dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients sample To assess the psychometric properties of the revised French version of the PTGI (version 2), we used the data collected within the ELCCA cohort (ClinicalTrials. gov Identifier: NCT02893774)  [35]. ELCCA is a longitudinal study conducted in France on patients recently diagnosed with early-stage breast cancer or melanoma at the Nantes Cancerology Institute (for breast cancer patients) and the Department of Onco-Dermatology of Nantes University Hospital (for melanoma patients). It was approved by an ethical research committee ("Comité de Protection des Personnes"). The objective of ELCCA was to study socioeconomic, psychological and HRQoL changes following a breast cancer or a melanoma within the months and years following the cancer diagnosis. Breast cancer and melanoma were chosen as they have a similarly good prognosis when detected early.
Nevertheless, health care is very different for these two cancer locations. On the one hand, breast cancer treatments are generally invasive with major surgery and therapies such as radio-, chemoand hormonal therapies. On the other hand, melanoma patients experience a minor surgery possibly followed by immunotherapy  [35]. Hence, the different nature of the treatments could interfere with the perceived severity of the disease, and by extension, with the changes that patients may experience. After the diagnosis, patients were informed about the study by an oncologist and were invited to sign an informed consent agreement. Participants were asked to complete a series of questionnaires at different measurement occasions: 1, 6, 12, 24, 48, and 60 months postdiagnosis. We chose to analyze the data from the 4th measurement occasion (i.
To withdraw the gender effect in the comparison of melanoma (both women and men) and breast cancer patients (only women), we restricted this score comparison to the data collected in women. All analyses were performed with Stata 16 (Stata Statistical Software: Release 16. College Station, TX: Stata-Corp LLC). The Stata module used to perform CLV is available from the Statistical Software Components archive  [51]. The French version of the PTGI evaluated in this manuscript (i. e. , the version 2 that derived from Lelorain et al. translation  [11] ) is available in the supplementary materials (see the Appendix B).
Sample characteristics At the 4th measurement occasion (i. e. , 2 years after their cancer diagnosis), 380 participants sent back the PTGI questionnaire. Among them, one patient with breast cancer only responded to one item, she was excluded from the analysis. Of the 379 remaining participants, 299 (79%) had breast cancer and 80 (21%) had melanoma. They were aged between 21 and 73 years, with an average age of 54. 8 (SD = 9. 1 years). All participants with breast cancer were women. Among melanoma patients, 32 were women (40%) and 48 were men (60%). Most of them lived in couple (81%). A total of 361 out of 379 (95%) individuals had a complete PTGI (i. e. , no missing data in their responses). Item-level missing data rates ranged from 0. 3 to 1. 8% (this latter rate being reached by item 18: "I have a stronger religious faith").
Limitations and perspectives Our study has several limitations that can be mentioned. First, inclusion criteria targeted only early-stage cancer patients from two hospital units (one per cancer site) data were collected 2 years after the cancer diagnosis for all patients. This may limit the generalizability of our results. Further validation studies across different cancer diagnoses, with various times since the onset of the disease, and in other French-speaking countries are needed to assess  the psychometric stability of the French version of the PTGI (i. e. , reproducibility under conditions of limited change). In addition, during Study 2, we performed the CLV analysis and the CFA on the same sample. A CFA using another independent sample should be carried out in the future to investigate structure stability.
Acknowledgments The authors would like to thank  Pr. Mario Campone  (the investigator of the ELCCA cohort), and  Dr. François Dravet  and  Pr. Gaëlle Quereux  who included the patients. The authors also sincerely thank the patients who participated in the study, the "Qualité de vie et cancer" platform, and  Sophie Lelorain  for the information she gave us about her translation.
This new test phase brought to light the lack of indicators to assess the session itself and the group dynamic, the use of the tools offered, etc. In the end, we obtained 48 indicators. In order to input these various indicators, we developed a data collection tool. This tool was tested during different sessions in order to be improved. The latest version of the tool, presented in this document, was tested over seven internal sessions, organised by the innovation laboratory in which we were immersed, and an external session, organised by another innovation laboratory which invited us to participate and test our tool during a co-creativity session.
Study Population: The NutriNet-Santé Cohort Individual data regarding physical activity, sedentary behaviors and other individual covariates were collected from participants of the NutriNet-Santé study, an ongoing web-based cohort launched in France in May 2009, which focuses on the study of relationships between nutrition and health. This cohort has been previously described in detail  [15]. Volunteers aged 18 years or older living in France and having access to the Internet fill in self-administered web-based questionnaires at baseline as well as at regular time points during follow-up using a dedicated website. Moreover, the participants also completed a set of questionnaires assessing demographic and socioeconomic characteristics  [16]. The study uses a secure and flexible online platform for recruitment and data collection (www.
Physical Activity and Sedentary Behaviors In April and in May 2020, a set of ad hoc questionnaires was sent to the NutriNet-Santé participants to collect extensive data on health status and health behaviors, including physical activity at the beginning (T1) and during the lockdown (T2). Similar questions were asked at the two time points (T1 and T2). During the first 15 days of lockdown (T1), i. e. , early April 2020, participants were asked about whether they had increased, decreased, or not modified their overall physical activity compared to their habitual level of physical activity before the lockdown. Detailed information was collected on different types of physical activity performed, asking if the activity under question had been performed earlier (and, if so, whether its duration had increased or decreased since the beginning of the lockdown) or if the activity had been started at the beginning of the lockdown.
Statistical Analyses A total of 32,409 participants completed the specific physical activity questionnaire sent during the COVID-19 lockdown at both time points (early April-T1 and early May 2020-T2). Among these, 23,558 participants had also completed the SAPRIS questionnaire (in April 2020) with covariables of interest for the present analyses. Data were summarized using numbers and percentages for categorical variables and mean values and standard deviations (or median values and interquartile ranges) for continuous variables. Data were categorized according to changes reported in overall physical activity at the beginning (T1) and later during lockdown (T2). Subjects were grouped into four categories according to changes in physical activity: (1) "Increased physical activity": those who reported an increase at both time points or who reported an increase and then stability, (2) "Decreased physical activity": those reporting a decrease at both time points or who reported a decrease and then stability, (3) "Stable": those who reported no change at both time points, and (4) "Others": those who had any other combination of increased/decreased/stable physical activity, for example who reported an increase at the first time point and then a decrease.
Strengths and Limitations Our study has several important strengths, including the large sample size allowing analyses stratified by gender, very detailed information on changes in specific types of physical activity as well as data on the reasons for subjects to increase or decrease physical activity behaviors during that unique time period. Some limitations should be mentioned. First, our study relies on self-reported data, and most questions used in the current analyses focused on the perceived changes during the lockdown, which may be prone to social desirability bias. The duration of reported physical activities was not assessed. Second, we did not consider information about physical activity and sedentary behaviors during the post-lockdown period that would help assess whether behaviors were (or were not) maintained over time.
For those who started activities during the lockdown, the challenge will be to maintain these activities over time, leading to the importance of post-lockdown studies. It will be interesting to further investigate the barriers to physical activity during (i) different levels (and time) of restriction in pandemic context at national and international level and (ii) during the period after the lockdown. Finally, data on relationships with depressive symptoms highlight the value of physical activity as an important marker of overall and mental health, especially in women, to be included in surveillance efforts and public health policies at national and international level. Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/ijerph191912370/s1, Table  S1.
Acknowledgments: The authors warmly thank all the volunteers of the NutriNet-Santé cohort for their continuous participation in the study and for participating in this COVID-19-specific project. The authors thank the  SAPRIS  project working group (management board:  Nathalie Bajos co-PI ,  Fabrice Carrat , co-PI,  Marie Zins ,  Gianluca Severi ,  Marie-Aline Charles ,  Pierre-Yves Ancel ,  Mathilde Touvier ). We thank  Cédric Agaesse  (manager),  Alexandre De-Sa  and  Rebecca Lutchia  (dietitians);  Thi Hong Van Duong ,  Régis Gatibelza ,  Jagatjit Mohinder  and  Aladi Timera  (computer scientists);  Nathalie Arnault ,  Julien Allegre ,  Laurent Bourhis , and  Nicolas Dechamp  (data-manager/statisticians);  Merveille Kouam  (health event validator); and  Maria Gomes  (participants' support) for their technical contribution to the  NutriNet-Santé study , for their dedication and engagement to collect and manage the data used for this study, and for ensuring continuing communication with the cohort participants.
V. ACKNOWLEDGEMENTS We thank  Nathan Klinedinst ,  Philippe Schlenker  and  Emmanuel Chemla  for their help in the design and preparation of this review. We also thank  Alexandra Bosshard ,  Pritty Patel-Grosz ,  Patrick Grosz  and  Dan Sperber  for insightful discussions. This research received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  788077 , Orisem, PI: Schlenker), the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ), and the  British Academy  for the  Humanities and Social Sciences  (grant number  PF170023 ). Research was conducted at  Département d'Etudes Cognitives, Ecole Normale Supérieure -PSL Research University , supported by the grant FrontCog  ANR-17-EURE-0017 , and at the  University of Exeter, Center for Ecology and Conservation (Penryn campus ).
Study Design We report findings from the cross-sectional baseline data obtained from the Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA), an accelerated longitudinal cohort from children and adolescents, in India between 2014 and 2016. The cohort profile and the study protocol have been published elsewhere  (Sharma et al. , 2020)  ,  (Zhang et al. , 2020). The history of all exposure-related variables (e. g. , environmental adversities, impact during pregnancy, migration) was collected retrospectively from structured interviewed completed by reliable informants, usually a parent.
Imputation of missing data At the first level, we ran the imputation at the scale-item level so that we could calculate total scores. As the items within a scale were likely to have the highest correlations, we did this as the first step. Also, due to the nature of different scales & items -some being continuous, some ordinal and some categorical, we employed different imputation algorithms (predictive mean matching, randomForest, logreg) for each of the scales. At the second level, after computing the scale or domain totals, we did another imputation. This time, we used a cut-off of no more than >25% of missing data per subject to impute. After excluding those cases that had >25% missing data for the summary scores and had no MINI data, we were left with 8300 participants. Prior to each imputation, we ran Little's test for missing completely at random (MCAR), which tests whether there are systematic differences between the observed and missing data on any of the other available/complete data.
Statistical analysis 8300 cVEDA participants (6-23 years), who had complete data available for the scales used in this study, were included. We used the adverse environmental exposure related, pregnancyrelated variables, and family history of psychiatric or substance use disorders to generate risk clusters of the participants. The relevant risk variables were adjusted for age and sex. Using Statistical Package for Social Sciences (SPSS version 22, IBM), we used two-step cluster analysis to generate risk clusters. Two-step cluster analysis could be considered as a combination of a K-means cluster and a hierarchical cluster analysis. Two-step cluster can deal with both ordinal and scale data. It also automatically selects the number of clusters with the best cluster quality, assessed by the average silhouette coefficient value of all objects in the cluster.
Cluster Analysis Two-step clustering based on these 12 developmental and environmental risk variables produced 2 clusters with fair cluster quality (average Silhouette score= 0. 2). The size of the two clusters was roughly the same between the two groups: 4197 (50. 5%) and 4103 (49. 5%) participants, and a size ratio between largest and smallest cluster of 1. 02. Figure  1  illustrates these results. Two-step cluster analysis in the SPSS is expected to produce the best cluster solution and optimal number of clusters, but we nevertheless checked the veracity of this by examining the auto clustering table which summarizes the process of determining the number of clusters. Our decision on the number of clusters was determined by the Bayesian criteria (BIC) change and ratio of distance measures.
80, α = 0. 05, and an intraclass correlation coefficient of 0. 03. With these parameters, The trial will also end if GP recruitment has ceased and all GPs have completed recruitment. If recruitment is too low (< 200 patient participants 12 months after beginning GP training programs), new recruitment strategies will be put in place and the study may be terminated. We will conduct semi-structured interviews with a purposive subsample of patients and GPs to better grasp qualitative aspects of their experience with quit aids offered as a default choice and the electronic decision aids. We will aim to recruit a purposive sample of six GPs and ten patients in the intervention and control groups, respectively, or until data saturation. According to Guest et al. , a sample of six to twelve participants per group is sufficient to reach thematic data saturation in qualitative interviews  [19].
Data management We will use REDCap to enter all data collected by GPs (paper forms) and data collected by telephone. RED-Cap software is a secure software hosted on Unisanté servers that allows for collecting and managing data. Source data will be from paper questionnaires, electronic questionnaires, and audio recordings of qualitative interviews, depending on the data collection point (Figs. 1  and 3 ). Patient paper questionnaires collected by participating GPs will be sent in batches, bimonthly from GPs to the study team at Unisante, where they will be stored in the Investigator Site File (office of the research assistant) in a closed cupboard. Data will be entered into REDCap Case Report Forms. Data collected during telephone interviews will be directly collected on electronic Case Report Forms in REDCap.
Statistical methods Data will be summarized for all patients and by group: intervention vs enhanced usual care. Continuous and count variables will be summarized as mean (95% confidence interval) or median (IQR) and categorical variables as numbers (percent). Binary outcomes at 6 months will be compared between the two arms intervention vs control using a logistic regression model with clusteredrobust standard error estimates. To take into account the clustered repeated measurement design in testing the intervention effect and changes over time, repeated measurement outcomes will be analysed using a mixedeffect models. Depending on the distribution of the outcomes, a logistic regression model will be used for binary outcomes while count regression models (Poisson, negative binomial or zero-inflated negative binomial model) will be used for count outcomes.
The intervention x time and others potential interactions will be tested in the models. In addition, subgroup analyses (sex, age, …) will be performed to assess in which subgroup the intervention is most effective. Data analysis will be performed using  Stata   4  Theoretical framework of interventions to use default choices and shared decision making for smoking cessation in primary care, which will change the approach of GPs to current smokers, increase quit attempts, and eventually increase rates of smoking cessation.
Handling of missing data and drop-outs For the primary outcome, participants lost to followup will be considered as continuing smokers. We do not plan to replace drop-outs as the sample size calculation anticipates a 15% under-recruitment / dropout rate. Imputation will not be done for other variables of interest (weight, laboratory data). Participants lost to follow-up will be considered as continuing smokers without quit attempts/quit aids. For other secondary outcomes, data will be considered missing completely at random. The primary analysis will be an intention to treat analyses, with participants analysed according to their study arm, regardless of the completion rate. Participants lost to follow-up will be considered as continuing smokers.
Confidentiality Coded data and participant identification lists will be stored separately. Trial and participant data will be handled with uttermost discretion and made only accessible to authorised personnel who require the data to fulfil their duties within the scope of the study. On the CRFs and other study specific documents, GP and patient participants are only identified by a unique participant number. The participant identification list that links identifying information to GP and patient study codes will be stored on the Unisanté server in a specific folder with access only for the PI and research assistants. The participant identification list will be deleted once patient recruitment and follow-up has been completed. Data from the Unisanté server are regularly backed up. Only allowed study members (doctors, statistician) and scientific collaborators and scientific collaborators with GCP training will have access to data.
We are including poly-morbid and vulnerable patients. We are purposefully keeping our inclusion process brief to encourage less motivated patients to participate and limit co-interventions. A research assistant will follow-up with GPs to ensure they are enrolling patients correctly in the study. During the follow-up calls, we will not reinforce or discuss the concepts presented during the course. We will ensure that all patients are provided with an information sheet and the information that the study is optional. This study presents several weaknesses, including an incomplete blinding and that the recruitment of patients is realized after the randomization of the GPs. We also use of self-reported smoking cessation at 6 months and not biochemical data to reduce the complexity of the study for the patients.
Quantitative data collection An online questionnaire was based on core themes identified in the literature as well as discussions with the research team (that included both MH and epidemiological experts). Tele-MH service provision was explored with activity managers from sites that shifted to remote care and those that were not able to, the former answering more detailed questions about feasibility, perceived acceptance and performance. 120 activity managers were invited to participate in an online survey through their official MSF email address, of which 84 (70. 0%) provided consent and 81 (67. 5%) participated. After completing an online informed consent process, a link to the quantitative questionnaire was sent to the participants. Quantitative data was entered and analysed using R v. 3.
Qualitative data collection To explore quantitative findings in more detail in-depth interviews (IDI) were conducted subsequently. IDI scripts were developed based on themes identified during questionnaire development and according to preliminary quantitative results. Data were collected until the point of saturation when no new themes emerged from interviews. Investigators sought a sample with maximal heterogeneity to capture as wide a spectrum of experience as possible, and therefore staff from every region where MSF has MH operations were included. The sample ultimately included staff from a variety of humanitarian contexts, and only two of 13 IDIs were from sites that did not shift to remote care. Interviews were conducted through audio or audio-visual equipment in English (except for one participant who indicated a preference for French, and whose interview was subject to English translation prior to analysis) and could be paused or ended at any time if requested by the participant.
Limitations Our results are limited by the composition of our sample. We were only able to survey and conduct interviews with MH staff, leaving the patient perspective unknown. Future research should capture their satisfaction with tele-MH services in these settings. Additionally, all respondents were active MSF staff at the time of their participation and were contacted through their official work email address, though scrupulous efforts were made to assure confidentiality and investigators' neutrality. Participants' gender and other demographic variables were not collected, so we are unable to say how these may or may not have influenced results, though they likely did in meaningful and potentially significant ways. Moreover, an online survey response rate of two thirds-likely driven by extreme time constraints of activity managers amid a pandemic crisis-may limit the generalizability of findings to other contexts, especially in the absence of data to allow comparison between respondents vs.
Please choose the three most time-consuming steps during the implementation of tele-MH. Logistical implementation of tele-MH infrastructure ☐ Promotion of the new teleservices within the community ☐ Training to staff ☐ Protocol drafting ☐ Data collection (including the adaptation of clinical files and database) ☐ Reorganization of team accountabilities and internal referral system ☐ Reorganization of external referral system ☐ Other:.