Discontinuation of mental health treatment among Canadian military personnel
Bibliographic record
Abstract
Introduction: Mental health problems are prevalent among Canadian Armed Forces (CAF) personnel. Despite ongoing efforts to promote mental health help seeking, treatment non-completion remains an overlooked issue in military settings. This study sought to provide estimates of past-year mental health treatment discontinuation among active CAF personnel, common reasons for discontinuation, and factors associated with treatment non-completion. Methods: Data from a nationally representative, cross-sectional mental health survey of active CAF Regular Force (RegF; n = 6,696) and Reserve Force (ResF; n = 1,469) personnel were analyzed. Predictors of treatment non-completion were examined using a series of logistic regressions. Results: Among RegF members, 20.8% sought mental health treatment in the past year. Of this sub-group, 38.4% discontinued all forms of treatment within the same year. Notably, only 26.6% of those who discontinued reported doing so because they completed the recommended course of treatment. Similar patterns were found among ResF personnel. Among RegF members, higher education, being married or in a common-law relationship, being a senior non-commissioned member, having a history of childhood maltreatment, and lower social support were associated with an increased likelihood of treatment non-completion. Common reasons for non-completion included feeling better, thinking treatment was not helping, and not being comfortable with the approach. Discussion: This study highlights the complexities of military mental health services provision and offers the first nationally representative analysis of treatment discontinuation in a Canadian military population. Recognizing the reasons for treatment discontinuation may enable future initiatives designed to enhance treatment completion among active military personnel.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".