dataesr/scientific-paragraphs-categorization huggingface dataset:
Recruitment of Respondents No health or personal data was collected as the survey items were designed to avoid the collection of sensitive or personally identifiable information. Participants were considered eligible to participate in this survey study if they were 18 years old or older, and currently screened or enrolled in a clinical study. Former clinical research participants were also considered eligible. No quota-based sampling was performed. A total of 11,695 members (8,386 women, 3,309 men) of a clinical research participant database (owned by Biofortis) from the Nantes region in France were contacted by email in May 2019 to read an information sheet and complete this anonymous questionnaire. The entire panel was contacted. The participating site, specialized in the conduct of nutrition clinical studies, was selected based on the research team's interest in the survey study and their ability to recruit a large sample of respondents from various age ranges and conditions, including healthy volunteers.
Statistical Analyses Each item of the survey was described using frequency and percentage overall and by age classes. For each item, a global proportion comparison was performed between the age classes using the chi-square test, or the Fisher-exact test if any expected cell count was inferior to 5 as an alternative to the  chi-square test. In case of global effect, pairwise proportion comparisons between age classes were completed using chisquare test or Fisher-exact test accordingly. The results of comparisons were adjusted to account for multiple comparisons according to the Bonferroni-Holm method. All analyses were performed using SAS v9. 4 R. A margin error of 5% was used for statistical tests.
Study Participants All 11,695 members of the clinical research participants database (8,386 women, 3,309 men) were contacted to complete our survey. Among the 1,529 respondents who completed the survey, 81. 0% were female (Figure  1 ). A share of 14. 77% of women accepted to complete the survey (1239/8386), while 8. 76% of men (290/3309) completed it. Most of respondents were aged 25-54 years old (64. 9%) and lived in medium-size (10,000-50,000 inhabitants) and large cities (>50,000 inhabitants) (28. 6 and 40. 0%, respectively). While 35. 4% of them had received primary education, 15. 0% reached third level education. However, 11. 1% declared having not received any formal education (Table  1 ).
While this survey mushroomed into an important dataset, comparison with prior work is however limited, as we could not identify prior research findings related to expectations toward digital heals and mobile dietary assessment tools obtained in samples of clinical research participants. A main limitation of our study is the absence of quotabased sampling which would have facilitated further analyses in sub-groups of respondents. While feasible on a larger scale, the focus on clinical study participants as our study sample prevented the use of further screening efforts and of a larger sample of respondents. Ideally, variables including age, gender and chronic conditions could have been used to enable a quotabased sampling reflecting the French population characteristics. Another limitation of our study is the lack of information regarding the nature of chronic conditions.
Parents with a sick child in a neonatal intensive care unit (NICU) usually experience stress, anxiety, and vulnerability. These precarious feelings can affect early parent-child interactions and have consequences for the child's neurodevelopment. Parents who have had a sick child in an NICU (veteran parents) can offer helpful interventions for these vulnerable families. This article is a scoping review of parental interventions used with the families of NICU infants, and an overview of French perspectives. Two independent reviewers studied the scientific literature published in English between 2001 to 2021 using Covidence software. The databases used were MEDLINE, ISI Web of Science, the Cochrane Database, and Google Scholar. Themes were identified from the articles' results using an open coding approach.
• Scoping review: A scoping review is an ideal tool for the first step in understanding the literature in an innovative research field  [27, 28]. As recommended, we followed the PRISMA-sR guide  [29]  and the JBI's guidance for a scoping review methodology  [29]. This review was then carried out by following five steps: (1) identifying research questions, (2) identifying relevant studies, (3) screening the studies, (4) obtaining data extraction and synthesis, and (5) preparing the presentation of the results.
• Identification of relevant studies Reviewed studies were published from 2011 to 2021, representing a ten-year overview, and corresponded to the growing development of these practices. Inclusion criteria: Included articles were peer-reviewed, written in English, and focused on parents' partnership with healthcare providers in different types of NICU activities: clinical care, peer support, research, administration, and teaching. Exclusion criteria: Studies of parents' participation during the hospitalization of their child were excluded, as well as studies about family-integrated care, parents' perspectives or opinion, bereaved parents, and studies on partnership in pediatric or adult population. Publications before 2010 were also excluded. The electronic databases searched were MEDLINE, ISI Web of Science, the Cochrane Database, and Google Scholar.
Bibliographies of all selected articles were checked, and the following Medical Subject Headings (MeSH) terms were used: "resource parents", "veteran parents", "peer-to-peer", "peer support groups", "peer counselling", "partnership with parents", "parents' engagement", and "family stakeholders". These MeSH terms were combined with: "NICU", "newborn", "neonate", and "preterm babies". Two reviewers independently selected the articles using Covidence software. Themes from the selected articles were identified using an open coding approach  [30]. The first and last author (SD, BT) exhaustively read the articles in their entirety, paying special attention to the description of the partnership interventions, recommendations, impacts, and barriers.
Study population Data were provided by the French Coronary Heart Disease population-based registries from three administrative regions: Bas-Rhin, Urban Community of Lille, and Haute-Garonne. All fatal Acute Coronary Events (ACE) occurring between January 1, 20 0 0 and December 31, 2016 were recorded according to the WHO MONICA initial protocol, for subjects aged 35-74 years living in one of the three geographical areas covered by the registries. These registries cover approximately five hundred thousand inhabitants belonging to the 35-74 age group. These registries constitute the national network of registries subordinate to the national epidemiological surveillance agency. Their methodology is validated and approved by the National Registry Committee and is used for official national statistics in France (Santé Publique France).
Data sources and classification of events Diagnostic criteria and coding rules are based on the WHO MONICA Manual  [14]  , extended and updated to comply with most recent definitions of acute coronary event. All deaths (either inor out-of-hospital) among the 35-74 year population are identified and selected for validation from a list of diagnoses recorded on the death certificates, according to the International Classification of Diseases, ninth or tenth revision (ICD9-10). This list includes all fatal events with an ischemic cardiac cause (ICD9 codes 410-414, ICD10 codes I21; I22, I23, I24, I25) whether as underlying, immediate or associated cause. Also, other fatal events from cardiovascular cause such as ICD9 codes 415, 425-429, 430-438, 4 40-4 4 4, ICD10 codes I27, I44-52, I64, I70-79; or other fatal events with no precise cause of death such as ICD9 codes 798 and 999 or ICD10 codes R95-99, whether as underlying or immediate cause, are selected for further investigation.
Details on circumstances of outof-hospital deaths, including nature of symptoms (cardiac arrest, thoracic pain or other symptoms evocative of a coronary event) and time elapsed between first symptoms and death is then obtained with interviews with the physician who signed the death certificate and, if necessary and possible, with the patient's general practitioner or cardiologist. Emergency medical service records and autopsy reports are also checked when available. Existence of previous CHD is checked in the MONICA database and GPs are specifically interviewed for further investigations when needed. Inhospital fatal events are thoroughly investigated by means of the hospital medical records. These events may also be found with the continuous verification of all discharge diagnoses in the cardiology and emergency departments of the three geographical areas.
Finally, hospital database systems on hospitalizations with an acute coronary event occurring in all hospitals regardless of the department are annually screened for event completion. After thorough research, each death is classified in a diagnostic category: -Myocardial Infarction (MI). The fatal event occurred during the 28 days following MI definitely diagnosed as such, whether clinically or at autopsy. -Coronary death. Fatal event, whether sudden or not, for which there is no good evidence of another cause of death (clinically or at autopsy) and with typical or atypical symptoms, or with a history of chronic CHD, or with evidence of chronic coronary occlusion or stenosis or old MI at necropsy. -Possible coronary death. Rapid death in less than 24 hours, with no history of typical or atypical symptoms, no history of chronic CHD, no necropsy and with no other diagnosis.
-Death with insufficient data. The cause of death remains unknown and/or the time until death could not be determined. As previously, it is a death with no history of typical or atypical symptoms, no history of chronic CHD, no necropsy and with no other diagnosis. -Non-coronary death. For this study, only events characterized in the MONICA protocol as fatal myocardial infarctions, coronary deaths or possible coronary deaths are included. Deaths with insufficient data or noncoronary death are excluded, such as deaths for which location cannot be clearly identified ( N = 45). The definition of in-and out-of-hospital deaths is based on the place where the death occurred. Out-of-hospital deaths are deaths occurring at home (including non-medicalized departments such as retirement homes, psychiatric or rehabilitation institutions), in public places or in an ambulance.
7. 0. 0, February 2019; Statistical Methodology and Applications Branch, Surveillance Research Program, National Cancer Institute, Bethesda, MA, USA)  [16]  and expressed as annual percentage changes (APC). We considered the mortality rate as the dependent variable and the calendar year as the independent variable. The 95% confidence intervals of APCs were calculated using the standard error. Finally, we used loglinear Poisson regression models to test interactions between year, sex, place of death and age. P values for trends and interaction significance were 2-tailed. Their significance threshold set was set at 0. 05. Analyses were performed using SAS 9. 4 and Joinpoint 4. 7. 0. 0 software (National Cancer Institute, Statistical, applications Branch).
Methodological aspects This study has several strengths. It provides recent, comprehensive data on CHD mortality over a seventeen-year period, with a total headcount of more than 20,0 0 0 fatal events recorded by three French MONICA registries from distinct geographical areas: North, East and South-West. These registries, which have collected data on ACEs since the 1980s, have a high degree of case ascertainment and comprehensiveness, based on standardized data collection and multiple data sources: hospital charts, data from emergency departments, death certificates and interviews with practitioners. All subjects aged 35-74 and living in one of the three areas covered by the registries are included, regardless of where death occurred. Moreover, data collection is standardized, and criteria for the definition of fatal events remained unchanged over the study period (regardless of   In-hospital -4.
5 ( -4. 9 to -4. 1) < 10 -5 -4. 3 ( -4. 9 to -3. 7) < 10 -5 -5. 0 ( -6. 0 to -4. 0) < 10 -5 Out-of-hospital -2. 9 ( -3. 4 to -2. 4) < 10 -5 -2. 9 ( -3. 4 to -2. 4) < 10 -5 -3. 2 ( -3. 9 to -2. 4) < 10 changes in international classification codes in death certificates or evolutions in ACE definitions), thus it unlikely that this may have affected mortality rates. The major input of this study is its focus on the respective contributions of in-and out-of-hospital mortality to overall ACE mortality. Recent data on out-of-hospital mortality (including patients who had never been hospitalized for coronary events) are currently lacking. Population-based registries are run to record comprehensive data on all fatal events, wherever the death occurs. Hospital data, obtained from medico-administrative information systems or intensive care databases are basically focused on in-hospital events in France.
Vocalization corpus We built a corpus of recordings of infant-directed song, infant-directed speech, adult-directed song, and adult-directed speech. Participants (N = 411) living in 21 societies (Figure  1  and Table  1 ) produced each of these vocalizations, respectively, with a median of 15 participants per society (range: 6-57). From those participants for whom information was available, most were female (86%) and nearly all were parents and/or grandparents (95%). Recordings were collected by principal investigators and/or staff at their field sites, all using the same data collection protocol. They translated instructions to the native language of the participants, following the standard research practices at each site. For infant-directed song and infant-directed speech, participants sang or spoke to their infant as if they were fussy, where "fussy" could refer to anything from frowning or mild whimpering to a full tantrum (note that each language had its own word for "fussy", suggesting that participants had an intuitive understanding of it).
We then used the silence detection tool in Praat 66 , with minimum sounding intervals at 0. 1 seconds and minimum silent intervals at 0. 3 seconds, to remove all portions of the audio where the participant was not speaking (i. e. , the silence between vocalization phrases). These were manually concatenated in Python, producing denoised recordings, which were subsequently checked manually to ensure minimal loss of content. Further details of the acoustic analyses are in the Supplementary Information.
We did all this with half the corpus, weighted by participant. In the course of the exploratory analyses, we noted a small number of extreme outliers, typically attributable to anomalies in the recording environment (e. g. , loud wind). As such, before running confirmatory analyses, we Winsorized all features at the lowest and highest 5 percentile ranks, and also restricted the set of features analyzed to those less sensitive to extreme observations (e. g. , using the median as a measure of central tendency rather than the mean). These data were used for all subsequent analyses. This decision had no impact on the interpretation of results, but is preferable to trimming extreme values  73  ; an alternate method, imputing extreme values with the mean observation for each feature, yielded comparable results.
Convergent analysis: Predicting listener intuitions from acoustic features Last, we examined the degree to which the naïve listener's perceptions of infant-directedness were explicable from the primary acoustic features identified by the exploratory-confirmatory and hypothesis-free analyses of the corpus. To reduce the risk of introducing false-positives in a large dataset, we only analyzed acoustic features that had convergent evidence from at least one summary statistic in both the exploratoryconfirmatory and LASSO analyses, in at least one comparison type. In these analyses, we collapsed across all vocalization types and attempted to predict only whether naïve listeners rated a given vocalization as infant-or adult-directed (regardless of society or vocalization type). This yielded 21 features.
Acknowledgments We thank the participants and their families for providing recordings;  D. Amir , who sparked the idea for this research in conversation with S. A. M. at the 2016 Annual Conference of the  Human Behavior & Evolution Society ;  J. Du ,  E. Pillsworth ,  L. Sugiyama ,  P. Wiessner , and  J. Ziker , who collected or attempted to collect additional recordings;  A. Bergson ,  Z. Jurewicz ,  D. Li ,  L. Lopez , and  E. Radytė  for research assistance; and  M. Bertolo ,  J. Kominsky , and  L. Yurdum  for feedback on the manuscript.
M. , C. D. P. ); the  National Institute of Allergy and Infectious Diseases  Award  R15-AI128714-01  (P. M. ); the  Max Planck Institute for Evolutionary Anthropology  (C. T. R. ); a  British Academy Research  Fellowship and Grant  SRG-171409  (G. D. S. ); the  Institute for Advanced Study in Toulouse , under an  Agence nationale de la recherche  grant,  Investissements d'Avenir   ANR-17-EURE-0010  ( J. Stieglitz ); and the  Natural Sciences and Engineering Research Council of Canada  (S. E. T. ).
Methods: The study was nested within a cluster-randomized controlled trial that was conducted to evaluate the effectiveness of the promotion, with before and after observation (NCT03565562). Data from questionnaires, observations, and institutional sources were collected in 27 localities where SB was implemented. A multiple correspondence analysis was performed to assess the relations between context, type of implementation and promotion, and use of the tool. Results: Three distinct promotion patterns emerged according to the profiles of the localities that were associated with specific SB utilization rates. From highest to lowest utilization rates, they are listed as follows: the privileged urban localities, investing in health that implemented a high-intensity and digital promotion, demonstrating a greater capacity to take ownership of the project; the urban, but less privileged localities that, in spite of having relatively little experience in health policy implementation, managed to implement a traditional and high-intensity promotion; and the rural localities, with little experience in addressing health issues, that implemented low-intensity promotion but could not overcome the challenges associated with their local context.
Characteristics of the Localities Considered All the characteristics included in the analysis were based on a review of the theoretical and empirical literature. Data collection included socioeconomic and demographic data from institutional sources, observations and discussions from the delegates' meetings, questionnaires, and web data extractions from SB analytic tools via Google Analytics (GA). The characteristics were divided into three sections: 1. The characteristics that were based on the pre-existing context. 2. The characteristics that derived from the program itself, its promotion, and implementation. 3. The utilization rate corresponds to the outcome variable of the promotion. For the purpose of the MCA, these characteristics, detailed below, were transformed into a set of categorical variables.
The Outcome of the Promotion: Utilization Rate of Stopblues We assessed the effectiveness of the promotion of SB through SB usage frequency using GA during the two-year experimentation period. Indeed, previous studies have shown that information from GA can be used to evaluate promotional campaigns, determine the geographic distributions of users, and analyze their use of online tools  [55] [56] [57]. Data were extracted at month 24 directly from GA. This provided general data regarding the users: number (active and new) and location (country and city). For the purpose of the analysis, the utilization rate variable was categorized as follows: low (< 25 per 100,000 residents or number of active users < 10), medium (between ≥ 25 and < 50 per 100,000 residents or number of active users < 20), and high (≥ 50 per 100,000 residents and number of active users ≥ 45).
Acknowledgments The authors gratefully acknowledge the financial support of the  French National Public Health Agency , which also provided the funding for the  PRINTEMPS  project (which this work is a part of) through a grant from the 2014 prevention call for research proposals of the  French Research Institute in Public Health  (grant  026/14 ). Additional funding was received from the  European Commission  through the  Interreg North-West Europe. The authors also wish to express their gratitude to all the people and their organizations who participated in the conduct of the project, notably all the delegates from the localities included in the project, the  French Ministries of Health and of Research , the  French National Health Insurance Fund for Employees , the  Arc Foundation for Cancer Research , the  French National Cancer Institute , and  INSERM.
| METHODS A meta-review (a systematic review of reviews) was undertaken to comprehensively summarize the evidence with more than one review on an important topic. 18 In this meta-review, any type of review was referred to as 'review', while studies within these 'reviews' were referred to as 'primary studies'. The meta-review adhered to the Preferred Reporting Items for Systematic Reviews and Metaanalyses (PRISMA) Statement  19, 20  and was registered in the PROS-PERO database (CRD42021227379). Guidance from the Joanna Briggs Institute informed the development, conduct and reporting of this meta-review. 21 y reviews that reported findings from primary studies on the needs of care partners of people with progressive cognitive disorders or NDD such as Alzheimer's, Parkinson's, motor neurone, cerebellar degenerations, Huntington's, and prion disorders were included (see Appendix 1, Table  A1  for full search strategy).
| Data search and extraction A qualified medical librarian searched the international literature in January 2021 through Embase, Medline, CINAHL, PsychINFO and Epistemonikos 98 databases. Thorough hand searching of identified articles' reference lists supplemented the database searching. The search was guided by search terms and derivatives related to "literature reviews," care partner needs," cognitive disorders", and "neurodegenerative disorders". Search results were imported into EndNote X7 software, duplicates removed, and imported into Covidence online software. We conducted data extraction in two stages. In stage 1, we extracted the needs and differences in needs (RQ1 and RQ2 accordingly). After completing stage 1 extraction and identifying the types of NDD, we conducted stage 2 extraction for condition-related needs assessment tools and the themes of needs captured by these tools.
We then cross matched the needs derived from the narrative synthesis in stage 1 against the needs captured by the needs assessment tools (RQ3). Two investigators (LE, IK) independently applied the eligibility criteria outlined above and documented reasons for exclusion. Any discrepancies flagged through Covidence were resolved through discussion. Once all included articles were identified, one investigator (LE) extracted data from each study into an Excel V15. 28 spreadsheet on publication year, region and country of origin, review type, NDD, progression, socio-demographic characteristics, results, and authors conclusion. YC, EA, AS, and A-SA conducted secondary data extraction for individually allocated variables. The needs assessment tools were sourced from the most recent psychometric systematic review by Kipfer S.