Exposure to moral stressors and associated outcomes in healthcare workers: prevalence, correlates, and impact on job attrition
Bibliographic record
Abstract
Introduction: Healthcare workers (HCWs) often experience morally challenging situations in their workplaces that may contribute to job turnover and compromised well-being. This study aimed to characterize the nature and frequency of moral stressors experienced by HCWs during the COVID-19 pandemic, examine their influence on psychosocial-spiritual factors, and capture the impact of such factors and related moral stressors on HCWs’ self-reported job attrition intentions. Methods: A sample of 1204 Canadian HCWs were included in the analysis through a web-based survey platform whereby work-related factors (e.g. years spent working as HCW, providing care to COVID-19 patients), moral distress (captured by MMD-HP), moral injury (captured by MIOS), mental health symptomatology, and job turnover due to moral distress were assessed. Results: Moral stressors with the highest reported frequency and distress ratings included patient care requirements that exceeded the capacity HCWs felt safe/comfortable managing, reported lack of resource availability, and belief that administration was not addressing issues that compromised patient care. Participants who considered leaving their jobs (44%; N = 517) demonstrated greater moral distress and injury scores. Logistic regression highlighted burnout (AOR = 1.59; p < .001), moral distress (AOR = 1.83; p < .001), and moral injury due to trust violation (AOR = 1.30; p = .022) as significant predictors of the intention to leave one’s job. Conclusion: While it is impossible to fully eliminate moral stressors from healthcare, especially during exceptional and critical scenarios like a global pandemic, it is crucial to recognize the detrimental impacts on HCWs. This underscores the urgent need for additional research to identify protective factors that can mitigate the impact of these stressors. This study explored the nature of moral stressors encountered by health care workers, along with impacts on moral injury and intentions to leave their jobs.Morally distressing encounters were common, with the most prevalent and distressing experiences being organizational or team-based in nature.Findings revealed that severity of moral injury, particularly related to trust violation or betrayal, was a key factor influencing healthcare workers’ intentions to leave their jobs. This study explored the nature of moral stressors encountered by health care workers, along with impacts on moral injury and intentions to leave their jobs. Morally distressing encounters were common, with the most prevalent and distressing experiences being organizational or team-based in nature. Findings revealed that severity of moral injury, particularly related to trust violation or betrayal, was a key factor influencing healthcare workers’ intentions to leave their jobs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".