Moral distress, coping mechanisms, and turnover intent among healthcare providers in British Columbia: a race and gender-based analysis
Bibliographic record
Abstract
Abstract Background This study explores intersectionality in moral distress and turnover intention among healthcare workers (HCWs) in British Columbia, focusing on race and gender dynamics. It addresses gaps in research on how these factors affect healthcare workforce composition and experiences. Methods Our cross-sectional observational study utilized a structured online survey. Participants included doctors, nurses, and in-home/community care providers. The survey measured moral distress using established scales, assessed coping mechanisms, and evaluated turnover intentions. Statistical analysis examined the relationships between race, gender, moral distress, and turnover intention, focusing on identifying disparities across different healthcare roles. Complex interactions were examined through Classification and Regression Trees. Results Racialized and gender minority groups faced higher levels of moral distress. Profession played a significant role in these experiences. White women reported a higher intention to leave due to moral distress compared to other groups, especially white men. Nurses and care providers experienced higher moral distress and turnover intentions than physicians. Furthermore, coping strategies varied across different racial and gender identities. Conclusion Targeted interventions are required to mitigate moral distress and reduce turnover, especially among healthcare workers facing intersectional inequities.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".