dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction and data synthesis For each study selected, the two investigators (LB and AZ) extracted data using a standardised data extraction checklist on costs, cost-effectiveness and family planning outcomes. Another template based on Hoffman's checklist was used to report data on the content and implementation of each intervention  [14]. Authors were contacted when data from the original report were unclear. Discordances between reviewers were resolved by consensus within the research team. Study quality was assessed using the risk of bias tool from Cochrane for randomised studies  [7]  and the Cochrane Effective Practice and Organization of Care (EPOC) checklist for non-randomised studies  [15]. Baseline outcome measurement, socio-demographic characteristics of groups (control and intervention) and protection against contamination were added to assess the studies' quality according to EPOC guidelines  [15].
Quality of both cost and cost-effectiveness studies was assessed using Drummond's checklist, as recommended by the Cochrane Collaboration  [16]. Data were entered into the Cochrane Review Manager software (RevMan 5. 1)  [14]. A meta-analysis was conducted with dichotomous data using odds ratios (OR) with a 95% confidence interval (CI) as measures of effect size. Interstudy variation was incorporated with the assumption of a random effects model for the treatment effect using the DerSimonian and Laird procedure and inverse variance method when heterogeneity between trials was significant or superior to 50%  [11, 13]. A fixed effect model was used in the absence of significant heterogeneity, using the Mantel-Haenszel method. Heterogeneity was assessed using the I 2 statistic and Q test. Sensitivity and subgroup analyses were conducted on study characteristics, quality of study, type of intervention and intensity of counselling and educational interventions.
Intensity was characterised by number of sessions and duration (low = one session of <30 min; high = more than one session and more than 30 min). Finally, narrative analysis was conducted when metaanalysis was not possible because of heterogeneity of outcomes or when no data were available. Narrative synthesis followed the guideline developed by Popay et al. [13]. Effectiveness in this review was mainly assessed based on significant changes in contraceptive prevalence rates or numbers of new users of contraception. Although some studies assessed perception, knowledge and attitude with regard to family planning, the evidence summary was based on effectiveness in improving contraceptive prevalence rates or number of new users of contraception.
The value of 9 mSv WLM -1 is obtained without any information on risk of exposure during childhood from radon and progeny. Dose coefficients for inhalation and ingestion of radioisotopes of radon, including inhalation of radon-222 and its radioactive progeny, will be included in Part 3 of a series of reports currently in preparation by ICRP on Occupational Intakes of Radionuclides (OIR series). These data will include values for inhaled radon-222 and progeny of about 11 mSv effective dose WLM -1 for exposures in mines and 20 mSv WLM -1 for indoor workplaces. However, using a more realistic breathing rate for sedentary occupations such as office workers gives a value of about 14 mSv WLM -1  (Harrison and Marsh 2012; Marsh et al. 2010). For dwellings, the dose coefficient was calculated to be about 13 mSv WLM -1.
It will be hard to communicate why in radiation protection we are in a variety of situations concerned with fractions of mSv, on the one hand, and casually offer a permanent dose that will come up with such an exorbitant higher value, on the other hand. Thus, we have to be extraordinarily careful with changes in dose coefficient, and if we change, we have to have an imperturbable firm database. At present, it can be judged for both the epidemiological approach and the dosimetric approach in this sense the database is not sufficiently resilient. The alternative approach presented here may avoid some of the shortcomings with respect to both principal conceptual aspects and practical implications. The detriment-adjusted risk of lung cancer per unit lung equivalent dose is identical for all radiation qualities and should be unaffected by new radon risk estimates.
, 2009; Mojtabai et al. , 2015) , but this work is limited by either being based on small restricted samples or by being subject to long-term recall bias. Given the importance of an educated workforce for the human capital potential of a country, it would be valuable to know more about five questions  : First, what  is the prevalence of mental disorders among college students? Second, what proportion of those disorders had onsets prior to college entry? Third, to what extent are disorders with pre-matriculation onsets associated with college entry? Fourth, what is the relative importance of disorders with pre-matriculation and post-matriculation onsets in predicting college attrition? Fifth, what proportion of college students with mental disorders receives treatment? We address these five questions using data from community epidemiological surveys carried out in 21 different countries in the WHO World Mental Health (WMH) Survey Initiative.
Another set of limitation involves the assessment of mental disorders. Lifetime mental disorders and ages-of-onset were assessed retrospectively. Although the WMH assessment used procedures shown experimentally to improve accuracy of recall  (Scott et al. , 2014) , there are likely to be downward biases in reports of lifetime prevalence and possibly also in dating age-of-onset of disorders due to recall errors. It is unclear whether these biases would be different for students, attriters, and respondents who never entered college. In addition, diagnoses were based on fully-structured lay-administered interviews rather than semistructured clinical interviews, although the WMH clinical appraisal data are reassuring  (Haro et al. , 2006). In addition, we assessed only prevalence, not severity, of disorders.
Most WMH surveys are based on stratified multistage clustered area probability household samples in which samples of areas equivalent to counties or municipalities in the US were selected in the first stage followed by one or more subsequent stages of geographic sampling (e. g. , towns within counties, blocks within towns, households within blocks) to arrive at a sample of households, in each of which a listing of household members was created and one or two people were selected from this listing to be interviewed. No substitution was allowed when the originally sampled household resident could not be interviewed. These household samples were selected from Census area data in all countries other than France (where telephone directories were used to select households) and the Netherlands (where postal registries were used to select households).
Acknowledgments The WHO World Mental Health Survey Collaborators are  Tomasz Adamowski, PhD, MD ,  Sergio Aguilar-Gaxiola, MD, PhD ,  Ali Al-Hamzawi, MD ,  Mohammad Al-Kaisy, MD ,  Abdullah Al Subaie , FRCP,  Jordi Alonso, MD, PhD ,  Yasmin Altwaijri, MS, PhD ,  Laura Helena Andrade, MD, PhD ,  Lukoye Atwoli, MD ,  Randy Auerbach, PhD ,  William Axinn, PhD ,  Corina Benjet, PhD ,  Guilherme Borges, ScD ,  Robert Bossarte, PhD ,  Evelyn Bromet, PhD ,  Ronny Bruffaerts, PhD ,  Brendan Bunting, PhD ,  Ernesto Caffo, MD ,  Jose Miguel Caladas de Almeida, MD, PhD ,  Graca Cardoso, MD, PhD ,  Stephanie Chardoul ,  Somnath Chatterji, MD ,  Alexandre Chiavegatto Filho, PhD ,  Pim Cuijpers, PhD ,  Louisa Degenhardt, PhD ,  Giovanni de Girolamo, MD ,  Ron deGraaf, MS, PhD ,  Peter deJonge, PhD ,  Koen Demyttenaere, MD, PhD ,  David Ebert, PhD ,  Sara Evans-Lacko, PhD ,  John Fayyad, MD ,  Marina Piazza Ferrand ,  DSc, MPH ,  Fabian Fiestas, MD, PhD ,  Silvia Florescu, MD, PhD ,  Barbara Forresi, PhD ,  Sandro Galea ,  DrPH,MD,MPH ,  Laura Germine, PhD ,  Stephen Gilman, ScD ,  Dirgha Ghimire, PhD ,  Oye Gureje, MD, PhD ,  Josep Maria Haro, MD, MPH, PhD ,  Yanling He, MD ,  Hristo Hinkov, MD ,  Chi-yi Hu, PhD, MD ,  Yueqin Huang, MD, MPH, PhD ,  Aimee Nasser Karam, PhD ,  Elie G.
Ebert, PhD ,  Sara Evans-Lacko, PhD ,  John Fayyad, MD ,  Fabian Fiestas, MD, PhD ,  Silvia Florescu, MD, PhD ,  Barbara Forresi, PhD ,  Sandro Galea ,  DrPH, MD, MPH ,  Laura Germine, PhD ,  Stephen E. Gilman, ScD ,  Dirgha J. Ghimire, PhD ,  Meyer D. Glantz, PhD ,  Oye Gureje, PhD ,  DSc, FRCPsych ,  Josep Maria Haro, MD, MPH, PhD ,  Yanling He , The 2007  Australian National Survey of Mental Health  and Wellbeing is funded by the  Australian Government Department of Health and Ageing. The  São Paulo Megacity Mental Health Survey  is supported by the  State of São Paulo Research Foundation (FAPESP)   Thematic  Project Grant  03/00204-3. The  Bulgarian Epidemiological Study of common mental disorders EPIBUL  is supported by the  Ministry of Health  and the  National Center for Public Health Protection.
The  Chinese World Mental Health Survey Initiative  is supported by the  Pfizer Foundation. The  Shenzhen Mental Health Survey  is supported by the  Shenzhen Bureau of Health  and the  Shenzhen Bureau of Science, Technology, and Information. The  Colombian National Study of Mental Health (NSMH)  is supported by the  Ministry of Social Protection. The  Mental Health Study Medellín -Colombia  was carried out and supported jointly by the  Center for Excellence on Research in Mental Health  ( CES University ) and the  Secretary of Health of Medellín. The  ESEMeD  project is funded by the  European Commission  (Contracts  QLG5-1999-01042 ;  SANCO 2004123 , and  EAHC 20081308 ), (the  Piedmont Region (Italy) ,  Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain  ( FIS 00/0028 ),  Ministerio de Ciencia y Tecnología, Spain  ( SAF 2000-158-CE ),  Departament de Salut, Generalitat de Catalunya, Spain ,  Instituto de Salud Carlos III  ( CIBER CB06/02/0046 ,  RETICS RD06/0011 REM-TAP ), and other local agencies and by an unrestricted educational grant from  GlaxoSmithKline.
The  Mexican National Comorbidity Survey (MNCS)  is supported by The  National Institute of Psychiatry Ramon de la Fuente  ( INPRFMDIES 4280 ) and by the  National Council on Science and Technology (CONACyT-G30544-H) , with supplemental support from the  PanAmerican Health Organization (PAHO). Corina Benjet  has received funding from the  (Mexican) National Council of Science and Technology  (grant  CB-2010-01-155221 )  Te Rau Hinengaro: The New Zealand Mental Health Survey (NZMHS)  is supported by the  New Zealand Ministry of Health, Alcohol Advisory Council , and the  Health Research Council. The Nigerian Survey of Mental Health and Wellbeing (NSMHW)  is supported by the  WHO (Geneva) , the  WHO (Nigeria) , and the  Federal Ministry of Health, Abuja, Nigeria. The  Northern Ireland Study of Mental Health  was funded by the  Health & Social Care Research & Development Division of the Public Health Agency.
The  Romania WMH study projects "Policies in Mental Health Area" and "National Study regarding Mental Health and Services Use " were carried out by  National School of Public Health & Health Services Management  (former  National Institute for Research & Development in Health ), with technical support of  Metro Media Transilvania , the  National Institute of Statistics-National Centre for Training in Statistics, SC. Cheyenne Services SRL ,  Statistics Netherlands  and were funded by  Ministry of Public Health (former Ministry of Health)  with supplemental support of  Eli Lilly Romania SRL. The  US National Comorbidity Survey Replication (NCS-R)  is supported by the  National Institute of Mental Health (NIMH ;  U01-MH60220 ) with supplemental support from the  National Institute of Drug Abuse (NIDA) , the  Substance Abuse and Mental Health Services Administration (SAMHSA) , the  Robert Wood Johnson Foundation  ( RWJF; Grant 044708 ), and the  John W.
Background: Although many organizational culture questionnaires have been developed, there is a lack of any validated multidimensional questionnaire assessing organizational culture at hospital ward level and adapted to health care context. Facing the lack of an appropriate tool, a multidisciplinary team designed and validated a dimensional organizational culture questionnaire for healthcare settings to be administered at ward level. Methods: A database of organizational culture items and themes was created after extensive literature review. Items were regrouped into dimensions and subdimensions (classification validated by experts). Pre-test and face validation was conducted with 15 health care professionals. In a stratified cluster random sample of hospitals, the psychometric validation was conducted in three phases on a sample of 859 healthcare professionals from 36 multidisciplinary medicine services: 1) the exploratory phase included a description of responses' saturation levels, factor and correlations analyses and an internal consistency analysis (Cronbach's alpha coefficient); 2) confirmatory phase used the Structural Equation Modeling (SEM); 3) reproducibility was studied by a test-retest.
Design of a questionnaire Selection of measuring instruments A comprehensive literature search was conducted to identify existing measuring instruments. The retrieval strategy (data bases explored and key terms used) is shown in Table  1. References indicated in articles found were explored and experts were contacted. Papers inclusion criteria were: French or English language, date of publication between 1980 and 2014, description of a quantitative organizational culture measurement instrument, based on a typology or not, available in its entirety or not, free or on a paying basis. Out of this first selection, a multidisciplinary expert group, composed of four methodologists and three specialists in management sciences, working in three different French research departments (Bordeaux, Paris, Nantes) specialised in questionnaire validation or management sciences, selected tools on the following criteria: quantitative instruments of organizational culture, having already been used in a healthcare settings, having a good face validity to assess a broad range of cultural dimensions.
Validation of the questionnaire Data collection A survey was conducted in five French regions: Aquitaine, Bretagne, Franche-Comté, Poitou-Charentes, Rhône-Alpes, representing a population of 10 millions inhabitants. The study population consisted of all professionals involved in patient care in the participating medical wards (doctors, nurses and orderlies), during the daytime or at night, and whatever their status or length of service. The following were excluded: trainee nurses and orderlies, doctors who were working only temporarily in the department, other paramedical personnel (physiotherapists, psychologists, etc. ), and professional staff on extended leave. Each professional received oral and written information about the study, delivered during a meeting organized in each participating ward.
The number of significant factors was determined by applying the combination of criteria (the Kaiser's criterion-eigenvalue > 1and the Horn parallel analysis) and an Oblimin rotation was used to facilitate the factor interpretation. Each factor was described taking into account a squared cosine higher than 0. 30 indicating high representativeness and a factor loading higher than 0. 40 (absolute value) indicating significant contribution of the item. Internal consistency of the questionnaire and each of its a priori dimensions and sub-dimensions was measured by the Cronbach's alpha coefficient (if alpha > 0. 70, consistency was considered as satisfactory). As a result of the analysis, the questionnaire was trimmed and a new construct of dimensions and sub-dimensions was envisaged. b) For the confirmatory analysis, the Structural Equation Model (SEM) was used to confirm the construct assumption of the questionnaire into dimensions and sub-dimensions, and the adjustment of the model to the data  [43].
OC questionnaire construct The literature review identified 41 OC measuring instruments. The expert group selection process excluded all but 14, out of which a base of 906 items and 100 themes was created. Were extracted and removed from this database the items that were less relevant to a French context, mainly a few items clearly oriented to productivity and on fierce competition. Healthcare professionals are not confronted with direct competition and productivity requirements in the French healthcare system. It is at least partly explained by the modalities of hospital professional management, based on public sector rules and by patient management means, organized at ward level. Items that could not be found in selected questionnaires were introduced as they were considered key points in sociology of work.
M Sibe et al. published an analysis of data collected for COMEt validation that reinforces its internal and external validity  [52]. M Sibe et al. showed the discriminative capacity of COMEt instrument. The analysis of responses obtained for COMEt validation, allowed to distinguished two groups of wards: the wards with high development of all COMEt dimensions (9 wards on the 36 included, called "optimistic" wards) and the wards with low development of the COMEt dimensions (13 wards on the 36 included, called "pessimistic" wards). There was also a third group of 12 wards with intermediate results. One of the critical functions of a group's culture is to establish a distinctive identity and thereby provide a means by which members of the group can differentiate themselves from other groups. COMEt showed its discriminative capacities, in accordance with this property of OC.
COMEt development included specificities of French health care system that would probably lead to exclude some items from a COMEt international common core set. From our comprehensive database of OC items and themes, were extracted and removed the items which were less relevant to a French context (mainly few items clearly oriented to productivity, items on fierce competition). Face validation led to some changes in item wording that are probably a direct consequence of French operational specificities. As example, contrary to what could be found in other instruments, conflict management procedures or quality of relationship between professionals were treated separately for medical doctors and paramedical professionals. In a transvalidation process across language and health care systems, attention should be put on these items.
Bariatric surgery is a well-accepted procedure for severe and massive obesity management. We aimed to determine trends, geographical variations, and factors influencing bariatric surgery and the choice of procedure in France in a large observational study. The Health Insurance Fund for Salaried Workers (Caisse National Assurance Maladie Travailleurs Salariés) covers about 86% of the French population. The Système National d'Information Inter-régimes de l'Assurance Maladie database contains individualized and anonymized patient data on all reimbursements for healthcare expenditure. All types of primary bariatric procedures (Roux-en-Y gastric bypass [RYGB] or omega loop, adjustable gastric banding [AGB], or longitudinal sleeve gastrectomy [LSG]) performed during 2011 to 2013 were systematically recorded.
Study design and participants French National Health Insurance covers the entire population. The general scheme, the Health Insurance Fund for Salaried Workers (Caisse National Assurance Maladie Travailleurs Salariés), covers all French employees, as well as specific subgroups such as students, and accounts for about 86% of the population (about 58. 2 million beneficiaries in 2010). Agricultural workers and farmers, self-employed, and 12 other specific schemes cover the remaining 14% of the population. The SNIIRAM database contains individualized and anonymized patient data on all reimbursements for healthcare expenditure including pharmaceutical products as well as outpatient medical and nursing care, prescribed or performed by French healthcare professionals. This database includes all hospitalizations recorded in the hospital discharge database (Programme de Médicalisation des Systèmes d'Information), all prescriptions, and healthcare services and procedures reimbursed together with their respective dates.
Drugs are identified using their Anatomical Therapeutic Classification code, while medical and surgical procedures are identified using another specific classification. The SNIIRAM does not contain any medical information regarding prescriptions or clinical examinations, but includes data on the presence of chronic diseases eligible for 100% reimbursement of healthcare expenditure following approval by a national health insurance physician. Chronic diseases and hospital diagnoses are coded according to the International Classification of Diseases (ICD-10, http://www. who. int/classifications/icd/en). Analysis of the SNIIRAM, including the Programme de Médicalisation des Systèmes d'Information database, was approved by the French data protection authority (Commission Nationale de l'Informatique et des Libertés).
Covariates BMI was available during the hospital stay and is classified in the SNIIRAM database according to the following categories: 30 to 39, 40 to 49, and ≥50 kg/m 2. The universal health insurance coverage for low-income patients ("Couverture Médicale Universelle") provides 100% reimbursement of health expenditures, and the data were recorded for all patients. The presence of coronary heart disease or heart failure was defined on the basis of ICD-10 codes for a chronic disease eligible for 100% reimbursement of healthcare expenditure during 2012 to 2013, or ICD-10 codes of all diagnoses coded during hospitalizations in 2009 to 2013. The presence of obstructive sleep apnea syndrome (OSAS) was determined by reimbursement for continuous positive airway pressure during the previous 12 months or hospitalization with a diagnosis of OSAS.
Statistical analyses Baseline characteristics were described according to gender, age, and type of surgery (AGB, sleeve, or bypass). Age-and sexstandardized rates according to the structure of the whole French population for the same year of surgery were computed in each region of France. Data are presented according to the regional number of procedures and the relative rate of a given procedure compared to the other 2 procedures. Prevalence was calculated without confidence intervals, as our estimates were based on all individuals undergoing bariatric surgery in France and not on a sample of the population. Stepwise descending linear regressions were computed to identify predictive factors for gastric bypass compared to AGB (reference group, P < 0. 20 for variable selection). Similar analyses were performed for SG compared to AGB.
The strength of the present study is that it includes all patients who underwent a bariatric procedure in France except for a few patients who did not meet the inclusion criteria. This large sample was collected across regions, allowing a detailed assessment of geographical variations of bariatric surgery. However, the present study presents a number of limitations. First, as the present study was based on a large administrative dataset, some data may be invalid or missing. However, several previous publications have demonstrated the value of using the French National Health Insurance Information System for epidemiological research in different contexts. [13, 14] Second, some administrative data are not sufficiently precise. For example, BMI is recorded according to 3 predefined categories: 30 to 39, 40 to 49, and ≥50 kg/m 2 , which does not allow specific analysis of patients with a BMI greater than or less than 35 kg/m 2.