dataesr/scientific-paragraphs-categorization huggingface dataset:
Detailed information was collected on the type of childcare (day-care centre 'creche', nanny), the age when care started, and the magnitude of child-care attendance (duration of stay, mean hours per week, total number of children attending the centre). Cumulative exposure to a type of childcare was calculated for each child (number of months attending child care Â mean hours per week at that child care Â number of other children at childcare multiplied by 4. 35 (number of weeks per month)). Data on the number of other children in the household and birth order were also collected. Mothers were also asked how often the child had visited a farm and whether the child had been in regular contact, at least once a week, with animals (cats, dogs, domestic birds, domestic rodents, poultry, rabbits, pigs, cows, sheep, goats, horses, and other animals).
ACKNOWLEDGEMENTS We are grateful to  Noureddine Balegroune ,  Sofiene Ben Salha , and the team of clinical research associates who contributed to the recruitment of the cases;  Laure Faure  and the staff of the  French National Registry of Childhood Blood Malignancies , who contributed to case detection and verification;  Christophe David  and the team of interviewers (Institut IPSOS), who recruited the controls and interviewed the cases and controls; and  Elsa Charles  for her technical assistance We would like to thank all of the  SocieteFranc¸aise de lutte contre les Cancers de l'Enfant et de l'Adolescent (SFCE) principal investigators: AndreBaruchel (Hopital Saint-Louis/Hopital Robert Debre, Paris ),  Claire Berger  ( Centre Hospitalier Universitaire, Saint-Etienne ),  Christophe Bergeron  ( Centre Leon Berard, Lyon), Gerard Michel (Hopital La Timone, Marseille ),  Yves Bertrand  ( Hopital Debrousse, Lyon ),  Pascal Chastagner  ( Centre Hospitalier Universitaire, Nancy ),  Patrick Boutard  ( Centre Hospitalier Regional Universitaire, Caen ), This work is published under the standard license to publish agreement.
5 % were not present at the time of the survey, 15. 7 % refused to participate again and 22. 9 % were lost to follow-up without information. People who could not be re-interviewed in 2010 were replaced by other people of the same census block, following a random methodology identical to the one described above. As in 2005, refusal rate in 2010 was 29. 1 %. Sex ratio and mean age of people followed up were not different from newly included people. This study analyzed the data collected in of 2010 among the final sample of 3006 individuals.
To explore factors associated with the intention of protection and the adoption of a condom-based approach for protection against HIV, we estimated logistic regression models, taking into account the cluster effect (vce(cluster)) in Stata 11® software, separately for men and women (as sexual biographies, attitudes and behaviours usually differ between men and women  [19, 36] ). After estimating the association between migration origin and both outcomes with participants' age as the only covariables (which was preferred to an univariate model since age influences strongly perceptions and behaviours related to sex), we estimated five models, adding successively variables related to 1) demographics, 2) socioeconomic status, 3) sexual biography, 4) communication about sexuality and perception of HIV, and 5) HIV prevention behaviours.
Acknowledgements The 2010 data collection of the SIRS cohort study was supported by the  Institute for Public Health Research (IRESP)  and has also benefited from the support of the Mutualité Française. This study was part of a specific research project supported by the  French National Agency for Research on AIDS (ANRS)  and  Sidaction. Paul Duke  and  Nas Sabrie  deserve special thanks for their proofreading.
For data collection, open, in-depth interviews were conducted, as one of the most appropriate resources to obtain textual materials, which provide access to participants' narratives with description of facts, explanations, justifications and feelings  (Demazière & Dubar, 1997; Bertaux, 2001; Norris et al. , 2005). Questions were raised regarding interviewees' sexuality experience, such as: what they think about sex, how they live their sexuality, who their partners are. Particular care was excercised to find a starting point and ways to return to questions that would make individuals express themselves without feeling embarrassed, which would block or limit their speech given the peculiarity of the subject. Objective data were also collected to characterize the individuals' social context.
Participants Following the recruitment of community organizations, all women who decide to attend the workshops will be self-identified for participation in the study. Recruitment strategies vary slightly per site, based on the requirements of the respective ethics committees. In France and in Western Canada, baseline data will be collected and participants will be screened and enrolled to the study prior to the receipt of the intervention. In the UK and Quebec, women will complete the baseline questionnaire prior to the delivery of the intervention, but interested participants will sign the consent form and return the baseline questionnaire only after the workshop is completed. In a second screening stage, a research assistant confirms eligibility status and interest in enrolling in the trial via telephone follow-up within 1 week of the workshop.
Across all sites, a subsample of participants will be recruited for an ethnographic study that aims to explore continence stigma, aging, and participant perspectives of the intervention and control workshops. Data from the qualitative interviews will help the team understand the mechanisms and outcomes of the intervention on selfstigma related to incontinence. Approximately 20 participants will be interviewed within 2 weeks of the workshop to explore participant perspectives of the intervention and control workshops, and related feelings associated with continence and age-related stigma. A second group of 20 participants will be interviewed 1 year after the intervention workshop. Five participants from each trial site will be purposively selected from those who have shown significant benefit from the continence promotion intervention, to investigate the impact on stigma and to understand the mechanisms behind the perceived benefits of the workshop.
Stigma and quality of life The second co-primary outcome will be the difference in quality of life and stigmatization between the intervention and control groups. The 22-item Incontinence Quality of Life questionnaire (I-QOL) is a validated quality of life instrument for urinary incontinence that includes content relevant to stigma, such as "I worry about others smelling urine on me" and "I worry about being embarrassed or humiliated because of my incontinence"  [33]. Exploration of stigma perceptions will be supplemented by the data collected in the qualitative interviews among a sub-group of participants immediately after, and at 1 year post intervention. Data will be collected using semistructured one-to-one interviews. The qualitative investigation will also be used in the development of the Continence Psychosocial Impact of Assistive Devices Scale (C-PIADS), which is being developed from the original validated Psychosocial Impact of Assistive Devices Scale (PIADS).
Falls The third co-primary outcome measure will be falls experienced by women exposed to the continence intervention compared to women exposed to the sham treatment 1 year postintervention. Falls will be measured as the group rate of falls at 12 months; the proportion of women who had a fall; and the proportion of injurious falls. Falls will be defined as an unexpected event in which the person comes to rest on the ground, floor or lower level  [36]. Data for falls will be obtained from a falls diary with telephone reminders  [37]. Previous work has shown that falls diaries pose significant advantage over other methods of capturing falls among older-aged people  [37].
e. , 0. 2-0. 49 is small, 0. 5-0. 79 is moderate, and 0. 8 and above is large). Multivariate linear models will be used to compare each I-QOL subscales score reported by the intervention group to the control group, with the control group as the referent and participants clustered by organization. Age at baseline and other potential confounders will be included as covariates in all adjusted models. Fall rates in the intervention and the control groups will be analyzed as follow: (1) the incidence rate of falls, using Poisson regression to allow for clustering of falls by the same participant, (2) the proportion of patients who had one or more falls versus no falls, using logistic regression analyses, and (3) the proportion of injurious falls, using logistic regression analyses. Each analysis will use patient-level data that will be clustered by organization and analyzed via GEE with an exchangeable correlation structure and a log and a logit link for Poisson and logistic regression analyses respectively.
Analysis of the qualitative sub-study on stigma The interview data on stigma will be analyzed using the principles of content description. The goal of content analysis is to generate a group of concepts that describes primary research questions. All text that addresses the fundamental research questions will be coded, whereby relevant text will be assigned a "heading" in the margin of the transcript. Headings will be reviewed, and categories of headings created. In the final step of content analysis, investigators will identify meaningful patterns within and across interview transcripts, and prepare general descriptors of the data. For this step, we will upload the data into ATLAS-ti, a software program designed to aid in the analysis of large bodies of text.
Economic analysis In this analysis we will assess the cost of the continence promotion program for senior women with incontinence compared to the sham control group. The study will involve constructing a decision analytic model through which we will estimate the cost-effectiveness and costutility of the intervention. This will take the form of a decision tree and will be based directly on the prevalence for the global rating of improvement (as "much better" or "very much better") in urinary incontinence as the outcome (Fig. 3 ). Each of the branches in the decision tree represents a set of actions or strategies under assessment and will be populated with data on probability, costs and utilities obtained from data collection in the study. The decision tree remains identical for every option in the model, although probabilities change.
Data and parameter estimates specific to Canada will be used to calculate the ICERs, first from a societal perspective and then from a healthcare system perspective. ICERs for the healthcare system perspective as base case will be of interest to site-specific health policy makers for resource allocation decisions, should the continence promotion intervention be found effective. Critically, the ICER from a societal perspective will facilitate discourse on the full opportunity cost of individual pad use in the context of the selected site. The results of the CEA will be expressed as cost per percentage improvement in the prevalence of urinary incontinence as measured by PGI-I and the results of the CUA will be expressed as cost per QALY gained. The ICER from the CEA will be comparable with other continence intervention programs using the same outcome measure (improvement in urinary incontinence), while QALYs from the CUA is a more standardized measure (not specific to the outcome measure) and will be comparable more broadly.
Acknowledgments The study is funded by the  European Research Area on Ageing2  ( ERA-AGE2 ) program, with joint contributions from the  Canadian Institutes of Health Research , the  Fonds de la Recherche en Santé du Québec , the  Economic and Social Research Council  of the UK, the  Institut National de Prévention et Éducation pour la Santé de la France , and the  Agence Regional de Santé Publique de Poitou-Charentes.
Sample The study was based on the data from the prospective national representative SIP (Santé et Itinéraire Professionnel) survey, performed by the French Ministries of Labour and Health (DARES and DREES), the French Centre for Employment Studies (CEE) and the French National Institute for Statistics and Economic Studies (INSEE). This survey was designed to explore the complex associations between work and health  [10]. In 2006, households were randomly selected from the 1999 census, that was updated for new housings, and one individual aged between 20 to 74 years was randomly selected to be interviewed in each household. Finally, 13648 men and women from the general French population were interviewed by a trained interviewer at respondent's home. The participation rate was 76%. Four years later, they were contacted again for the second wave of the survey, and 11016 individuals participated (i.
e. a follow-up rate of 81%). Among them, 5116 were working in both 2006 and 2010. Workers having a diagnostic of major depressive disorder (MDD) and/or generalized anxiety disorder (GAD) in 2006 were excluded from the study, i. e. 399 workers. Thus, the study sample included 4717 workers, 2389 men and 2328 women, free of both disorders in 2006 and who were working and followed up from 2006 to 2010. Four studies by our team have already been published using these data  [11] [12] [13] [14]. One of these studies explored the associations between psychosocial work factors and depression and anxiety using the 2006 cross-sectional data of the SIP survey. The present study is thus an attempt to improve our knowledge of these associations using the prospective data of the SIP survey. The SIP survey was approved by the French Ethics Committees (CNIL and CNIS).
The analysis of the frequency of exposure was done using the frequency variables as continuous variables and trend tests were performed to explore potential linear associations between frequency of exposure and outcome. The analysis of repeated exposure to occupational factors was performed using the exposures of 2006 and 2010 (binary variables), and an interaction term between these two exposures was added to test whether the effect of exposure in 2010 was the same or different according to the values of the exposure in 2006. Sensitivity analyses were performed including additional covariates that were: employment variables (economic activity of the company, public/private sector, employee/ self-employed worker status), overcommitment at work as a personality factor, job change and unemployment/ inactivity periods between the two data collections in 2006 and 2010, health status variables (long-standing illness, chronic diseases, work injury, disability, body mass index) and life events between 2006 and 2010.
Strengths and limitations The strengths of the study may be underlined. The study was based on a large national representative sample of the French population (i. e. not a specific or selected population of workers) with satisfactory response and follow-up rates. A comparison was performed between respondents and non-respondents in 2010 among the whole sample of workers in 2006 and showed that nonrespondents were more likely to be younger and without partner. As weighted data (taking amongst others, age and size of household, into account as calibration variables) were used in all analyses, we controlled for potential biases related to non-response and attrition and the results may be generalized to the whole national population. We found gender differences in the prevalence of mental disorders, occupational factors and covariates and we examined potential gender differences in the associations between occupational factors and mental disorders.
Background In industrialized countries, stroke constitutes the leading cause of adult handicap, the second leading cause of dementia, and the third leading cause of death  [1]. This impact on public health explains the ongoing development of basic research (using genetics, imaging, biomarkers, etc. ) and clinical studies (on stroke prevention, acutephase treatments, long-term outcomes, etc. ). Research in this field is very active, as evidenced by the large number of studies registered on the ClinicalTrials. gov website. This type of study can only be performed if patients have provided their written, informed consent to participation. Even though the legislation in most countries includes some exceptions for obtaining consent to participation in clinical research on stroke (particularly as part of emergency care), informed consent remains the rule in most situations  [2, 3].
Data collected and definitions Each patient's demographic data, baseline characteristics, clinical information, examination data, final vascular diagnosis, medications, and follow-up information were collected using a structured questionnaire. All patients underwent an initial standardized evaluation, including their medical history, a physical examination, a routine blood biochemistry screen, and diagnostic testing. Fasted blood samples were collected on admission for lipid profile evaluation. Brain imaging (either MRI or a default CT scan) was performed immediately after sample collection. A 12-lead electrocardiogram (ECG), Doppler ultrasound examination of the neck, and transthoracic echocardiography were always performed. Other diagnostic procedures (such as magnetic resonance angiography, transcranial Doppler, 24-hour ECG monitoring, and digitized angiography) were performed in selected patients.
These must be considered as to ensure a fair distribution of mental health care within resources  [10]. The Colorado study was one of the first attempts to use epidemiological surveys to plan mental health care resources  [11]. In this study, various levels of mental health and psychiatric needs were evaluated using epidemiological survey data, allowing the authors to develop a model based on socio-demographic data. They then employed it to diverse area levels, where such data are routinely available. However, although this study initiated an array of new resourceplanning strategies, the authors did not quantitatively validate their model nor did they provide the extent of prediction variability, hence making it difficult to assess its applicability. Other population characteristics, namely population density, may also affect mental health and mental health service usage  [12].
Variables that did not add significant prediction to the model (p<0. 20) were then eliminated in a stepwise, backward-selection procedure  [29]. The resulting multivariate models were used to predict needs for each department. A validation of our sample was then attempted using data available from the 2007 census conducted by INSEE. Departmental statistics were obtained on each of the Socio-Demographic variables, whereupon prediction of healthcare needs were calculated for each department based on the parameter estimates of the multivariable logistic regression model. All variables in the sample validation analyses were adapted to correspond to available INSEE data. Some variables used in the selection models are not reported in their original scales, rather in the scales of INSEE variables.
Second, we used post-stratification weighting in order to represent the entire French population and not individual departments. This could have affected variance estimates of our models and hence reduced prediction accuracy. Furthermore, our modelling strategy was somewhat atypical -constructing a predictive model with epidemiological data and then applying it to census data with missing information on mental health needs. Indeed, a crossvalidation approach or "internal validation" could have been helpful in this context, whereby a randomly-selected subset of the data is used to construct the initial model and other randomly-selected subsets thereafter are used to validate the model. "External validation", in which our model is applied to another epidemiological database containing the same parameters, would have also been immensely helpful, yet unfortunately; there were no databases of this kind at our disposition.
Finally, there are a number of more technical models that could help refine predictive capacity. For instance, "boosting" algorithms can create a series of subdatasets aimed to reduce model error, especially for observations that are difficult to predict  [34]. Future research should aim to validate our findings and to predict mental health needs with more complex models, while keeping in mind the profound differences observed between high-and lowdensity regions. In this study, we have constructed a model that can be used for allocation of mental health resource by predicting MHN from various social indicators generated in population survey. This model yields high predictability of MHN in HD departments. However, this does not always reflect the actual usage of the mental health resources  [5].
ACKNOWLEDGEMENTS AND FUNDING This study was funded by the  Direction Générale de la Santé (DGS)  and  Direction des Hôpitaux et de l'Organisation des Services (DHOS) , the  French Ministry of Health , and by the  Lorraine ,  Rhone Alpes ,  Ile de France ,  Haute Normandie  regional authorities (DRASS). Data was collected by  Ipsos, France. The studied have been approved by  CNIL (Commission Nationale Information Liberté ).
Methods Daily monitoring of patients and their PVC was carried out by the infection control team during a two months period in a cardiology department. The monitoring data collected were of clinical signs and symptoms as well as of mechanical events. After the selection of an incident, a root cause analysis was performed by Alarm method following the classic plan: chronology of care until the AE occurrence, identification with the nursing team of the immediate causes and of the contributing factors (patient, tasks, individual, team, environment, organization, institution), and action plan.
Cells were acquired on a Fortessa cytometer (Becton Dickinson), and analyzed using the FlowJo software. Cytokine production was analyzed in total lung cells. For this, 5 × 10 5 lung cells were seeded on 96-well plates coated or not with anti-CD3 Ab (eBiosciences). Forty-eight hours later, supernatants were collected and analyzed for IFN-γ, IL-17, and IL-22 concentration by ELISA (R&D Systems).
GAPDH F TGCCCAGAACATCATCCCTG R TCAGATCCACGACGGACACA DefB2 F AAAGTATTGGATACGAAGCAGAACTTG R GGAGGACAAATGGCTCTGACA DefB3 F TGAGGAAAGGAGGCAGATGCT R GGAACTCCACAACTGCCAATC Cathelicidin F CAGAGCGGCAGCTACCTGAG R TCACCACCCCCTGTTCCTT s100a9 F CACCCTGAGCAAGAAGGAAT R TGTCATTTATGAGGGCTTCATTT Il-1b F TCCCCAACTGGTACATCAGCA R ACACGGATTCCATGGTGAAGTC Il-6 F AGCCTCCGACTTGTGAAGTG R CTGATGCTGGTGACAACCAC Il-12p40 F GACCCTGCCCATTGAACTGGC R CAACGTTGCATCCTAGGATCG Il-23p19 F CACCAGCGGGACATATGAA R CCTTGTGGGTCACAACCAT To investigate mechanisms involved in enhanced susceptibility to pneumococcal infection in CS-exposed mice, IL-17 and IL-22 production was quantified. Challenge with Sp significantly enhanced IL-17 and IL-22 levels in BAL (Fig. 2a ) and lung lysates (data not shown) of air mice. In marked contrast, Sp infection failed to do so in mice previously exposed to CS (Fig.
COPD patients showed impaired Th17 cytokine production in response to S. pneumoniae To evaluate whether COPD status modulates the response to Sp, PBMCs were isolated from 3 different groups: non-smoker healthy controls, smokers and COPD patients, and were stimulated with Sp. Levels of IL-17 and IL-22 were evaluated in PBMC supernatants. Resting PBMC exhibited similar levels of cytokines in the three groups (Fig. 5a ). Exposure to Sp expectedly increased IL-22 and, to a lesser extent, IL-17 production in the non-smoker and smoker groups, but had no effect on cytokine production in COPD patients. The response to PHA was also partially altered in COPD patients, in contrast to the other two groups (data not shown). We next looked at the cellular sources of IL-17 and IL-22, focusing on conventional T cells, NK cells, NKT cells, γδ T cells, mucosal-associated invariant T (MAIT) cells and Lineage-negative cells (Fig.