Mental health services use among Canadian Armed Forces members and Veterans: Data from the 2018 Canadian Armed Forces members and Veterans mental health follow-up survey (CAFMVHS)
Bibliographic record
Abstract
Background: Differences in healthcare delivery systems and pathways to mental healthcare for Canadian Armed Forces (CAF) members and Veterans may contribute to variations in mental health services use (MHSU) and the factors associated with it. We: (1) estimated the prevalence of past 12-month MHSU (≥1 visit with a medical or mental health professional); and (2) identified sociodemographic, military-, trauma-, and health-related variables associated with MHSU among CAF members and Veterans. Methods: The current study used data from the 2018 CAF Members and Veterans Mental Health Follow-Up Survey (CAFVMHS). Model variables were selected a priori, and their respective associations with MHSU were estimated among (1) CAF members and (2) Veterans using separate multivariable logistic regression models. Results: Similar proportions of CAF members and Veterans reported past 12-month MHSU (26.9 vs. 27.5%, respectively). For both CAF members and Veterans, meeting criteria for at least one past 12-month MH disorder was associated with past 12-month MHSU [adjusted odds ratio (AOR) = 7.80, 95% confidence interval (CI) = 7.18-8.46; and AOR = 11.82, 95% CI: 11.07-12.61, respectively). Past-year suicide ideation, a history of sexual trauma, and endorsement of adverse childhood experiences were also significantly associated with MHSU among CAF members and Veterans. Significance: Similar to previous research, meeting screening criteria for a past 12-month MH disorder was strongly associated with MHSU among both samples. This study extends our existing knowledge about factors associated with MHSU among CAF members and Veterans, and offers direction for future research to increase MHSU.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".