ArticleData in brief2022
Working conditions and stressors data during Covid-19 and mental well-being in Iranian healthcare workers.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- The relationship between fear of COVID-19 and adherence to personal protective measures in a sample of Iranian healthcare providers: a cross-sectional study.BMC infectious diseases · 2024Article
- Burnout and Professional Engagement during the COVID-19 Pandemic among Nursing Students without Clinical Experience: A Cross-Sectional Study.Journal of clinical medicine · 2023Article
- Stress overload, influencing factors, and psychological experiences of nurse managers during early stages of the COVID-19 pandemic: a sequential explanatory mixed method study.Frontiers in psychology · 2023Article
- Working conditions and stressors data during Covid-19 and mental well-being in Iranian healthcare workers.Data in brief · 2022Article
- Risk Factors Associated with Psychological Health and Workload among Clinical Nurses.Iranian journal of nursing and midwifery researchArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current Covid-19 pandemic has affected the physical and mental stressors of hospital-based healthcare workers, but the extent of such effects are required to be quantified. This survey looked at data on nurses' perception across teaching hospitals to assess the impacts of Covid-19 on working conditions, exposure to stressors, and mental health symptoms. We implemented a population survey with a cross-sectional design in teaching hospitals affiliated with Medical Sciences Universities in Iran from April to November 2021. Participants were about 1200 health care workers, including hospital nursing staff, assistants, and technicians. Final data were assembled from 831 hospital nurses across surgery, dialysis, intensive care, emergency care, cardiac care, internal medicine, gynecology, and pediatric wards. Self-reported data were collected directly from survey participants. We collected information on variables including gender, marital status, employment status, occupational health training, evaluation of work environment stressors, fear of Covid-19, and occupational burnout constructs, specifically reflecting emotional exhaustion, depersonalization, and personal accomplishment. Focus groups of faculties evaluated and edited items to test the content wording and to define the content that are valid measures of the variables. The questionnaires were assessed for their reliability. Manual data entries were double-checked for errors. Data were recorded and categorized consistently to ensure the replicability of the data in the future. Statistical descriptive and analytical analyses were performed on the data. Data reported on the frequencies and mean values of responses and the variations of mental health in terms of worktime schedules. Chi- square, ANOVA, and correlation analyses determined relations between variables. The compiled data shed light on the exposure and response to physical and psychosocial factors and mental health symptoms among nurses during the pandemic. The data files detailed in this article can be further reused to inform workplace determinants of health in hospital settings. The obtained scores and existing dataset on mental health outcomes can help future studies to consider resilience strategies that should be provided among nurses.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.