Factors that help and factors that prevent Canadian military members’ use of mental health services
Bibliographic record
Abstract
LAY SUMMARY Canadian Armed Forces (CAF) members experience depression at higher rates than civilian Canadians. Mental health services are available, yet members do not always use them, even when needed. The authors hosted focus groups to find out what brings military members to mental health services. The results show that the CAF is dealing with structural barriers, including time for members to go to appointments, confidentiality, language about mental health, and higher ranking members talking about their own experience, which helps members seek help. Military culture, which has changed over the years, makes a difference for military members in either promoting or preventing getting help. Also, personal stigma still exists, and it is one reason members do not use mental health services. Basic training, when members are introduced to military culture, may be a place for higher ranking members to talk about their experiences with mental health help. Leaders’ openness about their use of services and ensuring that leaders know about the resources that exist may continue to foster members’ use of mental health services. Personal-level stigma needs more research.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 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) | 0.012 | 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".