Working through a pandemic: The mediating effect of nurses' health on the relationship between working conditions and turnover intent
Bibliographic record
Abstract
AIM: While research has demonstrated that nurses' health and working conditions are important predictors of turnover in COVID-19, the relationship between these factors is not well understood. Our study investigated the mechanism through which working conditions and nurses' physical and mental health could impact intent to leave the nursing profession. DESIGN: Secondary data from a cross-sectional survey of 3478 nurses in British Columbia administered in May 2021 were analysed using structural equation modelling. METHODS: Two models were assessed utilizing workplace conditions as the predictor, nurses' health as the mediator, and reported turnover intent (Model 1), and anticipated time to turnover (Model 2) as the outcomes. RESULTS: Nurses' health partially mediated the relationship between working conditions and turnover intent, where poorer workplace conditions were directly and indirectly associated with greater likelihood of leaving the profession. Nurses' health fully mediated the relationship between working conditions and nurses' anticipated time to turnover, after controlling for age. The findings from this study underscore the importance of enhancing working conditions and improving nurses' mental health and safety on the job. PATIENT OR PUBLIC CONTRIBUTION: The British Columbia Nurses' Union provided the data for this study; survey data from 3478 nurses were utilized in our study.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".