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4,954 results for “hospitals”
Psychiatric manifestations and associated risk factors among hospitalized patients with COVID-19 in Edo State, Nigeria: A Cross-sectional Study
<p>The Coronavirus Disease 2019 (COVID-19) has had devastating effects globally. These effects are likely to result in mental health problems at different levels. Although studies have reported the mental health burden of the pandemic on the general population and frontline health workers, the impact of the disease on the mental health of patients in COVID-19 treatment and isolation centres have been understudied in Africa.</p> <p>We estimated the prevalence of depression and anxiety and associated risk factors in hospitalized persons with COVID-19. A cross-sectional survey was conducted among 489 patients with COVID-19 at the three government-designated treatment and isolation centres in Edo State, Nigeria. The 9-item Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) tool were used to assess depression and anxiety respectively. Binary logistic regression was applied to determine risk factors of depression and anxiety. Results Of the 489 participants, 49.1% and 38.0% had depressive and anxiety symptoms respectively. The prevalence of depression, anxiety, and combination of both were 16.2%, 12.9% and 9.0% respectively. Moderate-severe symptoms of COVID-19, ≥14 days in isolation, worrying about the outcome of infection and stigma increased the risk of having depression and anxiety. Additionally, being separated/divorced increased the risk of having depression and having comorbidity increased the risk of having anxiety.</p> <p>A substantial proportion of our participants experienced depression, anxiety and a combination of both especially in those who had the risk factors we identified. The findings underscore the need to address modifiable risk factors for psychiatric manifestations early in the course of the disease and integrate mental health interventions and psychosocial support into COVID-19 management guidelines. --</p>
Glycemic Control After Sleeve Gastrectomy in Taif Hospitals, Kingdom of Saudi Arabia
<p>data for retrospective study based on retrieving the required information from patient's files in Medical Records Departments for type II diabetic overweight and obese patients who underwent LSG in Taif, from January 2017 to December 2019. The follow-up duration for all patient was three-to-six months postoperatively. A total of 96 patients were included in the study. Data collected included baseline characteristics, blood glucose indices and other routine laboratory tests.We conducted a retrospective study based on retrieving the required information from patient's files in Medical Records Departments for type II diabetic overweight and obese patients who underwent LSG in Taif, from January 2017 to December 2019. The follow-up duration for all patient was three-to-six months postoperatively. A total of 96 patients were included in the study. Data collected included baseline characteristics, blood glucose indices and other routine laboratory tests.</p>
Actual and potential drug interactions of psychotropic drugs in patients of the covid-19 medicine service of the Hospital de Emergencia-Lima,2021
<p><strong>Background:</strong> Actual and potential drug-drug interactions of psychotropic drugs in patients of the COVID-19 Medicine Service of the Villa El Salvador Emergency Hospital during the months of February to July 2021.</p> <p><strong>Methods:</strong> The study is deductive, retroprospective, quantitative, applied, cross-sectional observational. The instrument used was a collection card for 86 pharmacotherapeutic follow-ups where psychotropic drugs for anxiety, depression and insomnia were registered.</p> <p><strong>Results:</strong> In the actual and potential drug interactions of psychotropic drugs, according to the degree of severity dimension, it was identified that the important indicator represented the highest frequency of 89% of the interactions; according to the type of interaction dimension, it was identified that the pharmacodynamic indicator presented a higher frequency with 53%; according to the clinical evidence dimension, it was identified that the fair indicator had a higher frequency with 73% interactions; in the manifestation dimension, it was identified that the potential indicator presented a higher frequency with 92.2% interactions. In its moment of appearance dimension, it was identified that the quick indicator had a higher prevalence with 5.5% of real interactions. In its causality algorithm dimension, the probable indicator was identified as having the highest frequency with 7.25% of actual interactions. With respect to sex, the male presented 49.9% of potential interactions and in the real interactions, the female sex presented a higher incidence with 4.3% interactions. The average age of the potential interactions was 48.83 years, and the average age of the real interactions was 45.67 years. Sertraline presented 53.2% of potential interactions and in relation to real drug interactions the one that presented the highest frequency was mirtazapine with 3.5% interactions.</p>
Association between preoperative medication lists and postoperative hospital length of stay after endoscopic transsphenoidal pituitary surgery
<p><strong><span>Background</span></strong><span>:</span> <span>Endoscopic transsphenoidal surgery is the most common technique for resection of pituitary adenoma. Data on factors associated with extended hospital stay after this surgery are limited. We aimed to characterize the relationship between preoperative medications and the risk of prolonged postoperative length of stay after this procedure.</span></p> <p><strong><span>Methods</span></strong><span>:</span> <span>This single-center, retrospective cohort study included all adult patients scheduled for transsphenoidal pituitary surgery from July 1</span><span>st,</span><span> 2016 to December 31</span><span>st</span><span>, 2019.</span> <span>Anatomical Therapeutic Chemical</span><span> codes were used to identify patients' preoperative medications. The primary outcome was prolonged postoperative hospital length of stay. Secondary outcomes included unplanned admission to the Intensive Care Unit, in-hospital and one-year mortality. We developed a descriptive logistic model that included preoperative medications, obesity, and age.</span></p> <p><strong><span>Results</span></strong><span>:</span> <span>Median postoperative length of stay was 3 days for the 7</span><span>04 </span><span>analyzed patients. A prolonged length of stay was</span> <span>defined as > 4 days. Patients taking ATC-H drugs were at increased risk of prolonged length of stay (OR 1.56, 95% CI 1.26-1.95, p<0.001). No association was found between preoperative ATC-H medication and unplanned ICU admission or in-hospital mortality. Patients with multiple preoperative ATC-H medications had significantly higher mean LOS (5.4 ± 7.6 days) and one-year mortality (p<0.02).</span></p> <p><strong><span>Conclusions</span></strong><span>: Clinicians should be aware of the possible vulnerability of patients taking systemic hormones preoperatively. Future studies should test this medication-based approach on endoscopic transsphenoidal pituitary surgery populations from different hospitals and countries.</span></p>
Increase in coercive measures in psychiatric hospitals during the COVID-19 Pandemic
<p class="MsoNormal"><strong><span>Objective</span></strong><span>: To examine whether the pandemic in 2020 caused changes in psychiatric hospital cases, the percentage of patients exposed to coercive interventions, and aggressive incidents. </span></p> <p class="MsoNormal"><span><strong>Results: </strong>The number of cases in adult psychiatry decreased by 7.6% from 105,782 to 97,761. The percentage of involuntary cases increased from 12.3 to 14.1%, and the absolute number of coercive measures increased by 4.7% from 26,269 to 27,514. The percentage of cases exposed to any kind of coercive measure increased by 24.6% from 6.5 to 8.1%, and the median cumulative duration per affected case increased by 13.1% from 12.2 to 13.8 hrs, where seclusion increased more than mechanical restraint. The percentage of patients with aggressive incidents, collected in 10 hospitals, remained unchanged. </span></p> <p class="MsoNormal"><span><strong>Conclusions: </strong>While voluntary cases decreased considerably during the pandemic, involuntary cases increased slightly. However, the increased percentage of patients exposed to coercion is not only due to a decreased percentage of voluntary patients, as the duration of coercive measures per case also increased. The changes that indicate deterioration in treatment quality were probably caused by the multitude of measures to manage the pandemic. The focus of attention and internal rules as well have shifted from prevention of coercion to prevention of infection.</span></p>
Dataset - Literature on service robots in the hospitality industry
<p>List of fifty-nine articles retrieved from Web of Science, Google Scholar, and Scopus databases upon search inquiries with "robot," "hotel," and "hospitality" keywords. Data collection between October 2021 and March 2022. Figures of analysis in three clusters: robot*-customer relationship, robot-employee relationship, and robot-firm relationship. </p>
Supplemental material for the paper entitled "Estimating and planning hospital costs of public hospitals in Brazil"
<p>[xlsx] Stepdown cost accounting (SDCA) for estimating cost on hospitals network;</p> <p>[xlsx] Factor analysis for estimating hospital costs based on few operations variables.</p> <p>[xlsx] Cost Components and Care Modules approaches - results for each hospital of the network.</p> <p>[zip] Simulation-optimisation algorithm and models.</p> <p>[zip] Results from simulation-optimisation model.</p>
Data for "Health of neonates born in the maternity hospital in Bern, Switzerland, 1880-1900 and 1914-1922"
<p>Datasets underlying the analysis of the paper: "Health of neonates born in the maternity hospital in Bern, Switzerland, 1880-1900 and 1914-1922"</p> <p>This upload includes the following two data sets:</p> <ul> <li><strong>Bern_birth.csv</strong> : data from the maternity hospital in Bern for the years 1880- 1900 and 1914-1922 <ul> <li>Year: Year of birth </li> <li>Insurance: 1=yes, 0=no</li> <li>matage: Age of mother</li> <li>married: woman is married yes or no</li> <li>parity: number of parities</li> <li>gest: gestational age</li> <li>birthday2: birthday of newborn</li> <li>Grippe: flu 1=yes</li> <li>weight: birth weight of newborn</li> <li>city: 1=urban, 0=rural</li> <li>Month: birth month of newborn</li> <li>boy: 1= male, 0=femal</li> <li>stillborn: 1=yes, 0=no</li> <li>multiple: multiple births 1=yes, 0=no</li> <li>matheight2: 1=small, 2=medium, 3=tall</li> <li>matbody2: 1=grazil, 2=medium, 3=strong</li> <li>malnutrition2: 1=yes, 0=no</li> <li>occupation: german description of women's occupations</li> <li>occupation2: groups of occupations, 1= farm worker, 2=Maid, 3= Worker, 4=Housewife, 5/6/7=other</li> <li>agemenarche: age of first menstruation</li> </ul> </li> <li><strong>Flu_Bern.csv</strong> : Weekly recorded flu numbers for the canton of Bern <ul> <li>Year: Year of influenca</li> <li>week: week of influenca</li> <li>KW: calender week</li> <li>Canton: Number of influenca cases in canton of Bern</li> </ul> </li> </ul>
Hospital
Photogrammetry of the Hospital Russia, Ryazan A hospital is a health care institution providing patient treatment with specialized medical and nursing staff and medical equipment. The best-known type of hospital is the general hospital, which typically has an emergency department to treat urgent health problems ranging from fire and accident victims to a sudden illness. A district hospital typically is the major health care facility in its region, with many beds for intensive care and additional beds for patients who need long-term care. Specialized hospitals include trauma centers, rehabilitation hospitals, children's hospitals, seniors' (geriatric) hospitals, and hospitals for dealing with specific medical needs such as psychiatric treatment (see psychiatric hospital) and certain disease categories. Specialized hospitals can help reduce health care costs compared to general hospitals. Hospitals are classified as general, specialty, or government depending on the sources of income received. Source: Objaverse 1.0 / Sketchfab
Old Tuberculosis Hospital Entrance
Sadly modern Russian people in St. Petersburg no logner care about old building and only wait for nice time to demolish them. Abandoned and burned old hospital still stands in the city centre awating for it's final hour. Athorities have no plans to restore it and prevent any investors from interfering. Sad! http://www.citywalls.ru/house851.html Source: Objaverse 1.0 / Sketchfab
Week 1-Hospitality Mindset_ Changing The World, One Day At A Time
<p>Introduction To Hospitality</p> <p>Video for Week1,<strong> Hospitality Mindset_Changing The World, One Day At A Time</strong>.</p> <p>How we can each change the world every day by first changing our behavior towards the people who cross our path on a daily basis?</p> <p> </p>
University Hospital Augsburg nursing staff capacity
<p>The dataset contains information on the hourly nursing staff capacity, i.e. the total number of deployed nursing staff, and the number of planned and unplanned absences of the University Hospital in Augsburg. It includes data from four hospital wards of different medical areas and spans a period of one and a half years from January 1st, 2022 to June 30th, 2023. </p>
Dataset of Diagnostics Related Group (DRG) Data of Childbirth Cases in Romanian Hospitals in 2020
<p>This is a supplementary dataset generated for an analysis of Romanian C-Section rates in 2020, through the lens of Diagnosis-Related-Groups (DRG) payment systems. </p>
The Metadata of "The punishment intensity for research misconduct and its related factors: An exploratory study on hospitals in Mainland China"
<p>This is the metadata of the research article <em>The punishment intensity for research misconduct and its related factors: An exploratory study on hospitals in Mainland China.</em></p> <p> </p>
COVID-19 cases in U.S. hospitals with imputed locations and dates
<p>This RDS file contains imputed locations and dates for COVID-19 cases detected in U.S. hospitals. The locations are uniformly sampled within known counties (FIPS), additional covariates of which are included. This file is used for analyses contributing to the paper "Scaling the spatiotemporal Hawkes process to one million COVID-19 cases in U.S. hospitals".</p>
Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk
<p><strong>Paper Title: </strong>Leveraging patients' longitudinal data to improve the Hospital One-year Mortality Risk</p> <p><strong>Paper: </strong><a href="https://doi.org/10.1007/s13755-024-00332-4">https://doi.org/10.1007/s13755-024-00332-4</a> (<span>full-text view-only version: <a title="URL d'origine : https://rdcu.be/eccmN. Cliquez ou appuyez si vous faites confiance à ce lien." href="https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Frdcu.be%2FeccmN&data=05%7C02%7Chakima.laribi%40usherbrooke.ca%7C23870f4657634d7a102908dd5b986df8%7C3a5a8744593545f99423b32c3a5de082%7C0%7C0%7C638767432425362186%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=d9ieen5mU9pPFXGv8hJNF%2Bf5UlJNyhjDuk%2F8MWEKo28%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://rdcu.be/eccmN</a></span>)</p> <p><strong>GitHub Link: </strong><a href="https://github.com/MEDomics-UdeS/POYM" target="_blank" rel="noopener">https://github.com/MEDomics-UdeS/POYM </a></p> <p><strong>Description:</strong></p> <p>This dataset accompanies <a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a> and contains synthetic data generated using the <a href="https://doi.org/10.1038/s41746-023-00771-5" target="_blank" rel="noopener">AVATAR method</a> in partnership with <a href="https://www.octopize.io/" target="_blank" rel="noopener">Octopize</a>.</p> <p><strong>Files:</strong></p> <ul> <li><strong>dataset.csv:</strong> This file contains 248,485 rows and 247 columns, representing 248,485 synthetic visits from 123,646 synthetic patients. Detailed descriptions of each column can be found in <a href="https://doi.org/10.1007/s13755-024-00332-4" target="_blank" rel="noopener">Laribi et al. (2024)</a>. To preserve patient's privacy, we did not save admission and discharge dates. Consequently, it is not possible to split the dataset temporally as done with the original dataset or to identify admissions with same-day discharge.</li> </ul> <p><strong>Comparison of synthetic and original data: </strong><a href="https://doi.org/10.21203/rs.3.rs-5363467/v1">https://doi.org/10.21203/rs.3.rs-5363467/v1</a></p> <p> </p>
Manuscript dataset - Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia
<p>This is a dataset associated with submission of the manuscript "Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia"</p>
Dataset: Quality of Life of Patients with Dementia in Acute Hospitals
<p>Dataset for a publication (working title "Quality of Life of Patients with Dementia in Acute Hospitals: Comparing a regular ward to a special care ward with dementia care concept. A Bayesian Multilevel Model Analysis.").</p>
Assessment of Patient Safety Culture and Associated Factors among Healthcare Professionals in Public Hospitals of Bahir Dar City, Northwest Ethiopia: A Mixed-Methods Study
<p>This data set contains HSOPSC on the <span>Assessment of Patient Safety Culture and Associated Factors among Healthcare Professionals in Public Hospitals of Bahir Dar City, Northwest Ethiopia: A Mixed-Methods Study</span></p>
Study data for the journal article "Mental health of individuals at increased suicide risk after hospital discharge and initial findings on the usefulness of a suicide prevention project in Central Switzerland"
<p>Anonymized raw data of our cross-sectional survey study.</p> <p>id = study participant ID; se1-se10 = questions of the General Self-Efficacy Scale; sm1-sm5 = questions of the Self-Management Self-Test; hl1-hl12 = questions of the Health Literacy Questionnaire (Swiss version); prisms = question on the utilization of the PRISM-S technique; sp1-sp4 = questions on the utilization and perceived usefulness of the personal safety plan; app1-app7 = questions on the utilization and perceived usefulness of the SERO app; ensa = question on the participation in ensa courses; sex-income = sociodemographic questions</p>
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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.