The relationship between moral distress, burnout, and considering leaving a hospital job during the COVID-19 pandemic: a longitudinal survey
Bibliographic record
Abstract
BACKGROUND: Previous research suggests that moral distress contributes to burnout in nurses and other healthcare workers. We hypothesized that burnout both contributed to moral distress and was amplified by moral distress for hospital workers in the COVID-19 pandemic. This study also aimed to test if moral distress was related to considering leaving one's job. METHODS: A cohort of 213 hospital workers completed quarterly surveys at six time-points over fifteen months that included validated measures of three dimensions of professional burnout and moral distress. Moral distress was categorized as minimal, medium, or high. Analyses using linear and ordinal regression models tested the association between burnout and other variables at Time 1 (T1), moral distress at Time 3 (T3), and burnout and considering leaving one's job at Time 6 (T6). RESULTS: Moral distress was highest in nurses. Job type (nurse (co-efficient 1.99, p < .001); other healthcare professional (co-efficient 1.44, p < .001); non-professional staff with close patient contact (reference group)) and burnout-depersonalization (co-efficient 0.32, p < .001) measured at T1 accounted for an estimated 45% of the variance in moral distress at T3. Moral distress at T3 predicted burnout-depersonalization (Beta = 0.34, p < .001) and burnout-emotional exhaustion (Beta = 0.38, p < .008) at T6, and was significantly associated with considering leaving one's job or healthcare. CONCLUSION: Aspects of burnout that were associated with experiencing greater moral distress occurred both prior to and following moral distress, consistent with the hypotheses that burnout both amplifies moral distress and is increased by moral distress. This potential vicious circle, in addition to an association between moral distress and considering leaving one's job, suggests that interventions for moral distress may help mitigate a workforce that is both depleted and burdened with burnout.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".