Priority Nursing Populations for Mental Health Support Before and During COVID-19: A Survey Study of Individual and Workplace Characteristics
Bibliographic record
Abstract
BACKGROUND: Nursing is a high-risk profession and nurses' exposure to workplace risk factors such as heavy workloads and inadequate staffing is well documented. The COVID-19 pandemic has exacerbated nurses' exposure to workplace risk factors, further deteriorating their mental health. Therefore, it is both timely and important to determine nursing groups in greatest need of mental health interventions and supports. PURPOSE: The purpose of this study is to provide a granular examination of the differences in nurse mental health across nurse demographic and workplace characteristics before and after COVID-19 was declared a pandemic. METHODS: This secondary analysis used survey data from two cross-sectional studies with samples (Time 1 study, 5,512 nurses; Time 2, 4,523) recruited from the nursing membership (∼48,000) of the British Columbia nurses' union. Data was analyzed at each timepoint using descriptive statistics and ordinal logistic regression. RESULTS: Several demographic and workplace characteristics were found to predict significant differences in the number of positive screenings on measures of poor mental health. Most importantly, in both survey times younger age was a strong predictor of worse mental health, as was full-time employment. Nurse workplace health authority was also a significant predictor of worse mental health. CONCLUSIONS: Structural and psychological strategies must be in place, proactively and preventively, to buffer nurses against workplace challenges that are likely to increase during the COVID-19 crisis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".