dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We express our gratitude to  Zeynep Or (IRDES)  for her guidance throughout the project and for granting access to PMSI dataset for the duration of the study. We would also like to thank  Françoise Nguen ,  Maripier Isabelle ,  Anne Penneau ,  Hélène Huber ,  Audrey Laporte ,  Andrew Clark ,  Grégoire Mercier ,  Henri-Jean Philippe ,  Mark Stabile ,  Mark Dusheiko ,  Randall Ellis ,  Agnès Gramain ,  Marc-Arthur Diaye and Hospinnomics team  for their invaluable comments and contributions.
6 -96. 53 317. 73 2. We classify each hospital stay into both pre-and post-reform DRG classifications (i. e. , severity levels and resulting 34. 64 131. 7 11. 01 190. 8 r post -r pre -0. 022 0. 25 -0. 043 0. 27 -0. 047 0. 26 tariffs). 0. 037 0. 19 (r post -r pre )/r pre -0. 009 0. 15 -0. 019 0. 19 -0. 021 0. 18 0. 02 3. We estimate probabilities of each severity level for each child delivery outcome in both DRG classifications: 0. 13 (a) Based on the pre-reform (training) dataset, estimate ordered logit models with levels of severity as the outcome (see Gestational age Appendix D1 for model specifications); 22-35 weeks 36-39 weeks (b) Assign predicted severity probabilities 𝐴𝐴 d𝑑0 𝑤𝑤𝑤𝑤𝑤 and 𝐴𝐴 0. 034 0. 513 d𝑑1 𝑤𝑤𝑤𝑤𝑤 to all outcomes in both DRG classifications, where 𝐴𝐴 0. 18 0. 042 0. 2 0.
This overlaps with the number of electrodes in modern implants, and this choice gives more options for systematic changes in vocoder parameters for manipulating channel interactions  (Bingabr et al. , 2008). Moreover, 16 channels were used in a previous vocoder study with music materials (short melodies) that manipulated the current spread similarly to the current study  (Crew & Galvin, 2012). Each loudness-adjusted excerpt was thus processed with a 16-channel vocoder implemented in Matlab 9. 9 (R2020b, The MathWorks, Inc. , Natick, Massachusetts, United States). The band partitioning was based on Greenwood's cochlear place-frequency mapping function  (Greenwood, 1990)  between 150 and 7000 Hz. The analysis filters were 12 th order zero-phase Butterworth filters. Carrier. The carrier was either a sinewave or Gaussian noise.
Procedure All testing was conducted online using jsPsych 6. 1 software (de Leeuw, 2015) on a secure server. Participants were emailed an invitation with general instructions: they were asked to conduct the experiment in a quiet background on a computer, laptop, or tablet with an updated internet browser and stable internet connection. Participants were further recommended to use headphones but told that built-in speakers were acceptable. The email contained a unique link where they first read a consent form and agreed with the terms before proceeding to the experiment. Participants were informed that selfpaced breaks would occur approximately every 10 min where they could continue the experiment with a button-press, but to please complete the experiment in one sitting. The experiment lasted approximately 1.
To reduce testing time and potential testing fatigue, each participant was presented half of the stimuli set. The full stimuli set was counterbalanced across participants. During the experiment there was no control for participants closing the experiment and resuming later; if this happened, the 8-trial training always opened the new session to re-familiarize the participant with the task. It was not possible to return to earlier trials once responses were registered. A progress bar showed participants how much of the experiment they had completed. On average, excluding the time spent on the breaks, the whole experiment lasted about 1h, which corresponds to about 19 s per trial. Correct and incorrect answers per trial were recorded and formed the basis of the raw data confusion matrix.
Analysis Scores were first analyzed across vocoder conditions using a Generalized Mixed Effect Model (gLMM) based on a binomial distribution, i. e. , using logit as a link function. The analysis was implemented in R (v4. 0. 3, R Core Team, 2020) using the lme4 package (v1. 1. 27. 1,  Bates et al. , 2015)  The model had binary score (correct or incorrect) as dependent variable, Vocoder (nonvocoded, sinewave 12th order filter, sinewave 4th order filter, noise 12th order filter, noise 4th order filter) as fixed effect, and Participant and Presented Emotion as random factors. Note that the Presented Emotion is the emotion label that was assigned to the musical excerpt by the experimenter during the design and selection of the material by  Bigand et al. , (2005). An analysis of deviance was performed on the fitted model using the car package (v3.
0. 11, Fox & Weisberg, 2019). Post-hoc pairwise comparisons were performed using the emmeans package (v1. 7. 0,  Lenth et al. 2021). Multiple comparisons were here corrected with the Tukey method. Using raw scores, while informative, does not make the sensitivity of the participants apparent. For example, if a participant always answered one emotion, this one category would score 1 (all correct) while not taking into account that other emotion categories also incorrectly received that answer. Therefore, sensitivity, d', from the Signal Detection Theory  (Green & Swets, 1988; Macmillan and Douglas Creelman, 2004)  was derived from the raw data for each emotion category. For a given emotion, the d' was calculated by considering the correct categorization responses as hits (for a given row of the confusion matrix, the entries term on the diagonal), and incorrect categorization responses, i.
analysis S4. The d' values were then analyzed with a Repeated Measures ANOVA using the 'ez' package  (Lawrence, 2016)  in R (R Core Team, 2020). The RM ANOVA had Presented Emotion (joy, fear, serenity, sadness) and Vocoder (nonvocoded, sinewave 12 th order filter, sinewave 4 th order filter, noise 12 th order filter, noise 4 th order filter) as repeated factors. A separate RM ANOVA designed to investigate effects of vocoder type on emotion categorization was also conducted, with factors Presented Emotion (joy, fear, serenity, sadness), Carrier (sinewave, noise) and Filter order (Low-4th, High-12th). When the sphericity hypothesis was violated, the degrees of freedom were adjusted using the Greenhouse-Geisser method, and the adjusted p-value was reported as p GG. All ANOVAs used a Type 3 sum of squares.
The raw data of emotion categorization were also compiled as confusion matrices. In a confusion matrix, a 'perfect score' would be represented as one diagonal line with points only on the same emotion category for both 'presented' (x-axis) and 'responded' (y-axis). Errors are represented by data points off the diagonal, or 'confusions', where a different category was responded from what was presented. The confusion matrices were followed up with an analysis of feature information transmission  (Miller & Nicely, 1955). In feature information transmission analysis (FITA), considered features were the valence and arousal classes of each emotion category: each emotion was quantified as having either positive or negative valence, and low or high arousal (see Table  1 ). The analysis allowed us to estimate how much of the information associated with the feature was effectively received by the listener, which is particularly useful to compare acoustic features in the stimuli (van Wieringen & Wouters, 1999).
Sensitivity index d' In order to account for false alarms in the categorization of participants (see 'Analysis' above), the sensitivity index (d') was calculated. The sensitivity data are reported in Figure  3. Results with the sensitivity analysis were the same as with the proportion-correct scores. d' values for each presented emotion and vocoder were all greater than zero, i. e. , the performance was better than chance [t(  22  Non-vocoded vs vocoded: The main effect of Vocoder showed that, again, emotions were best categorized in the non-vocoded condition. The non-vocoded condition yielded the highest sensitivity (d' = 1. 74), but in the vocoded conditions, the performance was relatively higher for sinewave vocoders (d' = 0. 89 for 12 th order; d' = 0. 78 for 4 th order) than for noise vocoders (d' = 0.
60 for 12 th order; d' = 0. 42 for 4 th order). Vocoded carrier type and filter order: In order to examine the effect of carrier type and filter order within vocoded-only conditions, we analyzed only the vocoded data in a separate ANOVA with factors Emotion (joy, fear, serenity, sadness), Carrier (sinewave, noise) and Filter order (low-4th, high-12th). The effect of Emotion was again significant [F(3,66) = 20. 74, p < 0. 0001, η 2 G = 0. 13]. In addition, the analysis confirmed that Carrier type had a significant effect on sensitivity [F(1,22) = 28. 2, p < 0. 0001, η 2 G = 0. 09] with the sinusoidal carrier resulting in a higher sensitivity (d' = 0. 83) than the noise carrier (d' = 0. 51). Finally, the analysis also showed that filter order had a small, but significant impact on emotion categorization [F(1,22) = 7.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the  Dorhout Mees Foundation , the  Gratama Foundation, Heinsus Houbolt Funds , and the  VICI grant  (grant number  016. VICI. 170. 111 )from the  Netherlands Organization for Scientific Research (NWO)  and the  Netherlands Organization for Health Research and Development (ZonMw). The team Auditory Cognition and Psychoacoustics of the Lyon Neuroscience Research Center  is part of the  LabEx CeLyA (Centre Lyonnais d'Acoustique ,  ANR-10-LABX-60 ). The data that support the findings of this study will be openly available in the DataverseNL repository via https://doi. org/10. 34894/1REVFB.
A pilot test was conducted with two key informants from different sectors within government (education and social protection; health and agriculture) to identify any potential issues with the interview guide and determine the feasibility of the data collection process, which was to be done via telephone because of the COVID-19 pandemic at that time. Following the pilot, some questions were rephrased to provide clarity and ensure key informants answered them in a consistent and meaningful way. Additionally, to avoid respondent fatigue, it was decided to keep interview questions to those essential for generating readiness scores. Telephone interviews were conducted in English with 18 key informants between March and August 2021, lasting 30-60 min. The interviews were undertaken by two female researchers, AT (doctoral student) and a postdoctoral fellow working on the project.
Data analysis As specified in the CRM protocol,  23  each interview was transcribed verbatim and checked for accuracy. The interviews were then scored independently by two teams (AT/ALP and HO-K/GSA) using anchored rating scales to obtain a community readiness score for each dimension and an overall community readiness score on the nine-stage readiness scale. The nine stages of readiness Open access defined by the model range from 'no awareness' of the nature or extent of an issue through to a 'high level of community ownership' of ways to address the issue of concern (box 1). Where there were variations in scores, MH and RP reconciled differences and reached a consensus on what the score should be. The mean and SD for each dimension were calculated. The overall community readiness score was determined by finding the average readiness score for each of the five dimensions assessed.
Thematic analysis of interview transcripts was undertaken to provide additional understanding of the quantitative scores obtained and determine possible barriers and facilitators that could influence interventions. Using a deductive/a priori approach, a codebook consisting of predetermined themes from a community readiness study previously implemented to improve diets in Ghana  29  was adapted and used to code all transcripts. Coding of the transcripts was undertaken by AT using NVivo. 41  Twenty-five per cent of transcripts were double coded by a second coder (AL) to confirm if the codebook was applied consistently. Any discrepancies identified during the double coding process were discussed to achieve consensus.
Acknowledgements We acknowledge the contribution of  Dr. Phyllis Addo  and  Silver Nanema  who supported data acquisition and transcription of interviews, respectively. We would also like to thank all key informants for participating in this study. This publication was made possible through support provided by  IRD. Contributors AL, MH, RNOA, CA and AT were involved in the conception and design of the study. AT collected, analysed, interpreted the data and wrote the first draft of the manuscript. RP, ALP, HO-K and GSA supported the analysis of data for the work. All authors provided critical inputs to the initial draft, read and approved the final manuscript. AL and AT are guarantors of this work.
In this regard, we plotted candidate countries by latitude and nominal Gross Domestic Product per capita (US dollar) from the International Monetary Fund's 2021 World Economic Outlook Database  [14] , to ensure a heterogeneous sample. Second, it was required that each implementation region consisted of an ecosystem composed of health care organizations and partners from industry, civil society, academia, and government authorities all committed to implementing comprehensive, innovation-based approaches to healthy aging. In this sense, the highly rated European Innovation Partnership on Active and Healthy Aging (EIP on AHA) reference sites  [15]  were prioritized. Regarding the selection of RUCs to be implemented, there were also 2 key considerations. First, we wanted our site-specific pilots to cover the whole spectrum of citizen complexities and intervention intensities, thus the definitions of the RUCs needed to be broad enough to accommodate all possible combinations.
A set of monitoring devices will collect clinical data at home such as, bio-and physiological signals (ie, blood glucose concentration data or continuous glucose monitoring data, galvanic skin response, and heart rate variability) combining them with adaptive machine learning regression models to trigger promptly alarms and signs, allowing for personalized, precise, and timely interventions. Additionally, a novel prognostic index for disease control in people with type 2 DM will be built upon data gathered both from blood samples and from smartwatch-generated data linked to lifestyles (number of steps, calorie consumption, heart rate, and sleep).
RUC7: Multichronic Older Patient Management Including Polymedication RUC7 targets chronic older patients with variable complexity according to GATEKEEPER's risk stratification pyramid. The EIP on AHA blueprint "personas" can provide good examples of potential participant profiles  [32]. RUC7 proposes the implementation of novel chronic care models (CCM) for multimorbid subjects, or the enhancement of already existing CCM, using the possibilities of the different GATEKEEPER Spaces. Several sensing technologies, available in the GATEKEEPER Things Catalogue, can be leveraged and integrated into an unobtrusive mobile data collection platform (ie, based on smartphones and smart trackers), able to monitor the multiple parameters required in CCM for multimorbid subjects. Through the GATEKEEPER Health care Space, data could be shared with clinical professionals in charge of managing the CCMs, to adjust individual care plans accordingly.
RUC8: eHealth Solutions for the Management of High Blood Pressure Hypertension or high blood pressure (HBP), is a serious medical condition with an estimated worldwide prevalence of 31% of adults (1. 4 billion) that significantly increases the risk of heart, brain, kidney, and other diseases  [34]. RUC8 proposes novel integrated care management for patients with HBP, aiming for the monitoring of blood pressure and early detection of health complications (ie, heart problems and stroke). Telemonitoring services of different intensities will be harnessed to implement periodic or continuous monitoring. The comprehensive analysis of the generated data flow, channeled through the GATEKEEPER systems, will ultimately provide predictions anticipating the appearance of decompensations and triggering alerts and alarms.
RUC9: eHealth Solutions for the Management of COVID-19 As of January 2023, more than 650 million COVID-19 cases and 6. 7 million deaths have been confirmed globally  [35]. The onset of the COVID-19 pandemic has triggered a need for integrated management tools capable of working on a remote basis, thus limiting physical contact among different actors. In this scenario, the different GATEKEEPER Spaces can provide the technological basis to cover the necessities of patients with COVID-19, citizens in quarantine or lockdown, and health and social organizations dealing with the pandemic. RUC9 will tackle this challenge at 2 different levels. At the health care level, several sensing technologies, available in the GATEKEEPER Things Catalogue, can be leveraged and integrated into an unobtrusive mobile data collection platform (ie, based on smartphones, smart trackers, etc), able to monitor key parameters in COVID-19 (ie, heart rate, respiratory rate, and blood oxygen saturation).
Acknowledgments The authors would like to express their sincerest gratitude to all the partners and participants of the  GATEKEEPER  project. The  GATEKEEPER  project team comprises  Alexandra Bargiota ,  Clio Yuen-man Cheng ,  Ioanna Drympeta ,  Eugenio Gaeta ,  Kai Gand ,  Alba Gallego ,  Eleni Georga ,  Germán Gutierrez ,  Piotr Klimczak ,  Maria Krini ,  Pawel Lewek ,  Francisco Lupiáñez-Villanueva ,  Alessia Maccaro ,  Ersi Papayianni ,  Davide Piaggio ,  Francesco Ricciardi ,  Manuel Sanchez-de-la-Torre , and  Maria Vlachava. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  (under grant  857223 ), cofunded by the  European Regional Development Fund (ERDF) , "A way to make Europe. "JdB acknowledges receiving financial support from  Instituto de Salud Carlos III  (ISCIII; Miguel Servet 2019:  CP19/00108 ), co-funded by the  European Social Fund (ESF) , " Investing in your future.
Study design and population This was a cross-sectional hospital-based comparative study in Fako division Southwest region Cameroon. Healthcare professionals were approached and self-administered questionnaires were used to collect data on experience of BOS and factors associated with it as well as their sleep quality and practice of physical activity. Fako division is one of the six divisions in the South West Region of Cameroon with Limbe being its administrative capital. According to 2005 Cameroon national institute of statistics report, Fako division has a surface area of 2,093km 2 and a total population of 466,412. 15  We conducted our study in 3 public and 2 private randomly selected hospitals. The doctors, nurses and laboratory technicians in the selected hospitals were then recruited using consecutive sampling proportionate to size.
The minimum sample size calculated was 334 using a prevalence of 68% found among teaching staff of university institutions in Douala 16 and 5% precision. The study protocol and all the experiments have been evaluated and approved by the Faculty of Health Sciences Institutional Review Board of the University of Buea. The declaration of Helsinki guidelines have been followed for experiments, participants' privacy and data con dentiality. All participants signed informed consent prior to their enrolment in the study. Participants were met individually in their o ce and invited to participate to the study. Participants with at least six months of continuous activity were included in the study.
Data collection Data was collected using a structured questionnaire made up of four sections. The rst section contained information on socio-demographic characteristics, work and personal characteristics. The second section was assessed BOS, the third section assessed sleep quality and the fourth section assessed level of physical activity Parameters measurements BOS was assessed using the Maslach Burnout Inventory for human services survey (MBI-HSS). This is a reliable, widely used and validated tool for assessing burnout among healthcare personnel worldwide. 17  The MBI-HSS is made up of three components; emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). It consist of 22 items (numbered from 1 to 22) with answers on a 7-likert scale (a score from 0 to 6 is possible for each item) regarding the frequency with which certain workrelated sensations are experienced.
Statistical analysis Data were collected and analyzed using SPSS version 20. Quantitative variables were presented as means ± standard deviation and qualitative variables were presented as frequencies and percentages. Quantitative data were compared between gender and type of hospital using the student t-test while qualitative data were compared using the chi-squared test. Binary logistic regression was used to determine factors associated with BOS. Odd ratios and 95% con dence intervals were calculated and statistical signi cance was set at p-value less than 0. 05.
We used E-OBS for two main reasons. On the one hand, our health dataset represents the whole population of the provinces, and not just that of the main cities. Consequently, we needed to estimate the average temperature of each province by using temperature data that is spatially representative. Towards this aim, we needed to use gridded data, and not just data from a sample of stations. In that regard, we note that the use of gridded data has already been successfully validated for epidemiological studies elsewhere  (de Schrijver et al. , 2021). On the other hand, we wanted to use daily, high-resolution, gridded data covering the whole 39-year period, and based only on observational data, ie not including climate models. That is the reason why we did not consider reanalysis products such as ERA5-Land.
Acknowledgments HA, MQZ, SJL, RFMT and JB gratefully acknowledge funding from the  European Union 's  Horizon 2020 and Horizon Europe research and innovation programmes  under grant agreements No  865564  ( European Research Council   Consolidator  Grant EARLY-ADAPT, https://www. early-adapt. eu/), 101069213 (European Research Council Proof-of- Concept HHS-EWS ) and  101123382  (European Research Council Proof-of- Concept FORECAST-AIR ). HA also acknowledges funding from the  European Union 's  Horizon Europe research and innovation programme  under grant agreement No  101065876  ( MSCA Postdoctoral Fellowship   TEMP-MOMO ). SJL also acknowledges funding from  FOR-MAS , the  Swedish Research Council for Sustainable Development  ( Cross-Cutting Climate Adaptation -Challenges and Measures Grant ADATES ).
JB also acknowledges funding from the  Ministry of Science and Innovation (MCIU)  under grant agreement No  RYC2018-025446-I  (programme  Ramón y Cajal ). ISGlobal authors acknowledge support from the grant  CEX2018-000806-S  funded by  MCIN/AEI/10. 13039/ 501100011033 , and support from the  Generalitat de Catalunya  through the  CERCA Program. We acknowledge the E-OBS dataset from the  EU -FP6 project UERRA (https://www. uerra. eu) and the data providers in the ECA&D project (https://www. ecad. eu).
Data Sources The incidence and prevalence estimates needed for data generation were obtained either from preliminary estimation on a national database or large cohorts of elderly or from the literature when reliable estimates already existed. We selected or computed the estimated prevalences and incidences using representative samples as large as possible, and the estimated hazard ratios from cohorts with the best data on the risk factors. Population sizes at age 65 and projections for general mortality in France by sex, age and calendar year were obtained from the French National Institute of Statistics and Economic Studies (INSEE). Gender-speci c prevalence at age 65 and incidence of treated diabetes from age 65 were provided by Fuentes et al. [21]. These estimations rely on the French National Health Data System which includes outpatient reimbursement of dispensed healthcare for the whole French population.