Help-seeking for mental health issues in deployed Canadian Armed Forces personnel at risk for moral injury
Bibliographic record
Abstract
Objective: Potentially morally injurious experiences (PMIE) (events that transgress an individual’s subjective moral standards) have been associated with psychologically distressing moral emotions such as shame and guilt. Military leaders and clinicians have feared that those with PMIEs may be less likely to seek help due to the withdrawing nature of shame/guilt; however, to date, help-seeking patterns of military personnel with PMIEs has not been explored. Our objective is to address this research gap.Method: Data from a nationally-representative mental health survey of active Canadian military personnel were analysed. To assess the association between exposure to three PMIEs and past-year help-seeking across different provider categories (i.e. professionals, para-professionals (those delegated with mental health advisory tasks but are not licenced to practice as medical professionals), non-professionals), a series of logistic regressions were conducted, controlling for exposure to other deployment and non-deployment-related psychological trauma, psychiatric variables, military factors, and sociodemographic variables. Analytical data frame included only personnel with a history of Afghanistan deployment (N = 4854).Results: Deployed members exposed to PMIEs were more likely to seek help from their family doctor/general practitioner (OR = 1.72; 95%CI = 1.25–2.36), paraprofessionals (OR = 1.72; 95%CI = 1.25–2.36), and non-professionals (OR = 1.44; 95%CI = 1.06–1.95) in comparison to members not exposed to PMIEs. Those exposed to PMIEs were also more likely to seek professional care from the civilian health care system (OR = 1.94; 95%CI = 1.27–2.96).Conclusion: Contrary to long-held, but untested, assumptions regarding the impact of PMIEs on help-seeking, we found those with PMIEs are more likely to seek help from gatekeeper professionals (i.e. general practitioners), para-professionals, and non-professionals rather than specialized mental health professionals (e.g. psychologists). Increased utilization of civilian professionals raises concerns that active military members may be avoiding military health services. Clinically, this highlights the need to increase awareness of moral injury to ensure that actively serving military members are provided with appropriate advice and treatment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".