Determinants of healthcare workers' job retention during the global health crisis: insights from a national survey in Canada
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to investigate the determinants of job retention intention among healthcare workers (HCWs) in Canada during the COVID-19 pandemic. DESIGN/METHODOLOGY/APPROACH: Data are from a large nationally representative cross-sectional survey conducted by the Canadian National Statistics Agency. Ordered logistic regression is estimated to find an association between job retention and its main determinants as gleaned from the literature while controlling for a wide range of pertinent covariates. Odds and standardized odds are reported and discussed. FINDINGS: The results suggest that worsening working conditions, changes in health and well-being and lack of organizational support weaken intentions regarding job retention. Being employed rather than self-employed and working as a nurse also weakens job retention. ORIGINALITY/VALUE: This is the first research on the determinants of intentions regarding job retention in Canada using nationally representative data. It allows us to test and confirm the results of previous studies on a large sample of Canadian HCWs. The paper also discusses the implications of the findings for health management and administration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".