An Intersectional Analysis of Moral Distress and Intention to Leave Employment Among Long-Term Care Providers in British Columbia
Bibliographic record
Abstract
Objectives: In this study, we aimed to explore the relationship between intersectional inequities and moral distress among those working in Long-Term Care (LTC) in British Columbia, Canada. Methods: This was a cross-sectional and retrospective study. We assessed moral distress, of 1678 respondents, using a modified Moral Distress Scale, and an equivalent distress mitigation score, at the intersections of gender and racial/ethnic identity. Then, we explored which worker attributes were more predictive of intention to leave work. Results: We found notable difference in experiences of moral distress across intersecting identities, including high moral distress scores among Indigenous men and women, and white women. Significant differences in mitigation scores were also found by intersectional identities. Discussion: Moral distress was the most important predictor of intention to leave work. The differences across racial and gender identity groups suggest the need for tailored interventions to address moral distress among LTC providers.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".