Critical intercession for non-religious Canadian Veterans on the intersections of moral injury, religion, and spirituality
Bibliographic record
Abstract
With the growing acceptance of the concept of moral injury, there has been increasing interest in the role religion and spirituality play in the risk for, experience of symptoms of, and potential treatment for this form of posttraumatic-stress-related psychological distress. Although many studies indicate the potential for religion and spirituality to contribute to the understanding and treatment of moral injury, it is important to note that this predominantly U.S.-based research tends to naturalize American Christian conceptualizations of religion, frequently conflating religion and spirituality in the process. A key question is whether this culturally specific form of religiosity is applicable to Veterans and military service members who are non-American, non-Christian, polytheistic, or even secular or atheist. On the basis of observations made during long-term ethnographic research with Canadian Veterans, this article amplifies concerns voiced by Veterans that should be considered when applying an American Christian conceptualization of religion and spirituality to non-American, non-Christian individuals or contexts. This intercession draws attention to the possibility that applying ideas of religion and spirituality may not only have limited benefit in the Canadian context, but that it could also create barriers to care and even further harm to non-religious individuals experiencing 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".