dataesr/scientific-paragraphs-categorization huggingface dataset:
Age and sex were available in the data for egos and alters, while education level was only provided for egos. Therefore, it was assumed that an ego's work contacts had the same education level based on their occupation, and that an ego's home contacts had the same education level as an indicator of household socioeconomic status. To represent communities with different SES compositions, we resampled additional low education egos from the low education sample in the POLYMOD dataset. These networks allow us to examine how epidemic dynamics might differ in populations with different proportions of low SES individuals in the population (e. g. capturing the SES variability observed in the United States). We produce networks composed of approximately 20-60% low education individuals (S3 Table ).
The model was fit using the ERGM package  [47, 48]. The best model was selected based on multicollinearity criteria and goodness of fit to the POLYMOD data. The best model observed data was the egos and their alter contacts. Model terms included edges, node attributes for sex, age, school/work, and education, and homophily for age, home, school/work, and education. From the best fit ERGM model, we simulated 10 networks. Additional model details (S1 Appendix), model terms (S1 Table ), multicollinearity measures (S2 Table ), model diagnostics, and goodness of fit measures (S1-S26 Figs) are available. We highlight that we have selected the best fit model, balancing the fit of each incorporated model term. Thus, while each individual attribute may not be an exact fit, this model best captures the main characteristics of each attribute.
Random regular networks of the same size and mean degree were also generated as null networks to evaluate the effect of contact heterogeneity. We used the Networkx package for network generation and analysis  [49]. SES-based epidemiological model. Chain binomial SEIR (Susceptible-Exposed-Infected-Recovered) simulations were performed on the networks generated by the egocentric ERGM model and the random control networks to examine the spread of a respiratory infection, like influenza, through a naive population. Model parameters pertinent to seasonal influenza spread were selected from literature (S4 Table)  [50, 51]. Five hypothesized drivers for increased influenza in low SES populations were integrated into the epidemiological simulations. Each hypothesized driver represents a social or healthbased factor.
Modelling surveillance of disease in low SES populations To achieve an inferential understanding, we (a) integrated the network model findings with empirical ILI data for an estimate of ILI burden among low SES individuals; and (b) fitted a spatial Bayesian hierarchical model with population-level covariates to account for measurement biases and improve our estimate of low SES ILI burden at the population-level. Spatial inferential model. We used a Bayesian spatial hierarchical model to estimate latent ILI cases among low SES individuals. This is an N-mixture model, which accounts for imperfect detection of low SES ILI cases through a measurement process, as well as borrowing information from county-level factors associated with influenza in low SES populations. The goals of this model are to estimate cases of ILI in low SES populations in counties across the United States accounting for measurement processes and data on SES-based social and healthcare differences, and to identify the relationship between the hypothesized drivers of inequities and low SES ILI at the population level.
We modeled low SES ILI (Y it ) in county i in flu season t as: Y it jN i � BinomialðN i ; p i;t Þ where p i,t is the probability of detecting low SES ILI cases, and N i is the true ILI cases among low SES individuals. We modeled the probability of detection p i,t as: logitðp i;t Þ ¼ a 0 þ X k 1 a k z i;t;k þ n c þ n s where α 0 is the intercept, α k represents the coefficient estimate for the k measurement process predictor variables, z i,t,k (here, physicians in database and low SES population size), and ν c and ν s are group effects for county and state, respectively. We modeled the latent low SES ILI cases as: N i � NegBinðl i ; yÞ where the negative binomial distribution is parameterized by probability λ i and size θ. The λ i is modeled by: logðl i Þ ¼ b 0 þ X j 1 b j x i;j þ m c þ m s where β 0 is the intercept, β j represents coefficient estimates for the j low SES ILI process covariates, x i,j (here, variables that capture each of the hypothesized mechanisms-susceptibility, social contact differences, absenteeism, vaccination, and healthcare access-in low SES populations), and μ c and μ s represent county-level and state-level group effects, respectively.
Response data. We define the response in our model to be the observed influenza-like illness (ILI) burden in low SES populations. In particular, we use influenza-like illness reports from a medical claims database from across the United States collected during 2002-2008. Additional details on the dataset can be found in  [31, 35]. To normalize these observed counts, we take the ratio of ILI visits for every 1000 visits for any diagnosis during the influenza season. We refer to this value as an incidence ratio. These data are at the county-level but are not stratified by SES. To produce a county-level estimate of ILI in low SES populations for our spatial model, we use the observed ILI burden in the total population and scale this by the proportion expected among low SES individuals as predicted by the epidemiological model from the first part of our study (as summarized in Fig 2B ).
First, covariate data was included for the measurement submodel to characterize database coverage and population size. For database coverage, we used the number of physicians reporting to the medical claims database, which was reported by the database and averaged over reported years. Additionally, the population of low SES individuals was included since that measures the size of the considered population. Low SES population size was measured as the county population size, reported by the US Census Bureau  [57] , multiplied by the percent of the population with less than a high school education from County Health Rankings  [58]. Then, for the process model, covariate data were included as a marker for each hypothesized driver of low SES influenza. We ensured that all process covariate data pertained just to low SES populations.
(EPS) S34 Fig. Low SES beta sensitivity analysis. Proportion of epidemic size that is composed of low SES individuals for possible low SES beta (representing increased suscpetibility) where the low SES beta is randomly distributed (left of pair, light green), compared to where the low SES beta applies to low SES individuals (right of pair, dark green). For all values where low SES individuals are more susceptible than high SES individuals (black dashed line), low SES individuals are increasingly infected. The low SES beta shown in the main text is shown with the black dotted line. (EPS) S35 Fig. Medical claims data ILI cases, total visits, incidence ratio observed and modeled by county low SES population. A) ILI case counts decrease as county low SES population increases. B) Total visits in the medical claims database decrease as county low SES population increases.
Research Design This publication is based on an ethnographic study conducted in  France in the 2000s, first in 2002 France in the 2000s, first in , then between 2006 France in the 2000s, first in & 2009 , and completed at the end of the 2010s. It draws on empirical data from interviews and observation, and quantitative data, as well as analysis of secondary documentation. I conducted audio-recorded semi-structured in-depth interviews with consultant coaches (n=45), human resources managers who prescribe coaching (n=16) and coached managers (n=18). As this paper focuses on the professional group of coaches, I will develop only the methodology used to collect data through the coaches. The 45 coaches were selected to constitute a diverse sample of the professional group. More than half of them were prominent coaches, founders of coaching schools and key members of the oldest professional association in France, SF Coach.
On the other hand, active sensors that allow us to perceive health states by questioning patients at a certain point regularly during the day would be a promising management to avoid the biases due to subjective factors in questionnaires. For example, we recently developed a dynamometer, linked to a smartphone, that allowed the patient with RA to self-assess the grip strength of the dominant hand. We observed an inverse correlation between the disease activity score (DAS28) and the handgrip strength exerted by the dominant hand  (14). This device is an objective measure of RA activity and appears to be useful for monitoring patients at a distance. Nevertheless, these raw data must be transformed into digital biomarkers defined as numerical physiological and behavioral measures that explain, influent or predict health states.
Interviews with three representatives of local authorities were further undertaken to triangulate our data. We conducted structured face-to-face interviews in Malagasy to allow any person to participate, irrespective of their level of literacy. The responses were translated into French immediately after completion. The research followed the ethical code of conduct put forward by  Wilmé et al. (2016) : all participants were informed about their anonymity and prior informed consent was granted before any interview. We used the software QSR F I G U R E 1 Alaotra region with the two case study sites, Antanandava and Andreba Gare. Andilamena in the north is a prominent mining site, while mining is known to take place in the Zahamena National Park in the east of the socio-ecological system (cf. Reibelt et al.
ACKNOWLEDGEMENTS We would like to thank all participants for their time and collaboration, as well as the communities of Antanadava and  Andreba Gare  for their hospitality. A special thanks to  Madagascar Wildlife Conservation and Durrell in Ambatondrazaka  for their support. We also thank  Julia J. P. Jones  for her helpful comments on an earlier draft. Many thanks to the three anonymous reviewers who helped us improve the piece. This research was funded by the  Swiss Programme for Research on Global Issues for Development , grant number  IZ01Z0_146852.
Background: One newly proposed approach to determining eligibility of children aged 6-59 months for therapeutic feeding programs (TFPs) is to use mid-upper arm circumference (MUAC) < 115 mm, bilateral oedema or Weight-for-Age Z-score (WAZ) < -3 as admission criteria (MUAC+SWAZ). We explored potential consequences of this approach on the eligibility for treatment, as compared with the existing WHO normative guidance. We also compared sensitivity and specificity parameters of this approach for detecting wasted children to the previously described "Expanded MUAC" approach. Methods: We analyzed data from 558 population representative cross-sectional cluster surveys conducted since 2007. We retrieved all children classified as severe acute malnutrition (SAM), moderate acute malnutrition (MAM), and those who are both wasted and stunted (WA + ST), and calculated proportions of previously eligible children who would now be excluded from treatment, as well as proportions of non-malnourished children among those who would become eligible.
The WHO recommendations for MAM management focus on growth monitoring, nutritional advice and medical care  [7] , whereas the use of supplementary foods is only recommended in settings where the prevalence of wasting or food insecurity is high  [8, 9]. Current WHO case definition for SAM use low Weight-for-Height (WHZ < -3) and/or low Mid-Upper-Arm-Circumference (MUAC< 115 mm) as independent indicators, alongside nutritional oedema  [6]. For detecting children with MAM, widely accepted case definition is -3 ≤ WHZ < -2 and/or 115 mm ≤ MUAC < 125 mm  [8, 10]. It is well documented that WHZ and MUACbased criteria do not identify the same children as acutely malnourished. In order to get a quick overview of the extent of the diagnostic discrepancy, the analysis of a large dataset of cross-sectional surveys implemented in a wide range of countries and totaling around 1.
Methods Data for these analyses were obtained from Action Contre la Faim (ACF) International network, an international humanitarian non-governmental organization that conducts multiple field nutrition surveys in humanitarian settings worldwide  [23]. Surveys conducted during 2007-2018 that measured both sex, age, height, weight, oedema and mid-upper arm circumference (MUAC) in children aged 6-59 months were included. All surveys included were population representative cross-sectional two-stage cluster surveys following standard survey and sampling procedure and usually conducted at the district level  [24]. Survey countries were grouped into six geographic categories: Latin America and the Caribbean; East and South Africa; Democratic Republic of Congo (DRC); West and Central Africa; East Asia and Pacific; and South Asia  [25].
DRC was kept as its own category due to the large number of surveys from the country. Countries that had fewer than five surveys conducted during 2007-2018 were excluded from the analyses. We considered that fewer surveys would have too few cases of acute malnutrition to produce reliable per-country estimates. The Middle East and North Africa region was not included since none of the countries had five or more surveys conducted during the study period. Weight-for-height (WHZ), height-for-age (HAZ) and weight-for-age (WAZ) Z scores were calculated for all children using the WHO SAS macro, which applies the WHO 2006 growth standards  [26]. Children with missing data for age, sex, weight, height or MUAC and with age out of range (< 6. 0 months or > =60 months) were excluded. Following WHO flagging criteria, children were also excluded if they had MUAC that fell below 70 or above 220, WHZ that fell outside of + / -5 Z-scores, HAZ that fell outside of + / -6 Z-scores, and WAZ were < -6 and > + 5 Z-scores.
Acute malnutrition was defined as either by MUAC only, by WHZ only, or by both criteria (MUAC and/or WHZ). Severe malnutrition (SAM) defined by MUAC (SAMmuac) was MUAC < 115 mm and/or clinical signs of oedema, and moderate acute malnutrition (MAM) defined by MUAC (MAMmuac) was MUAC < 125 mm and > =115 mm. SAM defined by MUAC and/or WHZ (SAMall) was MUAC < 115 mm and/or WHZ < -3 and/ or clinical signs of oedema, and MAM defined by MUAC and/or WHZ (MAMall) was MUAC < 125 mm and > =115 mm and/or WHZ < -2 and > = -3, excluding those already defined as SAMall. "Wasting and stunting" status (WA + ST) was defined as WHZ < -2 and HAZ < -2. "Severe underweight" (SWAZ) status was defined as WAZ < -3. To explore and compare the consequences of a "MUAC and Severe Underweight" (MUAC+SWAZ) program and a "Expanded MUAC" (ExpMUAC) program on the targeting of various categories of malnourished children, we defined "target" and "supplement" categories of children that would be detected and treated by each of these programs.
"Target" includes all children eligible for the program. "Supplement" includes children eligible for the program in supplement to those eligible to a restricted program using only MUAC < 115 or oedema criteria. In essence, "supplement" indicates increase in program size compared to the program admitting only SAMmuac children: "Target MUAC+SWAZ"all children eligible for "MUAC and Severe Underweight" program (MUAC < 115 mm or WAZ < -3 or oedema). "Supplement MUAC+SWAZ" --children eligible for "MUAC and Severe Underweight" program except those who have MUAC < 115 mm or oedema (WAZ < -3 and MUAC > 115 mm and no oedema). "Target ExpMUAC"all children eligible for "Expanded MUAC" program (MUAC < 125 mm or oedema). "Supplement ExpMUAC" --children eligible for "Expanded MUAC" program except those who have MUAC < 115 mm or oedema (115 ≤ MUAC < 125 mm and no oedema).
To assess sensitivity of programs' inclusion criteria, we calculated proportions of SAMall and MAMall children (true positives) that would be captured by the "target" and the "supplement" in each program. Thus, sensitivity of MUAC+SWAZ program "target" and "supplement" in detecting SAMall children was calculated as: (children that satisfy criteria of both SAMall AND "Target MUAC+SWAZ")/(SAMall) and (children that satisfy criteria of both SAMall AND "Supplement MUAC+SWAZ")/(SAMall), respectively. Similarly, sensitivity of ExpMUAC program "target" and "supplement", in detecting SAMall children was calculated as: (children that satisfy criteria of both SAMall AND "Target ExpMUAC")/(SAMall) and (children that satisfy criteria of both SAMall AND "Supplement ExpMUAC)/(SAMall), respectively. Similar calculations were done to calculate the proportions of MAMall children captured by the "target" and the "supplement" for each program (replacing SAMall with MAMall0 in formulae above).
In order to assess the adequacy of the allocation of treatment resources to children needs, and as a surrogate for specificity, we calculated proportions of children categorized as SAMall and MAMall within "target" and "supplement" in each program. For example, for the MUAC+SWAZ program, proportions of children categorized as SAMall within "target" and "supplement" were calculated as: (children that satisfy criteria of both SAMall AND "Target MUAC+SWAZ")/("Target MUAC+SWAZ") and (children that satisfy criteria of both SAMall AND "Supplement MUAC+SWAZ")/("Supplement MUAC+SWAZ"), respectively. Similar calculations were done for the proportions of SAMall within ExpMUAC program "target" and "supplement", and for the proportions of chidren categorized as MAMall within "target" and "supplement" in each program.
Results Final analyses included 558 surveys from 22 countries, which included over 406,800 children aged 6-59 months (Table  1 ). Overall, 0. 4% of children were flagged for outlier values of MUAC, WHZ, HAZ, and/or WAZ. The sizes of the MUAC+SWAZ target and supplement, expressed as percentages of the 6-59 months population, are described by region and country in Table  1. As shown in Tables  1  and 2 , overall MUAC+SWAZ target approach (9. 1%) more than doubles (2. 3 times increase) therapeutic feeding program size compared to the WHO recommended target (SAMall) (4. 0%). It also roughly quadruples program size based on restricted SAMmuac target (the ratio of supplement MUAC+ SWAZ to SAMmuac of 2. 9 in Table  3  translates into an increase of 3. 9 times). This increase in the target size comes with a relatively high sensitivity in detecting SAMall (82.
8, Table  2 ), and WA + ST (82. 3%, Table  1 ). This high sensitivity however is attained at the expense of specificity: 63. 3% of the target is not SAM (including 35. 6% of the target being MAM and 27. 7% not wasted at all, Table  2 ) and 35. 8% of the target is neither SAM nor WA + ST (Table  1 ). When considering the supplementary population of children (supplement MUAC+SWAZ) who would be selected to therapeutic feeding program when using WAZ < -3 as an independent indicator in addition to SAMmuac, our analysis shows that almost half of them (48. 2%) would be neither SAM nor WA + ST (Table  1 , last column). Tables  2  and 3  present comparisons of increases in program size, sensitivity in detecting acute malnutrition, and composition of the targets for MUAC+SWAZ (left side of the tables) and ExpMUAC (right side of the tables) approaches.
Elle distingue par conséquent deux catégories d' emploi, la seconde plus spécialisée que la première. Il est tout à fait remarquable que le terme infox ait rencontré très vite un large écho favorable, phénomène relativement rare en ce qui concerne les termes « officiels ». Ce mot-valise néologique formé à partir d' éléments de « information » et « intoxication », avec la voyelle -o-comme seul élément commun, a séduit sans doute par son évocation de intox 8 , paronyme que l' on peut facilement considérer comme synonyme, sa brièveté et sa formation d' allure moderne, malgré la morphologie un peu inhabituelle de son second élément -ox-qui ne correspond à aucun morphème ni même à une syllabe de intoxication. Un second examen du terme fut cependant demandé afin d' établir une fiche terminologique, qui a été publiée au JO du 23 mai 2020 et sur FranceTerme, la base de données de la Délégation générale à la langue française et aux langues de France (DGLFLF) (Figure  1 ).
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