Pathways to mental health care in active military populations across the Five-Eyes nations: An integrated perspective
Bibliographic record
Abstract
Military service is associated with increased risk of mental health problems. Previous reviews have pointed to under-utilization of mental health services in military populations. Building on the most recent systematic review, our narrative, critical review takes a complementary approach and considers research across the Five-Eyes nations from the past six years to update and broaden the discussion on pathways to mental healthcare in military populations. We find that at a broad population level, there is improvement in several indicators of mental health care access, with greater gains in initial engagement, time to first treatment contact, and subjective satisfaction with care, and smaller gains in objective indicators of adequacy of care. Among individual-level barriers to care-seeking, there is progress in improving recognition of need for care and reducing stigma concerns. Among organizational-level barriers, there are advances in availability of services and cultural acceptance of care-seeking. Other barriers, such as concerns around confidentiality, career impact, and deployability persist, however, and may account for some remaining unmet need. To address these barriers, new initiatives that are more evidence-based, theoretically-driven, and culturally-sensitive, are therefore needed, and must be rigorously evaluated to ensure they bring about additional improvements in pathways to 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".