An intersectional analysis of moral distress and intention to leave employment among long-term care providers in British Columbia during the COVID-19 pandemic
Bibliographic record
Abstract
Long-Term Care (LTC) workers have reported declining emotional well-being over the past few years. In this study, we aimed to explore the relationship between intersectional inequities and moral distress among those working in LTC in British Columbia, Canada. This was an observational, cross-sectional, and retrospective study administered through a survey. Data collection occurred between October and December 2022. We estimated mean levels of moral distress through an adapted version of the Moral Distress Scale (MDS) at the two-way intersections of gender and racial/ethnic identity among LTC providers. We also estimated an equivalent distress mitigation score. Then, we explored which worker attributes (i.e., gender, racialized experiences, profession, work arrangement, migratory information, and professional distress levels) were more predictive of intention to leave work using a Random Forest model. We found notable difference in experiences of moral distress across intersecting identities, including high moral distress scores among Indigenous men (77.0, 95% CI: 65.7 to 88.4) and women (72.43, 95%CI: 65.44 to 79.43) and white women (68.17, 95% CI: 65.67 to 70.67). Significant differences in mitigation scores were also found by intersectional identities. The most distressing experiences of white women and women of colour (~80% of our sample) were those related to workload, lack of time with residents, and lack of time for self. 73.5% of Indigenous women were considering leaving their position. Moral distress was the most important predictor of intention to leave work. The differences across racial and gender identity groups, in both sources of distress and preferred mitigation strategies, suggest the need for tailored interventions to address moral distress among LTC providers. Further research and collaboration with stakeholders are warranted to develop comprehensive strategies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".