dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods This study analyzes data from the CONSTANCES cohort, a generalpurpose epidemiologic cohort designed to study a wide range of health problems in the general population. CONSTANCES collects data on personal, behavioral, occupational, and social factors from selfadministered questionnaires at inclusion and health examinations. Other information, such as reimbursement data (visits to doctors and other health professionals, reimbursement of the cost of care), is collected from databases of the French NHI. Inclusion in this cohort began in 2012 with the ultimate objective of including 200,000 cohort members (men and women) from the French population, aged 18 to 69 years. The random selection of the cohort is stratified for age, sex, region, and socioeconomic position  (Goldberg et al. , 2017).
The data Rates of underscreening differ significantly between the three groups < 1% level in Chi-2 test. The interaction test compares the interaction between the migration status variable in three categories and the monthly income variable in 5 categories. This analysis covers the 90,023 women recruited from January 2012 through December 2019. Finally, 17,940 were not eligible for Pap tests (13,755 women younger than 27 years or older than 65, 3,196 with a history of uterine cancer or hysterectomy, 985 with no sexual relations ever, and 4 seropositive for HIV). To ensure that the first-generation immigrants in this sample had been residing in France for at least 3 years, we excluded the 93 women who had not been reimbursed for any medical services during the 3 years before inclusion as well as 1,376 participants whose migrant status was unknown.
Stratified SII scores were compared by interaction tests (see appendix for model details). The missing data for income (6. 0%) were taken into account by fully conditional specification methods, with the MICE package in R (Buuren and Groothuis-Oudshoorn, 2011). The imputation model includes all variables associated with the variables to be imputed, including age, reported coverage by specific government-funded medical insurance for very low-income individuals from 2009 to 2016, overall perceived health condition (rated from 1 to 8), living with a partner, living with a child, and perceiving one's daily living financial situation as difficult. Five complete datasets were created. R software version 4. 0. 4 was used to conduct all analyses in 2023.
Acknowledgments The article was supported by the ' Institut de recherche en Santé publique' (IReSP)  located at  8 rue de la Croix Jarry 75013, Paris, France. The authors thank the " Epidemiological Population-Based Cohorts Unit" (UMS 11 ) of  Inserm-Versailles Saint Quentin en Yvelines University , whose researchers designed and are responsible for the Constances Cohort Study.
, 2018). This paper makes a significant contribution to the Special Issue by studying the mechanism underpinning training, which results not just in clinical competence but also confidence and mental resilience  (Bonsall, 2020; Laskowski-Jones, 2019) , particularly in a time of covid-19 pandemic when many communities worldwide have been deemed unprepared for the unanticipated turmoil resulting from such a possibly black swan event  (Cobianchi, Dal Mas, Peloso, Pugliese, Massaro, Bagnoli and Angelos, 2020a; Taleb, 2008)  and nurses have been hailed as heroes. This could be by addressing the following research question: What is the effect of clinical experience on the clinical confidence of undergraduate nursing students? Using data collected in the Australian and United Kingdom contexts, the study aimed to investigate the effect of mental health clinical experience on the clinical confidence of undergraduate nursing students.
Data collection and analysis procedures The study was done in two parts. The first part consisted of survey data collected from nursing students during the lectures and tutorials. The second part consisted of interviews with ten health professionals, including nurse teachers and doctors. For the survey, two days prior to the commencement of their clinical placement, the researchers collected the pre-test data. Participation in the study was voluntary, and that they were free to withdraw from the study at any time and that participation in the study would not influence their assessment in their course. The students who volunteered to participate were given an information sheet and a consent form to sign. Participants completed the first self-report demographic profile and the MHNCCS questionnaire in a classroom setting, taking 10-15 min to complete.
Only 54 students responded to the post-test. Therefore, a total sample comprising 54 participants completed both the pre-test and post-test self-report survey questionnaires. For the interview part, the research followed three key steps. We first conducted the semi-structured interviews and transcribed them. Secondly, we examined the transcripts through thematic analysis to derive the main topics from the discussions  (Guest et al. , 2012). The third step involved the interviews being coded manually given the small number of interviews. The researchers used axial coding to re-organise the data by establishing possible connections between the earlier identified themes. Finally, the qualitative data analysis was done on a case-by-case basis to bring to light the common aspects of the participants' technological knowledge and advantages for nursing practice.
To ensure confidentiality, we blanked the participants' identities. Table  1  shows details of the participants. The statistical analysis was performed using SPSS. The MHNCCS scores were compared in relation to demographic variables (age, gender), enrolled nurse status and work experience in healthcare facilities. Paired-samples t-test was used to compare pre-test and post-test variables within groups, and an independent t-test was used to compare the variables between groups. In addition, we used One-way Analysis of Variance (ANOVA) to compare pre-test and post-test confidence scores between different age groups and work experience.
Quantitative analysis Of the 77 participants, 18 did not respond to the post-test questionnaire resulting in 54 pre and post-test matched cases responding to demographics and the MHNCCS. Data from 54 participants who completed the pre-test and post-test self-report questionnaires were subjected to component analysis. The pre and post confidence scores were generated using the mean of the 20 items, with the mean based only on items that had a response. This standardised the result for pretest and post-test confidence scores. Most students were females (87%) of the sample. The average age of participants in the study was 25 years (SD = 7), with an age range of 19-46 years. Thus, four distinctive age groups were established for the analysis of data in the study. The demographic data revealed that students who participated in the study were employed as casual or parttime workers in various healthcare settings (Fig.
Selection process The librarian searched each database and collected articles according to the predefined search strategy, using Zotero. The results were uploaded to the Rayyan website for the screening process. Following this first phase, the two independent examiners (AR and AVB) conducted the first screening to identify appropriate manuscripts based on the eligibility criteria. Each reviewer was blind to the other reviewer's decisions ("Blind on" function in Rayyan). In the first stage, manuscript titles were screened according to the eligibility criteria. In the second step, the abstracts were reviewed to identify eligible sources. In the third step, the full texts of articles that appeared to meet the inclusion criteria were read. In case of disagreements between the reviewers (AVB and AR), a third author (ND) independently reviewed the work and discussed the decision with the other authors (AVB and AR).
Data extraction Two examiners (AVB and AR) performed the data extraction independently using a standardized table and classifying the data according A. -V. Bruyneel et al. to the following sections: primary author, year of publication, country, sample size, characteristics of the participants, study objective, setting, description of the single task, description of the dual-task, phone type, phone usage habits, gait assessment tool, outcomes, key findings, limitations, task prioritization. If there were disagreements, a third reviewer (ND) made the decision. For the quantitative synthesis, data (mean or median, standard deviation (SD) or minimum, maximum, quartiles) were collected from the results sections and tables of the manuscripts. If necessary, the Web-PlotDigitizer program (version 4.
Synthesis method The data were quantitative, and an aggregative synthesis was appropriate for assessing the influence of dual-task conditions (texting + walking) versus single-task conditions (walking). For walking speed (main outcome), a meta-analysis was performed for all included studies, except for articles lacking data (e. g. , mean and SD). After this first synthesis, a subgroups synthesis was realized based on age, walking tasks, and spatiotemporal parameters, only when two or more studies were included in the subgroup. Two analyses were performed: for all studies regardless of the quality of appraisal and for those with a Downs and Black's quality score greater than or equal to 9. Means and SD of single task and dual-task conditions and the sample size of each study were extracted to perform the meta-analysis.
Following Tufanaru et al. [27] , the standardized mean difference (SMD) and 95 % confidence intervals (CI) were calculated. The SMD expresses the intervention effect in standard units rather than the original units of measurement. A SMD value of 0. 2 represents a small effect, 0. 5 a moderate effect, and 0. 8 a large effect  [28, 29]. Heterogeneity between studies was assessed using I 2 tests. A fixed-effect model was applied in two cases: 1) if the I 2 values were ≤ 50 % (data were considered homogeneous); 2) if the number of studies included was less than five. A random-effect model was applied if the I 2 values were > 50 % (data were considered to have substantial heterogeneity). A p-value less than 0. 05 was considered statistically significant.
Four studies clearly reported an instruction according to task prioritization (texting or walking) in the procedure  [35, 38, 47, 53]. Prioritization instructions were not given in 16 studies and were not clearly reported in five studies (Table  3 ). The walking speed was measured in all studies, but two did not report the data  [39, 40]. The data was retrieved for one of these studies  [40]. Among the spatiotemporal gait parameters other than gait speed, the most frequently assessed were step or stride length (68 %, n = 17), cadence (52 %, n = 13), double support time (32 %, n = 8), and single support time (14 %, n = 3). Compared with normal gait, the walking speed was decreased by a mean of 0. 23 ± 0. 12 m/s (young healthy: 0. 19 ± 0. 08 m/s; older: 0. 42 ± 0. 11 m/s) when texting. The texting task significantly altered other spatiotemporal parameters (Table  3 ): a decrease in step and stride length, a decrease cadence, a decrease in single support time, and an increase in double support duration.
Meta-analysis Twenty-two of the 25 studies were included in the meta-analysis. One study was excluded because the values relative to the single task were not reported  [39]. Two studies were excluded because they did not report the SD value of spatiotemporal parameters  [6, 43]. Results for the two analyses (regardless the quality of appraisaln = 22, and then only the studies with a Downs and Black's quality score greater than or equal to 9 -n = 17) are given in Table  5. For the walking speed, the datasets were analyzed for all studies (n = 22) and separately for three subgroups: under 60, over 60, and multiple sclerosis. The only study involving individuals with obesity was excluded from the subgroups analysis  [34]. In the case of young subjects (< 60 years), the datasets were analyzed for normal gait (laboratory and ecological conditions) and gait with obstacles.
Such module will categorize help-seekers versus non-seekers and ultimately inform the design of personalized treatment recommendations. For this purpose, data from a large number of questionnaires and tests related to different psychological-and hearing-related individual characteristics, together with multiple assessments of a hearing screening test were collected in a mobile study that simulated a VHC. To target potential users of a future VHC, the study was addressed to individuals with subjectively perceived hearing difficulties who had not yet been compensated with HA. We used machine learning algorithms to predict readiness to seek professional help, as assessed at the end of the study and after two months. The following research questions guided our study design and analyses:.
Study overview We used the potential of Ecological Momentary Assessment (EMA, (  16 )), a measurement approach to collect data in the everyday environment of the individual ("Ecological") and to assess recent or immediate experiences and states ("Momentary"). researchers and as a team, are committed to and supporting Open Science practices. We have therefore published a preprint of the manuscript on medrxiv. org in February 2023 and share data analysis scripts via Zenodo (https://doi. org/10. 5281/zenodo. 7635920). The data will be shared with interested researchers upon request, since the dataset will be used for further projects and cannot be shared at the current time point.
Participants Older adults above 50 years of age were recruited between September 2021 and August 2022 through the online platform Ebay's minijob announcements, the university intranet and via mailing list services of several German universities' Guest-Audience and Senior-University programs. Inclusions criteria were: subjective reports of hearing difficulties in daily life, ownership of and the ability to use a smartphone, and good command of the German language. The exclusion criterion was the use of hearing aids. Prior to enrolment, participants provided written informed consent. The final dataset included N1 = 185 participants, 106 female and 79 male (0 diverse), aged between 47 and 82 years, with Mage = 63. 1 and SDage = 6. 5 (see Figure  1 ). One participant was below 50 years of age  (47)  but was nevertheless included in the final sample, given that this value only slightly deviated from the planned age threshold.
A descriptive summary of participants' socio-demographic characteristics is provided in Table  1. between platforms in a consistent format and in the respect of privacy and security. With the individualized link received via SMS, participants could perform the study on their own smartphone's browser. Seven participants performed the study on their computers since they experienced technical difficulties with their smartphones. During baseline assessment, cross-sectional data from a comprehensive set of 25 questionnaires and tests was collected. The questionnaires and tests were distributed on five consecutive days in order to maximise study compliance and avoid priming effects on different questionnaires. For a detailed list of all assessment tools and their references we refer to the Supplementary Material 1 (Features and assessment tools).
Machine learning The data was fed into three machine learning algorithms for supervised classification along the outcome labels action versus no-action. We chose Naïve Bayes (NB), Random Forests (RF) and Support Vector Machines (SVM) among other classifiers to cover a wide range of model complexity (from simple models like NB, to more complex and non-linear ones like SVM). We used Cross-Validation (CV) to train and subsequently test the model. The algorithms have been implemented on R using the mlr package (29), following the approaches described in  (30)  and  (31). Given the presence of three different outcome measures, the same analysis steps were carried out in parallel for each outcome, with a slight difference in the input features included in the analysis. For the first outcome (action to seek help), data on motivation at pre-study was kept in the features space, while motivation to seek help at the end of the study was removed.
However, class imbalance in the sample can negatively impact prediction accuracy, reducing its informativeness as performance measure  (35, 36). Matthews Correlation Coefficient (MCC) (yardstick package  (38) ) was additionally taken into account to evaluate model performance. This coefficient improves over accuracy measures in case of imbalanced datasets  (36, 38)  and can take values from -1 to 1, with 1 indicating perfect prediction, 0 chance prediction and -1 inverse prediction. In addition we computed the confusion matrix (calculateConfusionMatrix(), mlr package). Its output provides the absolute number and the proportion of correct model predictions and misclassifications for each outcome class. For binary outcomes, the confusion matrix allows to estimate model sensitivity (i. e. accurately identifying seekers) and specificity (i.
Classification performance Predicting the action to seek help. A summary of the model-specific classification accuracy for the first outcome (action to seek help) is provided in Table  3. The three algorithms show similar overall performance accuracy estimates on the test set, correctly classifying about 67% to 70% of the cases in the full dataset (N1 = 185). RF was the best performing algorithm with an accuracy of 70. 3% and an MCC =. 282, indicating that the model's prediction improves to about 30% over chance. By inspecting the confusion matrix, we observed that RF shows high specificity, correctly classifying 91% of the cases belonging to the no-action class. The NB classifier (10-fold CV repeated 50 times) showed the best sensitivity compared with the competing algorithms, with 51% cases in the action class being correctly classified.
Feature importance Feature importance was analysed based on the RF and NB algorithms predicting the first outcome, action to seek help at study-end on the full data set (N1 = 185). For both models, features were first ranked in decreasing order according to their importance values. Three sets of features among the most important ones were taken into account for subsequent analysis:  the top-10 features indicated by the two models, resulted in a total of 14 best features;  the top-15 features indicated by the two models, resulted in a total of 19 best features;  the top-20 features indicated by the two models, resulted in a total of 28 best features. Figure  2  shows all 28 features with their importance ranking values originating from the RF and NB models. Next, the three sets of features were evaluated for their predictive performance and classification accuracy on the reduced dataset of N2 = 131 participants who completed the follow-up questionnaire.
Discussion The present study contributes to the identification of individuals' hearing-related and psychological features that predict the readiness to seek professional help for hearing loss (HL). Cross-sectional and longitudinal data have been collected in a large ecological momentary assessment study. Potential users of a future Virtual Hearing Clinic (VHC), namely individuals with subjective hearing difficulties, were classified into help-seekers and non-seekers by means of supervised machine learning algorithms. Feature importance analyses helped to derive relevant individual characteristics to be used in a prospective psychological profiling algorithm of help seeking and support the design of a targeted recommendations module in a VHC.
Which machine learning model can best predict help-seeking? Three machine learning classifiers correctly predicted action to seek help at study-end in 66 to 70% of cases, clearly improving over chance prediction. This is a promising result considering the complexity of the prediction outcome. As discussed earlier, several individual factors can influence the decision to pursue hearing health care services and there can be discrepancy between the intention and the upcoming concrete action. When predicting action to seek help at follow-up with the selected important features, the performance of random forest and naïve bayes models improved up to 75%, despite the smaller dataset (N2 = 131). Measuring help-seeking two months after the study-end provided a more valid measure of the participants' behaviour.
For example, among the participants who were categorized as non-seekers at studyend, seven reported at follow-up to have completed an appointment with a hearing professional and 12 were willing to do so in the near future. A more precise and objective outcome measure, along with a larger dataset will contribute to further increase classification accuracy. In this direction, future studies could benefit from the adoption of a theoretical model of health-related behaviour change  (48, 49). A theoretical conceptualization of the different stages and factors that lead to behaviour change will help in defining precise outcome measures. Another factor that might have limited overall classification performance is the type of predictive features included, which were mostly self-reports rather than objectively measured traits.
Acknowledgements: We thank all the participants who took part in this study and the Universities that helped us with recruitment:  University of Oldenburg , Gasthörstudium;  Goethe-Universität Frankfurt am Main ,  Universität des 3. Lebensalters;  Freie Universität Berlin ,  GasthörerCard Programm ;  University of Köln ,  Gasthörer-und Seniorenstudium ;  University of Kassel, Gasthörendenprogramm ;  University of Bielefeld , Studieren ab 50. Preliminary results of this research were presented on symposia and conferences of the "Hearing4All" Cluster of Excellence, on the VCCA Conference 2022 and the DGPs Conference 2022. A preprint of the manuscript was published on medrxiv. org in February 2023. Analyses scripts are available at https://doi. org/10. 5281/zenodo. 7635920. The data is available upon request to the corresponding authors.
The function varImp() from the caret package (41)  was applied on the model trained with the function train() (caret package), after ensuring comparable performance with the same model previously trained in the mlr package. Information on feature importance was used to identify which features are mostly relevant for telling apart individuals depending on help-seeking intentions. No statistical criterion exists to determine which importance value threshold should be used to retrieve relevant features. Hence, three threshold values were inspected (the first 10, 15 and 20 features in their importance ranking order) and evaluated in terms of predictive accuracy and interpretability. Classification accuracy of these three feature sets were assessed on the outcome data obtained at follow-up. For this analysis, the dataset was reduced to N2 = 131 participants who completed the follow-up questionnaire.
Results are based on the reduced follow-up dataset (N2=131). The table shows the results for three sets of features (10, 15 and 20 most important features for the two models). For NB 10-fold CV repeated 50 times was used. For RF the table additionally shows the hyperparameter search space used in the resampling procedure. (NB: Naïve Bayes classifier; RF: Random Forest; SVM: Support Vector Machine; MCC: Matthews Correlation Coefficient) Top-15 features (total 19) NB RF ntree mtry nodesize 800 [1,10] [1,5]. 73. 73. 43. 41. 58. 56. 83. 84 Top-20 features (total 28) NB RF ntree mtry nodesize 800 [1,5] [1,5]. 75. 70. 47. 36. 63. 52. 83. 82 Note:.