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14,063 results for “Care”
The influence of boating noise on the parental care behaviors of smallmouth bass (Micropterus dolomieu) during the summer of 2024 at Douglas Lake, Michigan, USA.
Anthropogenic noise is on the increase and in aquatic systems one of the major sources of noise is boat traffic. For organisms in lakes, rivers, and oceans that are capable of hearing, anthropogenic noise may alter behavior in a number of different ways. Here we did a combination of field and experimental work by locating smallmouth bass nests that were actively being guarded by males. Using an underwater drone, we monitored nest guarding behavior before and after a boat ran by the nest. In addition, we monitored behavior during this period while simultaneously recording boat motor noise. The results showed that the sequence of behavior performed by bass was altered during and after the boat ran by the nest.
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
Implementation of Frailty Care Bundle (FCB) for older people in acute care settings
<p>A study aimed to implement a Frailty Care Bundle (FCB) for orthopaedic trauma patients to increase mobilisation, nutrition and cognitive well-being in order to reduce hospital associated decline risk.</p>
Minimal data set for "Cohort profile: The ENTWINE iCohort Study, a multinational longitudinal web-based study of informal care"
<p><strong>Title:</strong></p> <p>Minimal Data Set for the Reproduction of Findings in "Elayan et al., Cohort Profile: The ENTWINE iCohort Study, a Multinational Longitudinal Web-Based Study of Informal Care".</p> <p> </p> <p><strong>Study Summary:</strong></p> <p>The data sets provided herein are derived from the ENTWINE iCohort Study, a multinational web-based cohort study employing an intensive longitudinal design. The study integrates a two-wave panel survey (baseline and 6-month follow-up) with optional weekly diary assessments. The cohort comprises caregivers and care recipients from nine countries: the United Kingdom, the Netherlands, Italy, Sweden, Israel, Germany, Greece, Poland, and Ireland. The study aimed to examine the influence of personal, psychological, social, economic, and geographic factors on caregiving experiences.</p> <p>Participants were eligible if they met the following criteria: 1) residency in a participating country; 2) capability to respond to surveys in English, Swedish, German, Dutch, Italian, Greek, Hebrew, or Polish; 3) access to the internet and ability to use it; 4) at least 18 years of age; 5) self-declared cognitive and physical capacity to complete the surveys; 6) either providing care to an adult (aged ≥ 18 years) with a chronic health condition, disability, or other care need, or receiving care from an adult due to similar conditions.</p> <p>The detailed methodology and results of the study can be found in the associated manuscript. For the complete survey questionnaires, please refer to: Morrison V, Zarzycki M, Vilchinsky N, Sanderman R, Lamura G, Fisher O, et al. A Multinational Longitudinal Study Incorporating Intensive Methods to Examine Caregiver Experiences in the Context of Chronic Health Conditions: Protocol of the ENTWINE-iCohort. Int J Environ Res Public Health. 2022;19. doi: <a href="https://doi.org/10.3390/ijerph19020821">10.3390/ijerph19020821</a></p> <p> </p> <p><strong>Data files:</strong></p> <p>The repository contains the following data files:</p> <ol> <li>"cg_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Caregiver Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cg_minimal_dataset_codebook".</li> <li>"cr_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Care Recipient Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cr_minimal_dataset_codebook".</li> </ol>
Comparing effects of auditory and visual disturbances on smallmouth bass parental care behaviors during the summer of 2025 at Douglas Lake, Michigan, USA
A prevalent source of sensory pollution within aquatic systems is recreational motorboats that can impact aquatic organisms through several exposure mechanisms. Auditory and visual sensory disturbances are particularly important as fish may utilize these cues during critical reproductive behaviors such as parental care. Here, we conducted a field study in Douglas Lake, Michigan, and located wild smallouth bass nests actively guarded by males. We exposed smallmouth bass to two sequential treatments of playback auditory noise and visual disturbances. Using an underwater drone, parental care behaviors of smallmouth bass were monitored before, during, and after both auditory and visual disturbances. The results show that auditory and visual disturbances may alter smallmouth bass parental care behaviors differently.
Data relating to Chiedozie et al. How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England.
<p>Data in .csv format relating to the paper Chiedozie et al. 2020 "How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England." Also contains eTables 5-8 in Excel format, and Stata do file for deriving the DU90% indicator.</p>
Life Cycle Assessment Dataset for Kidney Care Environmental Optimisations within Haemodialysis
<p>This dataset supports a study on environmental optimizations in haemodialysis (HD) kidney care, focusing on reducing carbon emissions, water usage, and social impacts such as forced labour. It includes detailed analyses of interventions to improve sustainability across multiple domains:</p> <ol> <li> <p><strong>Travel Reduction</strong>: Data explores the impact of reducing patient travel distances by 10%, 50%, and 90%, highlighting significant greenhouse gas (GHG) emission savings (up to 2,540 kg CO2e per patient annually) and associated reductions in water usage and forced labour risks. Interventions include promoting home-based dialysis, telemedicine, and optimized patient facility allocation.</p> </li> <li> <p><strong>Water Management</strong>: The dataset documents innovations such as reclaiming reverse osmosis water for reuse, optimizing water treatment plant operations, and reducing water consumption during dialysis processes. Larger centres and daily operation schedules show better water efficiency compared to smaller, less frequent setups.</p> </li> <li> <p><strong>Waste Management</strong>: Data highlights strategies for diverting waste from clinical to domestic streams, recycling dialysis materials, and adopting advanced technologies like pyrolysis. These measures reduce the environmental and economic burden of waste disposal, including incineration costs.</p> </li> <li> <p><strong>Energy Optimizations</strong>: Included interventions cover energy-saving technologies such as heat exchangers in dialysis machines, solar panel installations, and IT system automation. Solar energy adoption demonstrates varied CO2e savings based on regional energy mixes.</p> </li> <li> <p><strong>Incremental Dialysis</strong>: Data supports the transition to incremental dialysis—starting with fewer weekly sessions—to preserve resources, reduce GHG emissions, and maintain residual kidney function, offering both environmental and clinical benefits.</p> </li> </ol> <p>Each intervention was assessed using Life Cycle Assessment (LCA) methodologies, with functional units based on annual HD use for one patient. Metrics include carbon dioxide equivalent emissions (CO2e), water deprivation, and forced labour hours, aligned with EU Product Environmental Footprint standards. The dataset provides comparative results to guide clinical sites in prioritizing high-impact interventions, offering actionable insights into sustainable HD care.</p>
Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities
<p>Simulation output files for 'Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities'.</p> <p>Simulating COVID-19 transmission and hospital burden to assess at which intensive care unit (ICU) occupancies mitigation, that reduces transmission, needs to be triggered to avoid exceeding ICU capacity limits, using the city of Chicago, Illinois as an example.</p> <p>Manuscript is under review for scientific publication, (see <a href="https://www.medrxiv.org/content/10.1101/2021.06.27.21259530v1">preprint on medRxiv</a>) and scripts are available from the GitHub repository at https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021. </p> <p>Simulation output files uploaded per scenario including projected COVIID-19 transmission and burden trajectories for Chicago city for March 2020 to May 2021 per day.</p> <p>Simulation scenarios:</p> <p><reopening % above ICU capacity>_<delay after reaching ICU threshold>_<%mitigation>_<common simulation name> i.e. `50perc_1daysdelay_pr6_triggeredrollback_reopen`</p> <ul> <li>`emodl` file <ul> <li>required file for COVID-19 transmission model in the <a href="https://docs.idmod.org/projects/cms/en/latest/index.html">Compartmental Modeling Software</a> (see <a href="https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021">GitHub repository</a> for details)</li> </ul> </li> <li>sampled_parameters.csv <ul> <li>simulation input and scenario parameters, (nrow=4400, 400 unique parameter combinations * 11 scenario values)</li> </ul> </li> <li>rt_trajectoriescovidregion_11.csv <ul> <li>estimated reproductive numbers per trajectory for complete timeline per day</li> </ul> </li> <li>trajectoriesDat_region_11_traces.csv <ul> <li>filtered to include top 100 trajectories fitted to ICU data</li> </ul> </li> <li>trajectoriesDat_region_trimfut.csv <ul> <li>truncated to only include projections after September 1st 2020</li> </ul> </li> </ul> <p>The folder `mainfigures_csvs.zip` includes processed simulation output data for the publication figures.</p>
GERONTE H2020 Project - GERDAT002 - Composition of the health care professional consortium
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community</strong> <strong>with a dataset of core health care professionals that should be involvoed in the evaluation and treatment trajectories of older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of the GerOnTe care pathway was to determine which health care professionals should be included in the health care professional consortium (HPC) providing care for the patient. Beforehand, we had considered the option of four core members and at least eight other participants depending on the patient’s specificities or profile.</p> <p>Based on clinical experience, we developed a list of 15 potential participants, including general practitioner, one or more oncology specialists (such as surgeons, medical oncologists, radiotherapists), geriatrician, oncology nurse, social worker, clinical pharmacist, physiotherapist, anaesthesiologist, home care nurse, dietician, occupational therapist, spiritual helpers/clerics, psychologist/psychiatrist, palliative care specialist, organ-specific physician(s) such as cardiologist, pulmonologist, nephrologist, rheumatologist etc.</p> <p>This list was presented to the expert panel, and they were asked to determine whether or not these participants should be involved in decision-making and/or the subsequent oncologic care trajectory; experts could specify if these participants should be involved for all patients, only in specific situations/profiles, or did not need to be involved.</p> <p>The results of this expert panel survey and the subsequent composition of the health care professional consortium forms the basis of this dataset.</p>
THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE "HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM" AND ITS IMPACTS
<p><strong>Dataset name:</strong><em> asppl_dataset_v2.csv </em></p> <p><strong>Version: </strong>2.0 </p> <p><strong>Dataset period: </strong>06/07/2018 - 01/14/2022</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>8118</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </p> </li> <li> <p>Brazilian Occupational Classification (CBO) (Brasil, 2022b);</p> </li> <li> <p>National Registry of Health Establishments (CNES) (Brasil, 2022c); </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>asppl_dataset_v2.csv</em> dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical. </p> </td> <td> <p>Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)</p> </td> </tr> <tr> <td> <p><strong>course_progress</strong></p> </td> <td> <p>Percentage of completion of the course. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>Range from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>0, 1, 2, 3, 4, 5 or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). </p> </td> </tr> <tr> <td> <p><strong>CNES</strong></p> </td> <td> <p>The CNES code refers to the health establishment where the participant works.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>CNES Code or NaN.</p> </td> </tr> <tr> <td> <p><strong>health_care_level</strong></p> </td> <td> <p>Identification of the health care network level for which the course participant works.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“ATENCAO PRIMARIA”,</p> <p>“MEDIA COMPLEXIDADE”, </p> <p>“ALTA COMPLEXIDADE”, </p> <p>and their possible combinations.<br> <br> (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE") </p> </td> </tr> <tr> <td> <p><strong>year_enrollment</strong></p> </td> <td> <p>Year in which the course participant registered.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Year (YYYY).</p> </td> </tr> <tr> <td> <p><strong>CBO</strong></p> </td> <td> <p>Participant occupation.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>prison_syphilis_and_population_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>13</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>National Penitentiary Department (DEPEN) (Brasil, 2022d); </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>prison_syphilis_and_population_brazil.csv</em> dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.</p> <p><strong>Table 2:</strong> Description of DEPEN dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2017</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2017</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2018</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2018</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2019</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2019</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2020</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2020</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>pop_2017</strong></p> </td> <td> <p>Prison population in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2018</strong></p> </td> <td> <p>Prison population in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2019</strong></p> </td> <td> <p>Prison population in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2020</strong></p> </td> <td> <p>Prison population in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>students_cumulative_sum.csv</em></p> <p><strong>Dataset period: </strong>2018 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: 7</strong></p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>students_cumulative_sum.csv</em> dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 3:</strong> Description of Students dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region of the course participant. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>Number of students enrolled in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Standardized rate of students in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Standardized rate of students in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Standardized rate of students in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>syphilis_tests_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Brazilian Ministry of Health, through the Outpatient Information System of the Brazilian Health System (SIA/SUS) (Brasil, 2022f);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>syphilis_tests_brazil.csv</em> dataset (see Table 4) originate mainly from the Outpatient Information System of the Brazilian Health System (SIA/SUS). This dataset provides data on the number of tests for syphilis detection by region and year. In addition, it brings a rate that represents the normalized data to compare the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 4:</strong> Description of Syphilis Testes dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region where tests for syphilis were performed. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>2017</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2017</strong></p> </td> <td> <p>Syphilis testing rate in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Syphilis testing rate in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Syphilis testing rate in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Syphilis testing rate in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: <a href="https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114">https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114</a> .</p> <p>Brasil (2022b). Cbo - classificação brasileira de ocupações. Available from: <a href="http://www.mtecbo.gov.br/cbosite/pages/home.jsf">http://www.mtecbo.gov.br/cbosite/pages/home.jsf</a> .</p> <p>Brasil (2022c). Cnes - cadastro nacional de estabelecimentos de saúde. Available from: <a href="http://cnes.datasus.gov.br/">http://cnes.datasus.gov.br/</a> .</p> <p>Brasil (2022d). Departamento penitenciário nacional. levantamento nacional de informações penitenciárias. Available from: <a href="https://www.gov.br/depen/pt-br/servicos/sisdepen">https://www.gov.br/depen/pt-br/servicos/sisdepen</a> .</p> <p>Brasil (2022e). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados">https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados</a> .</p> <p>Brasil (2022f). Ministério da saúde - sistema de informações ambulatoriais do sus (sia/sus). Available from: <a href="https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/">https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/</a> .</p> <p>Valentim, J., Oliveira, E. d. S. G., Valentim, R. A. d. M., Dias-Trindade, S., Dias, A. d. P., Cunha-Oliveira, A., et al. (2021). Data report: “health care of persons deprived of liberty” course from brazil’s unified health system virtual learning environment. Frontiers in Medicine 8. doi:10.3389/fmed.2021.742071.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE “HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM” AND ITS IMPACTS <br> </p> <p><strong>AUTHORS:</strong></p> <p>Janaína L. R. S. Valentim<sup>1,2</sup>, Sara Dias-Trindade<sup>2,3</sup>, Eloiza da S. G. Oliveira<sup>1,4</sup>, José A. M. Moreira<sup>2,5</sup>, Felipe Fernandes<sup>1</sup>, Manoel Honório Romão<sup>1</sup>, Philippi S. G. de Morais<sup>1</sup>, Alexandre R. Caitano<sup>1</sup>, Aline P. Dias<sup>1</sup>, Carlos A. P. Oliveira<sup>1,4,6</sup>, Karilany D. Coutinho<sup>1</sup>, Ricardo B. Ceccim<sup>7</sup>, Ricardo A. M. Valentim<sup>1</sup></p> <p> </p> <p><sup>1</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>2</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Coimbra, Portugal</p> <p><sup>3</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Faculty of Arts and Humanities, Coimbra, Portugal</p> <p><sup>4</sup>Multidisciplinary Institute for Human Development with Technologies, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil</p> <p><sup>5</sup>Open University (Universidade Aberta), Department of Education and Distance Learning (DEED), Lisbon, Portugal</p> <p><sup>6</sup>International Council for Open and Distance Education, Oslo, Norway</p> <p><sup>7</sup>Postgraduate Program in Education, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil</p> <p> </p> <p> </p>
Open Science as a nest that welcomes, as caring; and with openness to the world
<p>New infographic proposal on Open Science after those of the umbrella and the mushroom; care and welcome are added</p>
Supplementary material for: "Typology of Dementia-Specific Care Units: A Nationwide Survey Study in Germany"
<p>This is a dataset and R code of statistical software R version 4.2.1 (2022-06-23) to develop a typology focusing on dementia-specific care units in German nursing homes.</p> <p>The dataset is based on a national survey. For this purpose, 2020, a stratified, randomized sample of 134 care units was included. A telephone interview with facility managers and a standardized questionnaire were used to collect 28 organization-specific variables, which were analyzed as constituent variables for a typology. In addition, the dataset contains seven variables on nonpharmacological interventions (Drugs, Pain, Behavior, Training, Expert, DCM, Music), a variable on "Provider of the nursing home" (Provider) and the variable (sub-question) about "Admission criteria for residents contractually regulated with cost bearers" (Criteria) that were queried for the care units to analyze associations related to typology.</p>
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
<p>Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.</p>
Effect of the Increased Nursing Attrition Rate on Nursing Administration Process during the Covid-19 Pandemic in a Selected Tertiary Care Hospital
<p><span>During<span> </span>the<span> </span>COVID-19<span> </span>outbreak,<span> </span>healthcare<span> </span>professionals,<span> </span>particularly<span> </span>nurses,<span> </span>were<span> </span>more<span> </span>prone to<span> </span>diseases.<span> </span>Globally<span> </span>attrition<span> </span>rate<span> </span>was<span> </span>high<span> </span>among<span> </span>nurses<span> </span>and<span> </span>during<span> </span>the<span> </span>pandemic,<span> </span>it<span> </span>increased because<span> </span>of<span> </span>various<span> </span>reasons<span> </span>such<span> </span>as<span> </span>the<span> </span>risk<span> </span>of<span> </span>infection,<span> </span>occupational<span> </span>and<span> </span>psychological<span> </span>stress, causing risk to their loved ones. This led to a chaotic situation where nurse managers were forced to implement specific strategic plans to deal with increased nurse attrition. This study aims<span> </span>to<span> </span>describe<span> </span>the<span> </span>impact<span> </span>of<span> </span>nurse<span> </span>attrition<span> </span>rate<span> </span>on<span> </span>nursing<span> </span>administration<span> </span>during<span> </span>COVID-19 at a selected tertiary care hospital. The research approach adopted in this study is descriptive cross-sectional. A total sample of 66 nurses involved in nursing administration. The data is collected through a structured questionnaire and the nurse attrition data during the COVID-19 pandemic period was collected from the interview method during the survey. Statistical tests used were frequency, percentage, mean, Standard Deviation (S.D). The study showed that there is a moderate impact of increased nurse attrition on nursing administration during the COVID-19 pandemic. The study led to the identification of gaps that need to be addressed in a similar crisis.</span></p>
Environmental efficiency estimates for 127 Swiss acute care hospitals
<p>These are the estimates from our enviromental efficiency estimates using frontier analysis (Stochastic Frontier Analysis and Data Envelopment Analysis) for 127 Swiss acute care hospital in 2018.</p> <p>For further details please see our working paper: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3939627">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3939627</a></p>
Incidence and Characteristics of Adverse Events in Paediatric Inpatient Care: a Systematic Review and Meta-Analysis
<p>This is the open data repository for the connected systematic review and meta-analysis.</p> <p>Data sets for the meta-analysis.</p> <p>Data collection file with all the information extracted from the included studies.</p> <p>QAT file with the information from the quality assessment tool (QAT) for all included studies.</p> <p>ReadMe with information on data sets and updates.</p> <p>Codebooks for data sets.</p> <p>R Code for the analysis</p>
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad
<p>Here we are uploading a data sheet of the<strong> "Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad." </strong></p>
Simulation code and simulated data for: Transient polymorphisms in parental care strategies drive divergence of sex roles
<p>This repository contains C++ code, simulated datasets, an R-script for data analysis and a Mathematica notebook for mathetical analysis.</p><p>Datasets are organised into ZIP files named after the corresponding figure in the publication. All of the figures based on simulation data in the manuscript and supplementary materials can be created with the R-script. For further information see the article published in <i>Nature Communications (</i>doi:<i> </i>https://doi.org/10.1038/s41467-023-42607-6).</p><p> </p><p> </p><p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.