Measuring moral distress and moral injury: A systematic review and content analysis of existing scales
Bibliographic record
Abstract
BACKGROUND: Moral distress (MD) and moral injury (MI) are related constructs describing the negative consequences of morally challenging stressors. Despite growing support for the clinical relevance of these constructs, ongoing challenges regarding measurement quality risk limiting research and clinical advances. This study summarizes the nature, quality, and utility of existing MD and MI scales, and provides recommendations for future use. METHOD: We identified psychometric studies describing the development or validation of MD or MI scales and extracted information on methodological and psychometric qualities. Content analyses identified specific outcomes measured by each scale. RESULTS: We reviewed 77 studies representing 42 unique scales. The quality of psychometric approaches varied greatly across studies, and most failed to examine convergent and divergent validity. Content analyses indicated most scales measure exposures to potential moral stressors and outcomes together, with relatively few measuring only exposures (n = 3) or outcomes (n = 7). Scales using the term MD typically assess general distress. Scales using the term MI typically assess several specific outcomes. CONCLUSIONS: Results show how the terms MD and MI are applied in research. Several scales were identified as appropriate for research and clinical use. Recommendations for the application, development, and validation of MD and MI scales are provided.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.045 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".