Moral injury and the need to carry out ethically responsible research
Bibliographic record
Abstract
The need for research to advance scientific understanding must be balanced with ensuring the rights and wellbeing of participants are safeguarded, with some research topics posing more ethical quandaries for researchers than others. Moral injury is one such topic. Exposure to potentially morally injurious experiences can lead to significant distress, including post-traumatic stress disorder (PTSD), depression, and selfinjury. In this article, we discuss how the rapid expansion of research in the field of moral injury could threaten the wellbeing, dignity and integrity of participants. We also examine key guidance for carrying out ethically responsible research with participants’ rights to self-determination, confidentiality, non-maleficence and beneficence discussed in relation to the study of moral injury. We describe how investigations of moral injury are likely to pose several challenges for researchers including managing disclosures of potentially illegal acts, the risk of harm that repeated questioning about guilt and shame may pose to participant wellbeing in longitudinal studies, as well as the possible negative impact of exposure to vicarious trauma on researchers themselves. Finally, we offer several practical recommendations that researchers, research ethics committees and other regulatory bodies can take to protect participant rights, maximise the potential benefits of research outputs and ensure the field continues to expand in an ethically responsible way.
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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 | 0.374 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.163 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.026 | 0.053 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".