The potentially morally injurious nature of encountering children during military deployments: A call for research
Bibliographic record
Abstract
Armed forces personnel are a population at risk for exposure to potentially traumatic and morally injurious events because of the high-risk nature of military operations. One potentially morally injurious event (PMIE) could be when military personnel encounter children during deployment. These encounters may lead to acute and chronic psychological, behavioural, and social consequences, culminating in moral injury and other adverse mental health problems. According to anecdotal evidence, military personnel reported feeling torn, morally and ethically, in their decision-making when they encounter children in the line of duty. The decision to engage or kill a child may be difficult to reconcile with one's moral and ethical code, and decisions may have deadly consequences for oneself and others. To date, however, no reliable data exist as to the impact that encountering children during deployment may have on psychosocial and spiritual well-being. In this article, additional research into this domain is encouraged by providing a rationale for studying encounters with children during deployment through the lens of a PMIE, as well as relevant contextual and institutional factors to consider when examining the mental health impact of such experiences.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".