Moral Injury, Chaplaincy and Mental Health Provider Approaches to Treatment: A Scoping Review
Bibliographic record
Abstract
The aim of this research was to describe the evidence examining the approaches taken by mental health providers (MHPs) and chaplains to address symptoms related to moral injury (MI) or exposure to potentially morally injurious events (PMIEs). This research also considers the implications for a holistic approach to address symptoms related to MI that combines mental health and chaplaincy work. A scoping review of literature was conducted using Medline, PsycINFO, Embase, Central Register of Controlled Trials, Proquest, Philosphers Index, CINAHL, SocINDEX, Academic Search Complete, Web of Science and Scopus databases using search terms related to MI and chaplaincy approaches or psychological approaches to MI. The search identified 35 eligible studies: 26 quantitative studies and nine qualitative studies. Most quantitative studies (n = 33) were conducted in military samples. The studies examined interventions delivered by chaplains (n = 5), MHPs (n = 23) and combined approaches (n = 7). Most studies used symptoms of post-traumatic stress disorder (PTSD) and/or depression as primary outcomes. Various approaches to addressing MI have been reported in the literature, including MHP, chaplaincy and combined approaches, however, there is currently limited evidence to support the effectiveness of any approach. There is a need for high quality empirical studies assessing the effectiveness of interventions designed to address MI-related symptoms. Outcome measures should include the breadth of psychosocial and spiritual impacts of MI if we are to establish the benefits of MHP and chaplaincy approaches and the potential incremental value of combining both approaches into a holistic model of care.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".