The psychological effect of the COVID-19 pandemic on rural appalachian nurses
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected all individuals around the globe, spreading rapidly in late 2019. Confirmed cases of COVID-19 continue to be on the rise causing healthcare facilities to be overwhelmed. In the Appalachian region of the US, the pandemic has been devastating for the community. Healthcare workers on the frontline have had to respond to the pandemic by working directly with patients infected in both the hospital and community setting and have placed themselves and their families at risk of physical and psychological harm. Nurses are especially at risk due to their close personal contact with patients with COVID-19. The aim of this study was to assess the psychological impact of the pandemic on rural Appalachian nurses working directly with patients with COVID-19 in the acute care hospital setting. A cross-sectional design was utilized for this study. The Depression, Anxiety, Stress Scale (DASS-21) was administered to 77 nurses. The results of the study found that 66.3\% of the nurses self-reported their level of depression between a moderate to extremely severe level when caring for COVID-19 patients. In addition, 72.8% of participants reported their anxiety level between moderate to extremely severe and finally, 58.5% reported their stress levels between moderate to extremely severe during this time period. Based on the results of this study, it is imperative that healthcare facilities develop strategies and interventions to address the physical and mental health needs of nurses caring for patients in these stressful work environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".