Barriers to mental health support among Canadian Veterans: Complicating factors of confidentiality and moral injury
Bibliographic record
Abstract
Introduction: Military members often encounter high-stakes, morally challenging situations during their service (also known as potentially morally injurious events; PMIEs). Such experiences put military members at a higher risk of developing mental health concerns. Additionally, in response to PMIEs, some individuals may experience moral injury (MI)-profound and long-lasting psychosocial, spiritual, and functional impairments. There is concern that individuals with MI may be particularly hesitant to seek mental health services. This hesitancy may stem from the socially withdrawing nature of moral emotions and apprehensions about confidentiality during treatment seeking, as some PMIEs may be perceived as immoral, unethical, or illegal, depending on the context. Methods: Qualitative interviews (n = 20) were conducted to explore the barriers to mental health support-seeking experienced by Canadian Armed Forces Veterans, placing an emphasis on the nuanced challenges of MI, including perceived confidentiality concerns and whether PMIEs impact the decision to seek care. Results: Eleven barriers were found; concerns related to confidentiality, career, stigma, trust, legal, relationships, shame and guilt, and accessibility to and adequacy of support. Discussion: While numerous barriers to mental health support are common across various mental health issues, this study revealed that specific barriers, notably guilt, shame, and fear of reprimand, are uniquely pronounced in MI. There is a need to demarcate MI from other mental health challenges when considering treatment-seeking barriers. Effective communication and review of confidentiality assurances are recommended as viable approaches to reducing barriers to treatment and ensuring that Veterans are adequately supported in their mental health journeys.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".