Rural nurses experiences during covid-19
Bibliographic record
Abstract
Background COVID-19 brought about unprecedented changes to health care systems, putting a strain on nurses, including those in rural hospitals. The accounts of nurses who worked during COVID-19 can help to increase understanding of this strain and how nurses can be supported during such crises. The aim of this study was to increase the understanding of rural acute care nurses’ experiences during COVID-19 and identify what challenged and supported them. Methods This study was completed using a qualitative description design, supported by the Society-to-Cells Resilience Theory. Convenience sampling was used to recruit participants with a target sample size of 10-30 nurses. Semi-structured interviews were conducted between March and May of 2023 via Zoom software; lasting from 60 to 90 minutes each. Content analysis was conducted by the primary researcher, with checks by a secondary researcher on two interviews for coding accuracy. Results Six Ontario rural acute care nurses participated in the study. Three related categories of factors emerged from the analysis; individual, workplace, and community factors. At the individual level, nurses faced social isolation, but were supported by their family and their own optimism. A key workplace factor that contributed to their distress was poor working conditions, including ineffectual management. However, they were supported emotionally by their coworkers. Lastly, the community could have a positive or negative effect depending on how supportive they were of nurses during COVID-19. Conclusion Strong support systems, resource availability in the workplace, and active and supportive management increased nurses’ well-being and resilience in the rural workplace. These findings can be used to inform future policy and management decisions in rural workplaces, especially during times of crisis, to prevent turnover and worsened mental health in rural nurses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".