dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ) and the "e-health and integrated care and trajectories medicine MIAI artificial intelligence" chairs of excellence from the  Grenoble Alpes University Foundation  (JLP, HR, RT, and SB). AF was employed by  Grenoble Alpes University Hospital. HR and PW were also supported by the  French National Health Insurance  (" Régime général de l'assurance maladie "). ND was supported by the national research and technology agency as part of a thesis grant (CIFRE) at Agir a dom (Healthcare provider). The funding sources had no involvement in the conduct of the research or preparation of the article.
g. domestic life, social and family relations); (2) effect of the pandemic on their professional life (e. g. work organisation, resource management, relationship with the hierarchy/ administration/ colleagues); (3) societal aspects of the pandemic (e. g. role of the media and/or experts, public opinion). The interview guide was developed by our group based on a review of available literature, plus a focus group with a sociologist, qualitative researchers, and ICU physicians and nurses. Due to the ongoing pandemic during the study period, all interviews were performed by telephone. Interviews were performed until data saturation was reached. Interviews were recorded and fully transcribed (in French) for later analysis. Data were encoded to guarantee the anonymity of the participants. The transcripts of all interviews were analysed using thematic analysis, as previously described  [4, 5].
Discussion This qualitative study investigating the sources and repercussions of the psychological distress suffered by healthcare workers during the COVID-19 pandemic, provides novel insights into the perceptions and experiences of ICU staff, and how this affected them in their daily personal and professional lives. There is paucity of data on this topic, yet the health of these professionals seems to be in jeopardy, with a level of psychological stress that has been cumulating over the course of pandemic, and reports of a fear of dying and an elevated risk of suicide  [2]. Reports to date about the mental health in healthcare professionals have mainly relied on quantitative data, with wide heterogeneity across studies due to the large number of different scales and tools used. As a result, comparisons are difficult, and it is difficult to draw conclusions about the individual effects of various factors in generating anxiety, depression or posttraumatic stress disorder  [1] [2] [3] 6].
Example of an IFC Meeting: The Apollo Study As an example, we present a summary of one of the studies we investigated: the Apollo study (NCT02132975). The Apollo study was a first-in-man clinical trial. The main objective was to compare the efficacy of a non-CE marked innovative medical device (MD): a co-manipulator for assisting endorectal prostate biopsies. The main criterion analysed was the distance between the virtual (desired) biopsy and the real one. The study design was a randomized, controlled (with or without guide), open-labelled bi-centre pilot superiority clinical trial including 10 patients per group. 'Modified' intention to treat (ITT) and per-protocol analyses were realized. Outcomes are described in Table  3 : The distance was significantly lower in the 'Apollo arm' than in the control arm (p < 5%), whatever the type of analysis.
Medical data. Clinical, biological and demographic data were collected at hospital admission, including risk factors, comorbidities, chronic medications, heart failure (HF) assessed by Killip class, and hemodynamic parameters. An echocardiography was performed < 12 h after admission to measure left ventricular ejection fraction, as previously reported  10, 11. The Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2P) was used to quantify atherothrombotic risk  12. TRS-2P is calculated by the arithmetic sum of 9 variables associated with atherothrombotic risk: age ≥ 75 years, diabetes mellitus, hypertension, current smoking, peripheral artery disease (PAD), prior stroke, prior coronary artery bypass grafting (CABG), history of HF, and renal failure (estimated glomerular filtration rate < 60 mL/min) (supplementary Fig.
Environmental noise exposure. According to the European directive 2002/49/EC  15  , environmental noise prediction models were used to assess the outdoor residential exposure of each study patient; these emission-propagation models were elaborated with MITHRA-SIG v3. 7 (Geomod/CSTB) software. The models were validated by means of several measurement campaigns  16, 17. Noise levels were calculated for the residential building, in front of each façade and at each floor. They were then linked to the patient through their self-declared home address at the time of their MI. The address of each patient at the time of their MI was carefully gathered and verified to ensure that the assigned building corresponded to the right exposure. For each patient, the average building noise levels in front of the entire façade were calculated.
Multiple noises sources were considered: rail traffic, road traffic, and pedestrian traffic. Four indices were computed: road traffic noise and the railway noise (considering only one source, i. e. road or railway respectively), transportation noise (combining road and railway sources), and global noise (combining all the sources). Noise levels were calculated for two time periods, as defined in ISO 1996-2:2017 18 , and expressed as the daily equivalent A-weighted noise level L Aeq , 24 h and the night equivalent A-weighted noise level L night (22:00-6:00). Air pollution exposure: nitrogen dioxide (NO 2 ) and particulate matter (PM 10 ). The NO 2 and PM 10 outdoor concentrations were calculated using a two-step emission and diffusion modeling  19, 20  First, the daily averaged annual road-traffic emissions were calculated using Circul' Air, software developed and used by the French air quality monitoring network on the basis of the COPERT4 European standard methodology.
Socioeconomic level. The neighbourhood socioeconomic level was estimated with a deprivation index based on the French sub-municipal census block groups (called IRIS). As defined by the National Institute of Statistic and Economics Studies, an IRIS (acronym of aggregated units for statistical information) has homogeneous type of habitat and about 2000 inhabitants  20, 24. This index was split into three classes: affluent, mixed and deprived IRIS. The socioeconomic level of each patient was calculated with the R package SesIndexCreator  24. Statistical analysis. Patient characteristics for each sex are summarized as numbers (percentages) for categorical variables and as medians (interquartile range) for continuous variables. Atherothrombotic risk was categorized on 4 classes named: low-risk, medium-low-risk, medium-high-risk, high-risk.
Study limitations. Our study has some limitations. One is the use of TRS-2P, which includes the most important CV risk factors, as an index of atherothrombotic risk because it limited the relevance of adjustment on other variables. However, although the sample size was small, the odd ratios were only slightly modified by adjustments, highlighting the constancy of the associations between environmental noise and CV risk. We however acknowledge a weaker association between noise and the atherothrombotic risk when considering simultaneously the NO 2 exposure. The lack of biological sampling to assess CV risk factors such as proinflammatory or prothrombotic markers, limited the conclusion on the link between the CV risk and noise. Furthermore, medical data collected at the time of admission does not represent baseline condition, whereas the noise levels assigned to each patient correspond to chronic exposure.
| Data collection For both surveys, data collection took place using structured questionnaires with precoded responses, administered by trained enumerators with >10 years' experience in conducting communitybased health, demographic, and nutrition surveys. Interviews were conducted face-to-face via household visits (pre-COVID-19) and by telephone. The data collection team underwent a 2 weeks' training programme before each of the two surveys. For both surveys, the questionnaire was produced in English and translated to Spanish, and rechecked in both languages by a team member fluent in both languages. For PERUSANO, questionnaires were piloted in Lima (n = 20 interviews) using mothers of IYC living in another similar community (Canto Grande) to the target communities and changes were made accordingly.
Automated consistency checks were run and double data entry for a random sample of 10% of questionnaires was used to calculate the error rate. A predefined threshold was set to determine an acceptable level of variation in responses, with the error rate set at 1%. For the STAMINA survey, questionnaires were completed using tablets (Samsung Galaxy Tab-A) with an electronic data capture (CsPRO) PRADEILLES ET AL. | 3 of 12 during telephone interviews. Data quality was enhanced in CSPrO through programming of skips, controls, and other logic of the questionnaire. Data collected in both surveys included: household sociodemographic characteristics; infant and young child feeding (IYCF) practices using standardised and validated questions  (WHO, 2008; WHO and UNICEF, 2021) , and child and maternal qualitative 24-h dietary recalls using the standardised open recall method  (WHO, 2008).
ACKNOWLEDGEMENTS This study was supported by  UK GCRF-Newton Fund Agile Response  to  COVID-19  ( EP/V034057/1 ) and the  UK-Peru Newton Fund with UK Medical Research Council  ( MR/S024921/1 ) and  CONCYTEC/FONDECYT Perú  ( 032-2019 ). The authors would like to thank survey data collectors:  Patricia Calderon ,  María Muller ,  María Pompilla ,  Elizabeth Orrego , and  Karla Carbajal  and supervisors  Melissa Abad  and  Krysty Meza ;  Smildzinia Rico  for the data entry and  Hannes Rodriguez  for the tablet survey programme. They would also like to thank the survey participants in Húanuco and Manchay, Lima.
DATA AVAILABILITY STATEMENT The survey questionnaires and data that support the findings are available as open access resources via the Loughborough University Research Repository for the PERUSANO study (at 10. 17028/ rd. lboro. 18750458 and 10. 17028/rd. lboro. 16691455) and the STAMINA study (10. 17028/rd. lboro. 16825507 and 10. 17028/ rd. lboro. 18785666) following a period of embargo.
We focus on the latter approach, and more specifically on methods triangulating 2D joint center estimations from a network of several calibrated RGB cameras. The most common evaluation metric is the Mean Per Joint Position Error (MPJPE), which is the average Euclidian distance between the estimated joint coordinate and its ground truth. A large part of studies investigating 3D joint center estimation choose to triangulate the output of OpenPose  [13] , a deep-learning algorithm estimating 2D joint coordinates from videos. Their MPJPE usually lies between 30 and 40 mm  [14] [15] [16]. Ankle MPJPEs are within the margin of error of marker-based technologies (1-15 mm), whereas knee and hip MPJPEs are greater  (30-50 mm). These errors are systematic and likely due to "groundtruth" images being mislabeled in the training dataset  [17].
Pose2Sim Kinematics All videos from our virtual cameras were processed by OpenPose (version 1. 6), which delivered 2D joint coordinates for each view. We used the OpenPose experimental BODY_25B model (Figure  1 ) with the highest accuracy parameters  [32]. The Pose2Sim workflow was then used to track the person of interest, robustly triangulate the OpenPose 2D joint coordinates, and filter the resulting 3D coordinates. Then this output was fed to our OpenSim setup to constrain the results to physically consistent kinematics (Figure  3 ; more details in our previous study  [25] ). The code is freely available on https://gitlab. inria. fr/perfanalytics/pose2sim (accessed on 26 March 2022). Participant's 3D textured meshes were extracted using 68 video cameras in the studio, and then placed in a virtual environment.
Pose2Sim Kinematics All videos from our virtual cameras were processed by OpenPose (version 1. 6), which delivered 2D joint coordinates for each view. We used the OpenPose experimental BODY_25B model (Figure  1 ) with the highest accuracy parameters  [32]. The Pose2Sim workflow was then used to track the person of interest, robustly triangulate the Open-Pose 2D joint coordinates, and filter the resulting 3D coordinates. Then this output was fed to our OpenSim setup to constrain the results to physically consistent kinematics (Figure  3 ; more details in our previous study  [25] ). The code is freely available on https://gitlab. inria. fr/perfanalytics/pose2sim (accessed on 26 March 2022). Pose2Sim comes with a generic OpenSim skeletal model that has been slightly improved since the last study  [25].
Marker-Based Kinematics The captured markers were automatically identified with an AIM procedure within the Qualisys Track Manager software (version 2019. 1). Joint centers were then calculated. The centers of ankles, knees, wrists, and elbows were defined as the midpoints between the malleoli/epicondyles/styloids since it has been shown that when executed on a lean participant, functional methods do not improve the reliability of the kinematics of running  [35]. Hip joint center was defined with a functional method  [36]. The OpenSim model used for marker-based scaling and inverse kinematics was the same as the Pose2Sim model. Scale factors were computed in a similar way, but with marker data rather than with OpenPose keypoints. Weights proposed by the inverse kinematics solver of OpenSim were set to 5 for joint centers, to 1 for cluster markers, and to 2 for other anatomical markers.
Pose2Sim could operate with such a 2D pose estimation model, although new keypoints should then be placed afresh on the unscaled OpenSim model. Note that the training dataset of this more accurate pose estimation model should not base its labeling on markers, which could be interpreted as visual cues, which would not be available in real sports situations. However, this condition is not sufficient: the dataset should be large enough, represent a wide variety of body types and movements  [47] , and include images with motion blur such as found in sports videos. It is also possible to enhance the OpenPose dataset, by training it on specific sports poses, or by augmenting it with larger rotations, so that the model recognizes upside-down poses. One risk of this approach is that the model may perform better on specific extreme poses, but worse on standard ones  [48].
Materials and methods The qualitative phase of this study was conducted using semistructured interviews in Lyon (n = 3) and Paris (n = 4) following the end of the first lockdown in France (May 10th to June 10th 2020). The target population consisted of individuals living in shelters with the following inclusion criteria: 18 years old or over; without cognitive impairments; and with a basic level of French or English. The estimated sample size to reach data saturation was set at 30 participants, as supported by previous literature  (Pires, 1997). Interviews were conducted either in French or English by two researchers (LC, ACC) at participants' shelters, in an environment conducive to confidentiality. Based on prior literature on healthcare access and the living conditions of vulnerable populations  (Graetz et al.
, 2017; Winters et al. , 2018; Vandentorren et al. , 2016; La, 2009; Cognet et al. , 2012) , an interview guide was designed with the aim of exploring one's attitude and representations regarding the lockdown (see Table  1 ). All interviews were recorded and fully transcribed. Transcripts were analysed based on an inductive thematic approach  (Bardin and Chapitre, 2013; Braun et al. , 2019)  using NVivo© (release 1. 3 535) software. The analysis process consisted of six steps (cf. Fig. 1 ): familiarisation, coding, theme generation, reviewing, naming, and writing up  (Allard-Poesi, 2003; Negura, 2006). Once familiar with the data, blinded coding of 25% of the transcripts was undertaken by two researchers (LC, SD) in order to classify the initial codes and achieve inter-coder reliability  (Allard-Poesi, 2003).
Strengths and limits of the study The ECHO study is, to our knowledge, one of the first studies to investigate perceptions of the COVID-19 pandemic and lockdown amongst vulnerable populations living in homeless shelters in France. Conducting interviews in English allowed us to include persons with a wide range of sociodemographic characteristics, particularly those recently arriving in France. Data saturation was achieved according to the principle of theoretical sufficiencydeveloped by Glaser and Strauss (Glaser and Strauss, 2010)-therefore our results provided significant insight on this population in the context of the COVID-19 pandemic. Although our study represents a unique data source, including homeless and migrant participants often underrepresented in research, it is not without its limitations.
Firstly, the limited sample sizealthough consistent with the literature  (Pires, 1997)  cannot guarantee that all profiles within these groups are represented in our results. Participants were only recruited from shelters, thereby excluding all those still living on the streets and potentially excluding the most precarious population. However, during this first lockdown, the French government had an active policy of sheltering all homeless persons, as a preventive measure against the propagation of COVID-19. Overall, 58% of our sample were living on the streets before lockdown, thus indicating that these participants might be more comparable to the general homeless population than the usual population living in shelter. Finally, ECHO's data collection was cross-sectional, limiting our understanding of the impact of COVID-19 to a specific moment in time.
Acknowledgements Firstly, we would like to thank the participants who agreed to take part in this study. We also thank the non-governmental organisations involved in this study:  Habitat et Humanisme, Aurore , the  French Red-Cross, Empreintes, Groupe SOS ,  La Rose  des vents, SIAO 67, and their managers, social workers and volunteers for their kind participation. Finally, thank you to the other members of the  ECHO  team.
Assisted by a technical team based at the London School of Hygiene & Tropical Medicine, London, Commissioners drew on the following sources of evidence: case studies prepared by national consultants on innovations in PHC financing in seven LMICs (Brazil, Chile, China, Ethiopia, Ghana, India, and Philippines) and three high-income countries (Estonia, Finland, and New Zealand); a compilation of Organisation for Economic Co-operation and Development (OECD) and WHO health expenditure data to conduct expenditure analysis; a new survey of PHC organisation and provider payment in LMICs; literature reviews, including systematic and scoping reviews, of existing knowledge on financing PHC; and an expert roundtable on digital technologies and PHC financing. Additional publications based on these Panel 1: Health financing functions and arrangements Three core health financing functions are mentioned throughout the report: • Mobilisation of funds: the collection of revenue (from taxes, insurance contributions, user fees, donations, or other means) that is used to pay for delivery of health services.
The Lancet Global Health Commissions expenditure data on PHC in LMICs remain inadequate because of underinvestment in resource tracking systems, as well as the complexity of defining and tracking expenditure on PHC. 6  Finally, different international bodies and countries compile PHC expenditure using different definitions (panel 4). Not only is there no agreed-on approach to estimate spending on PHC, but there is also no single database that provides globally comparable data for analysing PHC expenditure. Definitions matter. They signal what is prioritised and valued, and they shape norms regarding how services should be organised. They also influence how data are collected and presented. There is a trade-off between using a simple global definition and accounting for country-specific definitions to permit more accurate reporting.
To estimate PHC expenditure using a platform-based approach requires data that cross-classify between health care function and provider category. However, because currently available data from WHO are only reported by health-care function, we are limited to estimating PHC expenditure using a To prepare our landscape analysis of PHC expenditure across countries, the Commission needed to combine data from various sources, considering different definitions and assumptions. We focused on two key sources: OECD  46  and WHO. 47 e OECD definition of PHC begins with expenditure on basic health care services derived from the health care function (HC) classification (namely, the sum of spending on general outpatient curative care (HC131), outpatient dental care (HC132), home-based curative care (HC14), and preventive care (HC61-HC64).
The Commission's definition The Commission used a definition of PHC expenditure based on the WHO definition to compile the data presented below. However, we excluded the administration and governance expenditures. This brings our definition closer to that used by the OECD and limits additional arbitrary assumptions. We are not suggesting that administration and governance are not important for PHC; rather, we believe that these inputs are better captured outside the direct measurement of PHC spending. The inclusion of administration costs disproportionately biases estimates of PHC in low-income countries upwards. Even with this restriction on the definition of PHC spending, our estimates have been calculated using a broad definition of PHC that does include some hospital care. Therefore, our estimates should be seen as an upper bound estimate.
The Lancet Global Health Commissions service-based (rather than a platform-based) approach. We also made some modifications to the PHC expenditure reported by the WHO for the purposes of our analysis (panel 4). WHO reported data on total PHC spending on 98 countries for 2018 and government spending on PHC on 61 of these. We observed that the WHO database did not provide data on government spending on PHC for all OECD countries. In order to compare spending levels in high income countries with those in LMICs, we constructed PHC expenditure for OECD countries from data on expenditure by financing scheme, using expenditure by government and compulsory schemes to proxy for government spending on PHC. We used exchange rate data from the Global Health Expenditure Database to convert the raw data from local currency.
Levels of financing for PHC Despite data limitations, our analyses have identified some notable patterns in the levels and sources of PHC spending across countries. Table  1  presents overall health expenditure data by country income group for 2018. The table shows that total expenditure on PHC in low-income countries is $24 per capita and in lower-middle-income countries it is $52 per capita. Government spending on PHC is even more meagre, at $3 in low-income countries and $16 in lower-middle-income countries, which falls short of the WHO estimate of the per capita recurrent cost for PHC of $65 in low income countries and $59 in lower-middle income countries 10 (section 3). Although the share of government health spending allocated to PHC is similar in LMICs and high-income countries, govern ment spending on PHC as a share of total PHC spending is lower in low-income countries than in highincome countries and the same is true of the share of government PHC spending in relation to GDP.