From Moral Distress to Moral Integrity: Qualitative Evaluation of a New Moral Conflict Assessment Tool
Bibliographic record
Abstract
BACKGROUND: Moral distress affects the well-being of health care professionals and can lead to burnout and attrition. Assessing moral distress and taking action based on this assessment are important. A new moral conflict assessment (MCA) designed to prompt action was developed and tested. OBJECTIVE: To evaluate the utility of the MCA. METHODS: All intensive care unit professionals in 3 hospitals were invited to attend a presentation about the MCA and to participate in semistructured interviews that followed the steps of the MCA. Transcriptions of interviews were interpreted by using qualitative content analysis. RESULTS: Analysis of individual interviews of 7 participants and 1 focus group of 3 participants revealed that the MCA was a catalyst for expressing feelings and characterizing moral distress, but optimal use required a facilitator. Participants noted that prevention and amelioration of moral distress were determined by organizational culture issues such as consistent understanding of what can be accomplished in the intensive care unit, resolution of power imbalances among staff, and psychological safety to mention moral issues. Structural determinants included disparate work and education schedules between nurses and physicians. Leader determinants included listening to staff and ensuring accountability to address causes and consequences of moral distress. Education and communication were proposed most often as solutions for moral distress. CONCLUSIONS: The evaluation revealed positive and negative features of the MCA. Prevention and amelioration of moral distress require attention to cultural, structural, and leadership issues through education and communication.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 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".