will be read, and using the above inclusion/exclusion criteria , papers Author’s surnameYear of publicationCountryAim of studyStudy design—qualitative, quantitative or mixed methodMethodological orientation—if knownData collection methodsSample and sample sizeStudy findings/resultsTheoretical framework—if knownThis part of the process will begin on the 1st February 2022. Round three searching is expected to be completed by the 31st March 2022. After which, relevant information from included papers will be extracted and charted in a comparative table to demonstrate critical thinking. The following information will be extracted for this comparative appraisal table: This process is expected to take the first half of April 2022 to complete (1–15 April 2022). Both authors will be involved in creating the comparative appraisal table. Risk of bias within individual studies will also be assessed as part of the quality appraisal process. Bias will be assessed under the following headings: performance, selection, and attrition bias. To demonstrate the outcome of such assessments, McGuiness and Higgins  Robvis wAs part of this review, the quality of evidence will also be assessed. Here, variations of the Critical Appraisal Skills Program (CASP) Tool ,28,29 anThis review will employ Brawn and Clarke’s  thematicAs this proposed review is including quantitative and mixed method studies exploring WRAP, a meta-analysis may be necessary, dependent on the number of such studies in the final inclusion list. If such data are included after the screening process is complete, a meta-analysis will be conducted on the data using a forest plot approach, as stipulated by Neyeloff and colleagues . A meta-This systematic review and meta-analysis protocol did not involve individual patients/service users or public agencies.This proposed systematic review and meta-analysis will use the PRISMA 2020 statement to guide the review in examining the evidence base for the recovery education program, WRAP. In doing so it will also examine the use of the program in other cultures and mental health services internationally. This may be an important finding which could support the rationale for representatives of the Copeland Center to expand their current work remit to include cultures/services that do not utilize the program. In terms of an evaluation in an Irish context, the reviewers are aware of two evaluations that have occurred previously within an Irish context by Agnes Higgins and colleagues , and HorAs with all systematic reviews and meta-analysis, there are some strengths and weaknesses to this proposed review. In terms of strengths, this review is possibly the first to systematically synthesize all study types as they relate to WRAP. The review will utilize the most up-to-date PRISMA guidelines to guide and structure this systematic review and meta-analysis. It will use evidence-based tools, such as CASP and MMAT, to measure quality of all included papers, and Robvis to measure and visualize the potential biases of papers included in the review. Finally, the review will also focus on papers from the last 10 years. Though this is good practice in terms of evidence-based research, it is also important to ensure that we collect data from sources who kept to the fidelity of the program.There are also several limitations to this proposed review. Firstly, as this proposed paper will be a systematic review, it will not capture the pure breadth of literature available (peer reviewed and gray) on the program. Although included studies will undergo a screening process, there is still a possibility that the included papers may be: 1. Of poor quality; and 2. From only a select region, which may impact the generalizability and reliability of the review results.