Mental Health Service Use in Depressed Military Personnel: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Major depression is a leading cause of morbidity in military personnel and an important impediment to operational readiness in military organizations. Although treatment options are available, a large proportion of individuals with depression do not access mental health services. Quantifying and closing this treatment gap is a public health priority. However, the scientific literature on the major depression treatment gap in military organizations has never been systematically reviewed. METHODS: We systematically searched the EMBASE, MEDLINE, and PsychINFO databases for studies measuring recent mental health service use in personnel serving in the armed forces of a Five-Eye country (Australia, Canada, New Zealand, the United Kingdom, or the United States). We excluded studies conducted with retired veterans. Because of the substantial heterogeneity in included studies, we did not pool their results. Instead, we computed median period prevalence of mental health service use. RESULTS: Twenty-eight studies were included in the systematic review; 12 had estimated mental health service use in personnel with depression, and another 16 had estimated mental health service use in personnel with depression or another mental health disorder. The period prevalence of mental health service use in depressed military personnel ranged from 20 to 75% in 12 included studies, with a median of 48%, over 2-12 months. The other 16 studies yielded similar conclusions; they reported period prevalence of mental health service use in personnel with any mental health disorder ranging from 14 to 75%, with a median of 36%, over 1-12 months. The median was higher in studies relying on diagnostic interviews to identify depressed personnel, compared to studies relying on screening tools (60% vs. 44%). CONCLUSIONS: There is a large treatment gap for major depression in particular, and for mental health disorders in general, among military personnel. However, our results highlight the association between the use of measurement tools and treatment gaps: estimated treatment gaps were larger when depressed patients were identified by screening tools instead of diagnostic interviews. Researchers should be wary of overestimating the mental health treatment gap when using screening tools in future studies.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".