Health Care Providers’ Well-being Indicators are Associated With Their Intention to Leave Their Positions: A Cross-Sectional Study From Saskatchewan, Canada
Bibliographic record
Abstract
This study aimed to measure the intention to leave and well-being indicators (ie, job satisfaction, burnout, moral distress, risk of depression, and resilience) of health care providers (HCPs) in Saskatchewan, Canada and to explore the association between their intention to leave and well-being indicators and other demographic factors, including gender. A cross-sectional study was conducted among registered nurses (RNs), physicians, and respiratory therapists (RTs) in Saskatchewan between December 2021 and April 2022. An online survey inquired about intentions to leave current positions, well-being indicators, and demographics of HCPs. Logistic regression models explored associations between intention to leave current positions and HCPs' well-being indicators. Adjusted odd ratios (AORs) and 95% confidence intervals (95% CI) are reported. In total, 1497 HCPs participated; 38.6% considered leaving their positions. Controlling by gender, age group, children at home, redeployment, burnout, and resilience levels, the odds of considering leaving their positions decreased by 0.55 (95% CI = 0.43-0.70) per unit of increase in job satisfaction. HCPs experiencing high moral distress were more likely to consider leaving their positions (AOR = 3.97, 95% CI = 2.93-5.39). RNs were more likely to consider leaving their positions than physicians (AOR = 1.68, 95% CI = 1.13-2.50). Age interacted with gender, and burnout interacted with children at home. The job satisfaction, distress levels, and RN designation predicted HCPs' intention to leave. We must recognize the dissimilar effect of age on the intention to leave between women and men and the effect of burnout between those with and without children. Strategies to increase retention of HCPs should consider well-being indicators and focus on reducing morally distressing environments and redeployment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".