Mitigating Moral Distress in Leaders of Healthcare Organizations: A Scoping Review
Bibliographic record
Abstract
GOAL: Moral distress literature is firmly rooted in the nursing and clinician experience, with a paucity of literature that considers the extent to which moral distress affects clinical and administrative healthcare leaders. Moreover, the little evidence that has been collected on this phenomenon has not been systematically mapped to identify key areas for both theoretical and practical elaboration. We conducted a scoping review to frame our understanding of this largely unexplored dynamic of moral distress and better situate our existing knowledge of moral distress and leadership. METHODS: Using moral distress theory as our conceptual framework, we evaluated recent literature on moral distress and leadership to understand how prior studies have conceptualized the effects of moral distress. Our search yielded 1,640 total abstracts. Further screening with the PRISMA process resulted in 72 included articles. PRINCIPAL FINDINGS: Our scoping review found that leaders-not just their employees- personally experience moral distress. In addition, we identified an important role for leaders and organizations in addressing the theoretical conceptualization and practical effects of moral distress. PRACTICAL APPLICATIONS: Although moral distress is unlikely to ever be eliminated, the literature in this review points to a singular need for organizational responses that are intended to intervene at the level of the organization itself, not just at the individual level. Best practices require creating stronger organizational cultures that are designed to mitigate moral distress. This can be achieved through transparency and alignment of personal, professional, and organizational values.
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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 | 0.018 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".