dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcome measures Data were collected using a self-administered questionnaire that included questions about participants' sociodemographic characteristics, as well as a range of health and substance use data. The main outcome was a PTSD diagnosis, measured using the PTSD self-report checklist of 20 PTSD symptoms de ned in the DSM-5 (PCL-5)  (11). PCL-5 is a widely used self-administrated questionnaire to detect and evaluate a PTSD, with a validated French version  (12). Each item on this scale is rated on a ve-point Likert scale re ecting severity of a particular symptom from 0 (not at all) to 4 (extremely) during the past month, with a threshold score of 33. Other outcomes included: the possibility of reaching women by phone in the 6 months following the inclusion visit, the level of self-rated global health (Likert scale: "Very good", "Good", "Quite good", "Bad", "Very Bad"), the number of emergency visits in the past 6 months, the substances use: smoking status, alcohol (evaluated by the Alcohol Use Disorders Identi cation Test-Consumption/AUDIT-C (13)), drugs ("Did you use hypnotics, sleep pills, antidepressants or anxiolytics in the past 6 months ?"), the readiness to change, the safety behaviors (evaluated by questions inspired by the Safety behavior Checklist (  14 )) and the help seeking behaviors in the past 6 months (evaluated by questions inspired by Van Parys et al.
Analysis Quantitative data analysis was conducted using SAS for Windows (version 9. 4). To describe the sociodemographic characteristics, perceived social support, health and substance use indicators descriptive statistics was used consisting of frequency, percentage, and mean and standard deviation. The qualitative data were analysed with NVivo V12 software using thematic content analysis. The transcribed text was coded, then the codes were sorted into categories and main themes, and illustrated using verbatim quotations.
The glasses were also equipped with a MarkerDriveBox connected to four active markers. The 3D translations and rotations of the ball and the glasses were measured by two optoelectronic units (Codamotion Cx1, Charnwood Dynamics Ltd, UK) which sent the positions and rotations, to the host computer running an OpenGL real-time 3D engine  (Goulon, 2019). The software extrapolated the ball trajectory in real-time, updated the virtual scene according to the player's point of view, and rendered the trajectory of a virtual ball on the screen when the ball passed behind the screen. Once thrown, the ball landed into a net on the other side of the screen and was collected by the experimenters (Figure  1 ).
Scales of analysis and Dependent variables The first scale of analysis of shooting performance exanimated the interaction between the virtual basket and ball by computing the players' %success-rate in VR. The second scale focused on the ball-player system through the measurement of the ball's speed and angle at the moment of release that were extracted from the virtual ball trajectories provided by the Codamotion system. Ball release was identified as the maximum speed reached by the ball before its apex. The third scale scrutinized players' body kinematics. Each participant was instrumented with 41 reflective markers (Qualisys Sports Marker Set, Qualisys AB, Sweden). The 3D position of each marker was simultaneously collected at 179 Hz using an eight-camera Oqus 5 optical motion capture system (Qualisys AB, Sweden) in a specific acquisition software (Qualisys Track Manager 2019, Qualisys AB, Sweden).
The start and stop of the shooting movements were identified a posteriori from 3D kinematics, when players raised the ball for the first time by flexing their elbow and at the ball release, respectively. From this time series, we computed the duration of the shooting movement. Then we screened each throw to categorize the type of shooting movement: free-throw when the participant's feet remained on the ground, and jump-shot when they jumped during their shots. Finally, we used a 3D motion analysis package (Visual3D v6, C-Motion, USA) to compute wrist, elbow, shoulder, trunk, knee, and ankle joint angles, as well as release height and elbow and shoulder angular velocities at ball release.
Statistical analysis For each dependent variable, the values obtained from the three shooting repetitions at each basket distance in the simulator were averaged. The influence of basket distance, expertise, and gender were analyzed using three-way analysis of covariance (MANCOVA). Release height was set as the covariate  (Miller and Bartlett, 1996; Hudson, 1985). To explore whether there is a relationship between type of throwing (free-throw vs. jump-shot) and basket distance  (3. 225, 4. 225, and 5. 225 m) , expertise (experienced vs. novice), and gender (male vs. female), we used Pearson's chi-square test. The level of significance was set to 0. 05, and SPSS Statistics 27 (IBM, USA) was used for all statistical analyses. Normality and homogeneity of variance were checked, and in the case of abnormal distribution and non-homogeneity, alternative statistics were applied.
Subject preparation and careful placement of markers are other issues that need attention. Finally, smart choices can be made for the measurement of certain kinematics dependent variables. For example, foot switches are more easily implemented than measuring ankle angle to distinguish between free-throw from jump-shots. Regarding the scenarios, just the basic use of such basketball shooting simulator is reliable enough to allow coaches to track the progress of players during injury recovery and to compare them to a database to certify their level during the off-season. Other more complex scenarios can also allow detecting the perceptive expertise of players more precisely. In the same way that contextual information was neutralized using VR in this paper, other manipulations are possible including spatial  (de Oliveira et al.
Acknowledgments, ethics and integrity statement This work was supported by the  "Investissements d'avenir" French Government Programme (Intermove, A*MIDEX)  under Grant number  ANR-11-IDEX-0001-02 ;  Carnot Institute "STAR" under Grant Cybershoot  (2017); and  Défi Instrumentation aux Limites program  of the CNRS under Grant Virtushoot (2017). The first author also received funding from  Collège de France  (PAUSE program 2018). We thank  Cedric Goulon  (ICE),  Michael Weisz  (Spalding), and  Technosport AMU. The authors report there are no competing interests to declare. The participants were informed about the experimental procedures but not about the purpose of the study. They provided written informed consent forms in accordance with the Declaration of Helsinki before the experiment. The study protocol was approved by the local ethics committee.
Ability is clearly associated with a country's resources to have an impact. Thus, the observed correlation is a valid result, on average across countries, and it should not be a surprise that GDP accounts for some of the shared variance between SoMi and EPI. Furthermore, we should note that EPI also accounts for shared variance between SoMi and GDP, and we see no strong reason to suggest that GDP should be considered a more proximal predictor of SoMi than EPI. Second, our cross-national study included 31 countries (2). We agree that the sample is not very large. But leaving out the data of three countries for no special reason, other than that they are influential, is not convincing. In our view, it is good scientific practice to consider each data point as valuable and informative. Moreover, one could also arbitrarily remove three other countries and, in doing so, strengthen the association in the remaining data.
COVID-19 health numeracy COVID-19 health numeracy measured understanding of COVID-19 concepts and statistics using a custom-made questionnaire composed of 18 multiple-choice items. Items covered three concepts important to understanding COVID-19-related data in the media: graph literacy, proportions and ratios. Fictional data were used in all sections in order to avoid bias due to prior COVID-19 knowledge. All items were multiple-choice format and were scored correct (1) or incorrect (0). Therefore, a higher score indicates high understanding of COVID-19-related concepts and statistics, whereas a lower score indicates poor understanding of COVID-19-related concepts and statistics. Nine items measured graph literacy. Of these, five items were conceptually modelled after the graph literacy scale proposed by Galesic & Garcia-Retamero  [34].
Individuals were shown a graph representing the total number of confirmed COVID-19 cases over time for two fictional countries, one with linear growth and the other with exponential growth. Four of these items had one of three difficulty levels related to graph literacy. The first difficulty level consisted of retrieving information, such as data points, directly from the graph (one item). The second difficulty level consisted of evaluating relationships between data points, for example, by finding where two curves intersect each other (two items). The third level consisted of making inferences from the information given by the graph, without the information being directly observable in the graph (one item). Also, one item measured explicit knowledge of linear and exponential functions. The last four graph literacy items tested individuals' knowledge of 'flattening the curve' and were accompanied by a diagram showing curves for the number of cases over time, with and without social distancing.
g. 'Which country has conducted the most COVID-19 tests?'; three items). The second difficulty level assessed the ability to obtain relevant proportions from the data in order to accurately compare country statistics (e. g. 'Which country has the highest fatality rate for COVID-19?'; three items). Finally, three items measured participants' ability to compare risk ratios. A situation describing fictional fatality rates for COVID-19 alone as well as for two other hypothetical risk factors were presented using risk ratios. Items involved comparing the odds ratios associated with the different risk factors and calculating relative risk. Similar to items in the basic numeracy section, COVID-19 health numeracy items were presented pseudo-randomly. In other words, items within a subsection (e. g.
Therefore, for rating-scale questions, identified outliers were examined for obvious errors or participant inattention. When no evidence for errors or inattention was found, the value was retained in the final dataset. For questions without a well-defined range (i. e. the number line questions in the basic numeracy section), identified outliers were examined for errors. If no errors were found, the outliers were treated as missing data. Finally, for the set of questions in the number line task, we calculated the median absolute deviation of each participant's responses and excluded participants if the median absolute deviation of their responses was below 0. 1 ( please see the electronic supplementary material, appendix A for more details). A total of 2124 participants (710 from US, 703 from Canada and 711 from the UK) were recruited for the current study.
In total, 93 participants were removed from the reported data analyses based on the outlier exclusion criteria. Specifically, two participants were removed based on the IP address criterion, one was removed based on the seriousness criterion, four were removed due to the missing data criterion, 27 were removed based on the median absolute deviation criterion, 54 were removed based on the vaccination criterion and two were removed due to the Mahalanobis distance criterion. The final sample consisted of 680 participants from the US, 679 participants from Canada and 675 from the UK.
Missing data As preregistered, we conducted a missing values analysis to identify patterns of missing data in the items. We anticipated two patterns that often occur in online research. The first consists of missing sporadic responses throughout the questionnaire, due either to inattention, or to participants not wanting to answer specific items. We analysed frequency of non-response for each item and identified items with higher non-response frequency. These items are not missing at random, and we determined whether they could be explained by covariates included in the current study, at which point, we included the covariates either in the model or as auxiliary variables that can be used to improve the estimation of missing data in full information maximum-likelihood (FIML). Total percentage missingness was calculated for each participant.
e. less motivation, having restricted amounts of uninterrupted time available to complete the survey, lower overall math skills). We analysed this particular pattern by testing whether missingness at the later points was related to performance on earlier items. Regardless of the in-depth missingness analyses, FIML  [46]  is still the best approach because no cases needed to be left out except when careless responding was identified. To assess attrition, per cent missingness was calculated for each of the three main sections of the main study and each were regressed on performance in the basic numeracy and COVID-19 health numeracy tasks. Results indicated that per cent missingness was significantly related with performance in both basic numeracy and COVID-19 health numeracy. As such, missing data were processed using FIML  [46].
Main analyses After ensuring data quality by implementing our participant and outlier exclusions, we pursued the research questions using a structural equation modelling approach. Basic numeracy, COVID-19 health numeracy and COVID-19 health-related attitudes and behaviours were operationalized as latent variables each with three parcels as indicators. Parcels were treated as continuous variables. All analyses were carried out using Mplus 8. 3 using the maximum-likelihood estimator with robust standard errors  [46]. Where applicable, goodness of fit of the models was assessed with the χ 2 test statistic, the comparative fit index (CFI), the Tucker-Lewis index (TLI) and the root mean square error of approximation (RMSEA). Typical cut-off scores for excellent and adequate fit are CFI and TLI > 0.
Given these findings and those of the current study, there does not appear to be a robust and strong relation between numeracy and health-related attitudes and behaviours pertaining to COVID-19. A number of factors can help explain the lack of association between basic and health numeracy and COVID-19 health-related attitudes and behaviour. It is possible that there was insufficient variability in the attitudes and behaviour measure due to a ceiling effect, which made it difficult to find associations between this measure and the numeracy measures. Data collection for the current study began in December 2020. By that time, public health advice had been established for many months and adherence may have been less dependent on people's estimation of the severity of the situation and more dependent on established local norms.
There is some data already to suggest that political worldview, such as prosocial tendencies and individualistic values  [62]  or even political party affiliation  [63]  are strongly related to COVID-19 attitudes and behaviours. Therefore, as the cultural cognition thesis suggests, high numeracy and scientific dissemination about the risks and spread of disease alone may not be enough to influence a population's health-related attitudes and behaviours. Directly addressing cultural values that influence cultural cognition may be a more effective strategy for influencing health behaviours in combination with improving health numeracy. Similar strategies have been suggested for influencing perspectives and behaviours related to climate change where public outreach would target both science literacy and cultural factors  [64].
Nevertheless, future studies would be required to assess construct validity, such as examining our measures with other established measures of numeracy and health numeracy. Overall, the current study suggests that basic numeracy is highly related to one's understanding of data about COVID-19. However, one's level of understanding of the numerical information about the disease does not predict their attitudes about its severity or their likelihood of following public health advice. Therefore, while numeracy appears to be a necessary part of understanding data about COVID-19, numeracy alone may not be sufficient for predicting public opinion or adoption of recommended health practices. Ethics. The study was approved by the non-medical research ethics board at the University of Western Ontario and was conducted according to their guidelines.
Funding. This research was supported by funding from an  Advanced Research Fellowship  from the  Klaus J. Jacobs Foundation  to D. A. at  Western University ; by the  U. S. Department of Education Institute of Education Sciences  Grants  R305A160295  and  R305U200004  to C. T. at  Kent State University ;  NIH NICHD F32  Grant  HD102106  to A. D. R. ;  BrainsCAN Postdoctoral Fellowship  funded by the  Canada First Research Excellence Fund (CFREF)  to M. S. ;  CAPES (Doc-Pleno ,  88881. 128282/2016-01 ) to I. S. A. ; Post-doctoral fellowship  PDM/20/057  to E. B. and a  Banting Postdoctoral Fellowship (NSERC) and BrainsCAN Postdoctoral Fellowship  at  Western University , funded by the  Canada First Research Excellence Fund (CFREF)  to E. D. W.
Participants. Participants were recruited in a French University. All registered students received an email giving them the opportunity to participate in an online survey about 'Personality in university students' , created on LimeSurvey. To participate, the only criteria were to be a student, aged between 18 and 28 years and to be a native French-speaker. All participants gave electronic informed consent, and the study was approved by the University review board (UNISTRA/CER/2020-13) and the French data protection authority (CNIL). They weren't paid for their participation. In total, for study 1, 3144 students (26. 5% male) completed the survey. After eliminating 355 respondents who did not match the criteria, 2789 students constituted the study sample. For study 2, 1678 new first-year students (38.
Online survey. For Study 1, the survey was divided in four parts: sociodemographic characteristics, education, general functioning, and psychopathology (see Supplementary data 2 online). All these factors were assessed with qualitative measures. Four validated scales were added to complete the assessment of psychopathology. The names and themes of the four questionnaires were not specified online. (1) The Dimensional Apathy Scale (DAS)  47   For study 2, the survey was divided into two parts: sociodemographic characteristics and psychopathology. Two validated questionnaires were used to assess the psychopathology: the DAS  47  and the BDI-II (Beck et al. 52  ). See Supplementary data 3 online for correlations between all the questionnaires for both studies.
Statistical analysis. Each participant had three DAS subscores: one for each form of apathy. To determine the percentage of participants with a subclinical apathy, a cut-off was calculated for each subscore, based on the mean and the standard deviation of studies samples (Study 1, 2789 students, Study 2, 1678 first-year students). The suggested cut-offs were + 1 standard deviation from the mean for subclinical apathy/ apathetic trait and + 2 standard deviation from the mean for clinical apathy / apathetic symptom  11, 39, 48. Both studies had large sample size, so according to the Central Limit Theorem, data could be approximated by a normal distribution and parametric tests were used  49. Pearson correlation tests were implemented between DAS scores. To identify variables correlating with each form of apathy, Pearson correlation tests were implemented with continuous variables; ANOVAs and Bonferroni post-hoc tests were used with categorical variables.
Sample and Procedure Study participants were recruited via personal and professional contacts, online platforms and the first author's university's press department and website. In the course of the survey, participants were asked to indicate the e-mail address of either their direct leader or one of their followers. These dyadic partners were then automatically invited to take part in the survey. We stressed anonymity and confidential treatment of the data in order to minimize concerns about the dyadic partners having insight into the data. Overall, 164 leaders and 192 followers completed the questionnaire. After matching the leaders and followers, the final sample consisted of 123 dyads because some participants could not be matched. A total of 35% of the leaders and 61% of the followers were women.
20, SE = 0. 05, p = 0. 026; see Online Appendix A), which renders further support for Hypothesis 1. To test the moderated mediation posited in Hypothesis 2, we used the PROCESS macro for SPSS  (Hayes, 2018). The results revealed that followers' supervisor-directed deviance did not moderate the indirect effect of leaders' narcissistic rivalry on abusive supervision via perceived self-esteem threat (index of moderated mediation: B = 0. 02, SE = 0. 05, 95% CI [-0. 07, 0. 12]). This means that leaders' narcissistic rivalry was unrelated to perceived self-esteem threat, irrespective of followers' supervisor-directed deviance, and perceived self-esteem threat was unrelated to followers' ratings of abusive supervision. The direct effect from leaders' narcissistic rivalry on abusive supervision was still evident here (B = 0.
41, SE = 0. 05, p < 0. 001). In contrast, leaders' narcissistic admiration was unrelated to abusive supervision intentions (β = -0. 02, SE = 0. 05, p = 0. 760). Results are presented in Table  3  (see model 1). Without leaders' narcissistic admiration as covariate, we also found a direct positive association between leaders' narcissistic rivalry and abusive supervision intentions (β = 0. 40, SE = 0. 05, p < 0. 001; see Online Appendix A), which further supports Hypothesis 1. To test Hypothesis 2, we also used the PROCESS macro for SPSS  (Hayes, 2018). There was no conditional indirect effect of leaders' narcissistic rivalry on abusive supervision intentions (index of moderated mediation: B = 0. 03, SE = 0. 04, 95% CI  [-0. 04, 10] ). However, the indirect effect of leaders' narcissistic rivalry on abusive supervision intentions via perceived self-esteem threat was significant in both experimental conditions (low supervisor-directed deviance: B = 0.
Acknowledgments I would like to thank  Mikael Flatot ,  Tom Markarian , and  Thibaut Piquemal  for their help in data collection and analyses. Correspondence about this paper should be directed to  Patrick Lemaire ,  Aix-Marseille University & CNRS , 3  Place Victor Hugo ,  Case D, 13331 Marseille , France (email: patrick. lemaire@univamu. fr). This research was supported by a grant from the  Agence Nationale de la Recherche : [Grant Number  ANR # 20-ASTC-0031 ].
Analysis and data preparation We used a one-stage Meta-Analytic Structural Equation Modeling (MASEM;  Jak, 2015; Jak et al. , 2021). This method is a random-effects technique and combines the methods of meta-analysis and SEM to fit a path model from a set of correlation matrices. This analysis also allows for the test of a moderator which, in our case, was group status. We conducted the analysis in R, using the metaSEM package (Cheung, 2015). The one-stage MASEM was based on eight correlation matrices (one for Studies 1 and 2, and two, one per group, for Studies 3a, 3b and 4). For Study 2, Spearman correlations were calculated for correlations involving support for progressive and reactionary movements, treating these variables as ordinal variables with three increasing modalities (no support, support, participation).