dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , impact of the intervention on TPB measures, mediational analyses), and feedback and considerations on the theory (items 21-23; i. e. , results use to discuss, validate, refine the TPB). An overall theoretical implementation score summing the 23 items was also created for each study. Two reviewers (OL-MG) independently coded the data for each included study. Results of the coding were then compared. The interrater reliability for the variables coded was at least substantial (κ >. 63), with a satisfactory global mean κ =. 88 (SD =. 13) (more details in Supplementary File 4). Any disagreements were resolved by discussion with AS and FCG.
Online survey to general public A web-based survey was used to measure awareness of the ECAC and attitudes towards its cancer prevention messages. The items of the questionnaire were adapted from a published instrument examining knowledge and attitudes towards cancer prevention  [15]. An additional item on prior awareness of the ECAC was included in the questionnaire. The survey was administered using the YouGov online omnibus survey panel  [16]. This format was used as it provides an efficient method to reach a broad, international population. The sample population was all adults aged >18 years old who were registered with the YouGov international online panel database. This criterion was chosen in order to be representative of the general public, which is the target audience for this survey.
Eight countries were selected to provide a broad geographic scope: Finland (Northern Europe); France, Republic of Ireland and the United Kingdom (Western Europe); Hungary and Poland (from Central and Eastern Europe); and Portugal and Spain (Southern Europe). The items of the questionnaire were provided in all respective national languages. The survey was conducted between the 18th and 24th October 2017. Respondents were contacted by email following a simple random selection from the panel database. For online surveys, the policy of the YouGov survey panel guarantees a minimum sample size per country of 500 respondents. To ensure the representativeness of respondents per country, a quota sample based on age cohorts according to the demographic characteristics of each country was applied. This ensures that respondents are not over-representation of a particular age cohort.
Acknowledgements Funding for this study was provided by the  Third EU Health Programme 2014-2020 , Grant Agreement # 881569. The views of this manuscript are those of the authors and do not reflect the official opinion of the  European Commission , the  Consumers, Health, Agriculture and Food Executive Agency (CHAFEA)  or any other institution of the  European Union. Neither the European Commission nor CHAFEA may be held responsible for the use which may be made of the information contained therein. YouGov PLC performed the survey referred to in this study. Fieldwork was undertaken between 18th -24th October 2017. The survey was carried out online.
Background: Medication error is a global threat to patient safety, particularly in pediatrics. Yet, this issue remains understudied in this population, in both hospital and community settings. Objectives: To characterize medication errors involving pediatrics reported to the French Medication Error Guichet, and compare them with medication errors in adults, in each of the hospital and community settings. Methods: This was a retrospective secondary data analysis of medication errors reported throughout 2013-2017. Descriptive and multivariate analyses were performed to compare actual and potential medication error reports between pediatrics (aged <18 years) and adults (aged >18 and <60 years). Two subanalyses of actual medication errors with adverse drug reaction (ADR), and serious ADR were conducted.
Data sources The French Medication Error Guichet is a centralized database for collecting all medication error reports (medication error risk, potential, and actual medication errors) occurring in France. Medication errors were reported by the regional pharmacovigilance centers network, pharmaceuticals laboratories, healthcare professionals, and patients. Medication errors without ADRs were reported voluntarily. However, medication errors with ADRs were reported on a mandatory basis for healthcare professionals, through the regional pharmacovigilance centers network.
Data collection Collected data included the person who reported the error (profession, workplace of the declarant), patient (gender, age), error description (date of occurrence, setting, classification, stages, types, and causes), products involved (drug classes, dosage, and routes of administration), and error consequence (presence or absence of an ADR and its seriousness). The error setting referred to the place of occurrence of the error, i. e. , hospital or community setting. The latter included homes, medical-social institutions, community pharmacies, and outpatient clinics. The stages of the medication-use process were classified as prescribing, dispensing, preparation, administration, and others (including monitoring error). The types of error were categorized as per the French National Agency for Medicines and Health Products Safety: wrong drug, wrong dose, wrong technique, wrong route, wrong patient, wrong duration/time, expired, deteriorated or not well conserved drug, and other medication errors (omission error, wrong rate, and other types defined as any error not fitting into the above pre-specified categories).
RESULTS Between 2013 and 2017, 12,136 medication error reports were submitted to the French Medication Error Guichet. The flowchart of the reports is detailed in Figure  1. Among the 12,136 total medication error reports, 1,322 reports of medication error risk, and 2,593 reports involving geriatric population were not included. Concerning missing data, 1,340 reports with unknown error setting, and 2,163 reports with unknown age data were excluded. Finally, 4,718 reports on actual and potential medication errors in pediatrics and adults in the hospital and community settings were included in the analysis.
The involvement of antibacterials for systemic use in pediatric medication errors is also commonly reported elsewhere, with proportions reaching 27. 2% in the hospital setting and 17% in the community. 16, 23 Antibiotics are extensively used in pediatrics, and subject to overtreatment through pressure from parents on clinicians to prescribe them. 29, 30 These factors potentially explain the common involvement of antibacterials in reported medication errors. A French study using a national reimbursement claims database showed that half (49%) the children received antibacterials for systemic use. 31 An opportunity may exist in decreasing unnecessary exposure to antibacterials, and the medication errors that occur with their use. In our analysis, administration stage topped the reported medication errors in adult and pediatric populations, in both settings.
Strengths and limitations This study is the first to comprehensively analyze pediatric medication errors reported to the French Medication Error Guichet, over a five-year period. Using a large national database, this study allowed delineating pediatric-specific medication error patterns in comparison with adults, in each of the hospital and community settings. Furthermore, we complemented our results with comparative analyses of reported medication errors with ADR and serious ADR. The analysis bore numerous challenges including the use of retrospective secondary data based on a reporting system. 49, 50 Particularly, missing data have decreased our initial sample size; nevertheless, since the sample remained large, it is unlikely that this resulted in a substantial loss of statistical power.
Randomization A permuted-block randomization (i. e. random block sizes) will be conducted by the study biostatistician using a computer-generated random allocation (Stata software, version 15, StataCorp, College Station, US), with a 1:1 ratio allocation, ensuring complete randomness of the assignment of a patient to each randomized group. The randomization will be stratified by centre. The randomization and allocation process will be performed by the statistician involved in this study. Physicians will be involved for the enrolment participants.
Socio-demographic data At baseline, socio-demographic data including age, gender, education level, marital status, household composition, number of children living in the household and employment status (i. e. length of tenure, working hours, overtime hours and job classification) will be collected. Also, medications and/or altered doses of an existing treatment over the last 3 months will be recorded. At T1 and T2, participants will be asked to report any life events that may have occurred over the past 3 months that may have affected their health or wellbeing.
Heart rate will be recorded at the end of the test, after 30 s and 60 s of recovery. This test has been found reliable for healthy adults and is highly reproducible  [53]. Low muscle mass and strength is associated with higher risk of mortality  [54]. Muscle strength will be evaluated through upper and lower limb strength. A handgrip dynamometer will be used with dominant hand to assess upper limb strength. Three measurements will be made and only the best one will be registered. This method has already proved its efficiency and reliability with healthy adults  [23, 55, 56]  and in clinical use  [57]. Lower limb strength will be assessed through a kneeextension power test on an isokinetic dynamometer (Biodex System 2, Biodex, Shirley, USA). Torque and power data will be corrected for gravity and will be digitized and exported at a rate of 2 kHz to an external data acquisition system (PowerLab 8/35; ADInstrument, New South Wales, Australia) driven by the LabChart 7.
3 Pro software (ADInstrument, New South Wales, Australia). This isokinetic dynamometer has been validated and found reliable for isometric and concentric assessment  [58, 59 ]. Subjects will be seated comfortably with a hip joint at 105°of flexion and will be attached on the trunk, the hip and the left leg to the dynamometer chair with Velcro straps to provide a complete stability during every maximal contraction. The dynamometer lever arm will be attached to the right leg by a strap positioned 1-2 cm above the lateral malleolus. Torque will be measured at a knee joint angle of 90°. Participants will perform three isometric 3 s-Maximum Voluntary Contraction (MVC) with 2 min of rest between each try. Peak torque values will be checked during data collection and an additional test will be done when there is a torque variation higher than 10% between the three tests.
A one-on-one interview will be done by an ergonomic specialist each participant. This interview will aim to better understand (1) how PA on the workstation has been conciliated with the work tasks performed by the subject and (2) reasons of subject's involvement in the prescription of PA. To achieve this objective, an analysis of the subject's diary will be carried out as well as a direct, semi-direct or open interview on the participant's motivations. These interviews will be recorded with the permission of each participant. These recordings are essential for the analysis of the verbal data collected.
Data management The Innovation and Clinical Research Direction (ICRD) of the University Hospital of Clermont-Ferrand carried out the method for data management but not have any authority or interfere in the data entry. Qualitative and quantitative data will be entered in paper-based case report forms and electronically into a database by TG. Regular verification will be conducted to ensure the accuracy of the coding and entry by members of the RE-MOVE team. Access to all the information regarding the study and to the database will be restricted and controlled. The electronic data forms will be kept safe in a specific computer in the research laboratory with access code needed. All paper-based form related to the trial will be kept in locked closets. While updating the database, a complete back-up will be executed twice weekly.
Confidentiality All participants' information will be collected in a pseudonymous form to ensure a complete anonymity of their data throughout the research process. Participants will be informed of the confidentiality and the anonymity of their data in the information note and the ethical application. Furthermore, participants will take notice of their ability of withdrawal from the study and to withdraw/ amend parts of their data at any moment. Data will be kept safe in a specific computer in the research laboratory with access code needed. Only the principal investigator will be allowed to open the specific session for data access.
Data Source Data were extracted from the clinical information system of HEGP, a 700-bed university hospital located in Paris, France, specializing in oncology, cardiovascular disease, and emergency medicine. HEGP has been using a clinical information system since its opening in 2000  [17]  and is certified on the 6th level of the Healthcare Information and Management Systems Society (HIMSS) stage  [18]. An i2b2 clinical data warehouse  [19]  fed with data collected in the clinical information system is also deployed.
Data Extraction In the remainder of the manuscript, we will refer to the part of an email address located after the character "@" as the domain name (ie, Local-part@domainname) We collected the following data from the hospital's information system: the domain name of the patient's email address, sex, year of birth, year of the first contact with the hospital (stay or visit), year of last contact with the hospital, and the presence of their first or last names in the domain name of their email addresses. To preserve privacy, an algorithm automatically substituted the domain name with a random string before the extraction when the domain name included the first or last names of the patient.
Comparison of the Population of Patients According to the Presence of an Email Address in the Database The presence of an email address in the database may depend on the year of first contact with the hospital. In addition, the use of email address and the administrative registration procedure are time dependent. To mitigate this risk of bias, we matched cases (patients with email address) and controls (patients without email address) on the year of first contact with the hospital using a 1:1 ratio. (univariate matching). The quality of the matching was checked (Multimedia Appendix 1). In addition to demographic data, we measured the chronic comorbidities from the ICD-10 (International Classification of Diseases, 10th revision) adaptation of the Charlson Comorbidity Index  [20]. We defined the variable "at least one chronic disease" from the chronic comorbidities included in the ICD-10 phenotyping: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, connective tissue disease, ulcer disease, mild liver disease, moderate/severe liver disease, diabetes mellitus, hemiplegia, moderate/severe renal disease, diabetes mellitus with chronic complications, any tumor, leukemia, lymphoma, metastatic solid tumor, and AIDS.
Classification of Email Addresses We mapped the most frequently used domain names to companies providing the email service (eg, emails ending with gmail. com, hotmail. com, orange. fr, or free. fr are associated with Google, Microsoft, Orange, and Free corporations, respectively). The companies were identified using prior knowledge and Whois requests when necessary. We then evaluated in the curated data the frequency of each of the providers. We defined a business email as an email provided by someone's employer. To define business emails, we leveraged a list of free email service providers compiled by Svay  [21] , secondarily enriched with French and European email providers: voila. fr, dbmail. com, icloud. com, gmx. fr, bluewin. ch, dartybox. com, skynet. be, gmx. com and topnet. tn, numericable.
Statistical Analysis and Implementation Data were expressed as n (%). Characteristics were compared between groups (included versus excluded population, with email address versus without email address) by Mann-Whitney U test for numerical data and chi-square test for categorical data. A logistic regression analysis was performed to determine characteristics associated with having an email address on the matched groups. All analysis were performed using R Statistical Software  [22]. The source code and resource files (including the updated list of email providers) are available online  [23].
Population Between 2000 and August of 2018, we collected 41,004 patient email addresses (4. 22% of the total population of patients, ie, 41,004/971,822). Of the total number of patients in the database (971,822 patients), we selected a subgroup of 41,004 patients as a control group (ie, patients who did not provide an email address). The characteristics of the population and the control group are described in Table  1. Statistical tests are presented in Multimedia Appendix 3.
This step runs over 6-12 months and having completed Diploma 1-2, the students are well trained in using the literature and writing a thesis that can possibly be structured like a scientific article and published. It is based on their own data from their research project or secondary data from the MAHRE-Net. The thesis can be either structured as a scientific article or as the traditional thesis structure. By using the traditional thesis template in developing countries a lot of valuable scientific work is wasted because the thesis cannot be used as a scientific reference like peer-reviewed articles. There must, of course, be support from the institution that the finished study work can consist of a scientific article manuscript. Now the students can benefit from the possibilities to get data from The Maritime Health Research and Education-NET (MAHRE-Net) and other sources.
Acknowledgements The authors would like to express our deep gratitude for inspiration and support during the development of the project over the years to all friends and colleagues who have followed and supported the project. Especially we thank  Director Mrs. Fulvia Sucre  and Medical Director  Dr. Rimsky Sucre ,  Clinic Einstein , and  Dr. Enrique Mendoza ,  Dean of the Medical School of the National University of Panamá  and the more than sixty health professionals from 30 countries who have accepted to be members of MAHRE-Net on the invitation.
Setup A bench previously developed in the laboratory was used. The simulator was a two-part metallic structure. The upper part of the simulator was connected to the lower part through four springs which supported the weight of the upper part of the simulator. A shaker (V 550, LDS, Bruel & Kjaer, Naerum, Danemark) was placed underneath and could move the upper part vertically. A standard helicopter seat (H160, Fischer Seats, Landshut, Germany) was fixed to the upper part. Figure  1  shows a picture of that bench. The simulator was controlled via Matlab. Signals were generated by a sound card (ROGA D, Viaxys, Ferri eres-en-Gaˆtinais, France) and went through a band pass filter whose cut-off frequencies were 10 and 40 Hz before being sent to the shaker amplifier (PA 1000, LDS, Bruel & Kjaer, Naerum, Danemark).
Subjective data processing The raw discomfort ratings show that the participants use very different scales. The majority of participants felt that discomfort increased with the level of vibration. The minimum ratings given by participants ranged from 0. 05 to 30 and the maximum ratings from 6 to 210. Ratios between maximum and minimum individual data varied between 7 and more than 100. This shows that participants used very different scales to assess discomfort. Therefore, an average of the three rehearsal ratings followed by a normalization had to be done. This was achieved using the average deviation procedure described by Han et al. 12. Let X ijk be the raw discomfort assessment given by the subject i for the presentation k of the stimulus j. The normalized value of the ith participant for the jth stimulus SV ij is defined as follows: SV ij ¼ M ij � MD i (3) where M ij is the average of the logarithm of the three ratings for the ith participant and the jth stimulus: M ij ¼ X 3 k¼1 log 10 ðX ijk Þ 3 (4) and MD i is the difference between the average of the participants' ith odds and the general average GM: MD i ¼ M i � GM (5) where M i is the average of the score of the ith participant (computed over the 41 stimuli) and GM is the average of all scores of the 53 participants: M i ¼ X 41 j¼1 M ij 41 (6) GM ¼ X 53 i¼1 M i 53 (7).
Acknowledgement The author is involved in the working group network of the  National Health Literacy Demonstration Projects (WHO Europe , and  WHO Geneva) , and an active member of  R eFLIS  ( R eseau Francophone  de recherche en LItt eratie en Sant e). He wishes to thank all practitioners, patients, and all researchers who contributed to the reflexion and to the development in France (R eunion) and Africa of the health literacy projects and scaling up, and particularly  Maryvette Balcou-Debussche  and her team,  ICARE unit (Institut Coop eratif Austral de Recherche en Education ,  Universit e de La R eunion ) and  Richard Osborne  and his team of the  Centre for Global Health and Equity, Swinburne University Melbourne.
As before, I draw on my own research experience to stimulate a discussion. I already mentioned a case in which I declined to speak at a TV show to avoid risks of distortion of my findings on online networks and eating disorders. This is a far-too-common trade-off between two ethical imperatives, one that urges researchers to reach out to the public, and one that invites to caution in light of the potential negative Fig. 2. T-shirts printed by OuiShare members in 2017, with my graphs of networks of ties between participants at the previous edition of the event. The "before" graph refers to ties that pre-existed the event; the "after" one to ties formed during it. I drew the graphs in Gephi using questionnaire data I collected in 2016. The labels, the "Human Tinder" title, and the "Connecting people in real life since 2013" caption were added by OuiShare.
These considerations prompt a reflection on when to return results. The Society for Neuroscience, one of the few to have released guidelines for ethical communication with the press, recommends waiting until a finding is peer-reviewed and accepted for publication before announcing it to the public at large. However in the social sciences, this should not rule out the possibility of earlier release to researched and/or affected communities, as long as the preliminary or descriptive nature of findings is clear. I construe report of findings as a cyclical process involving dynamic, mutual interchange between researchers and relevant stakeholders. I shared my preliminary OuiShare Fest findings with the organizers before showing them to academic audiences. By so doing, I both offered a reward to OuiShare and benefited my research, earning the possibility to go back to the field for a new wave of data collection, and receiving feedback that helped me refine my interpretation.
Acknowledgements A preliminary version of this paper was presented at the workshop " Recent Ethical Challenges in Social Network Analysis " (RECSNA) organized by the  Social Network Analysis Group of British Sociological Association (BSA-SNAG)  in collaboration with  RT Réseaux Sociaux of Association Française de Sociologie , in Paris on 5-6 December 2017. I gratefully acknowledge support from  MSH-Paris Saclay  and  IEA de Paris. I thank the RECSNA team, attendees, and anonymous external reviewers for their feedback. The usual disclaimer applies.