Repairing moral injury takes a team: what clinicians can learn from combat veterans
Bibliographic record
Abstract
Moral injury results from the violation of deeply held moral commitments leading to emotional and existential distress. The phenomenon was initially described by psychologists and psychiatrists associated with the US Departments of Defense and Veterans Affairs but has since been applied more broadly. Although its application to healthcare preceded COVID-19, healthcare professionals have taken greater interest in moral injury since the pandemic's advent. They have much to learn from combat veterans, who have substantial experience in identifying and addressing moral injury-particularly its social dimensions. Veterans recognise that complex social factors lead to moral injury, and therefore a community approach is necessary for healing. We argue that similar attention must be given in healthcare, where a team-oriented and multidimensional approach is essential both for ameliorating the suffering faced by health professionals and for addressing the underlying causes that give rise to moral injury.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".