Bibliographic record
Abstract
Mentally dissociating from Potential Moral Injurious Events (PMIEs) is important in building resilience. It eliminates the unnecessary guilt, shame, and emotions associated with moral injuries. This chapter aims to improve the effectiveness of military forces’ resilience-building efforts in the context of Moral Injury (MI) for their well-being and work-life balance. Specifically, this study synthesizes knowledge about soldiers with guilt, shame and loss of trust and how committal or non-committal to an act harms in battle conflict. We used qualitative study and triangulation methodology to develop a framework based on a literature review synthesis, anecdotal evidence and years of the author’s interviews, experience and insight. Our framework indicates that the mental ability to dissociate from PMIE is an essential component of resilience, which serves as a buffer and moderates the relationship between PMIE exposure and MI occurrence. The soldiers should not have long-lasting self-blame, regret, and anger, as they are irrelevant due to the nature of the combatant’s role. This study has two implications for human resource training managers. First, this study develops a framework that can be used to identify factors that contribute to the effectiveness of the resilience-building process. Second, an inverse correlation between dissociation from PMIEs and the mental status of soldiers’ MI diagnosis outcome is a ground for pre-deployment training to include the particular strength in resilience building, which results in striking a work-life balance. For future research, the proposed framework can be empirically tested internationally for its effectiveness across cultures.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".