Patterns and Predictors of Treatment Delay for Mental Disorders in a Nationally Representative, Active Canadian Military Sample
Bibliographic record
Abstract
BACKGROUND: Although mental disorders constitute a significant public health problem in military populations, little is known about whether military members seek mental health treatment in a timely manner. OBJECTIVE: The objective of this study was to examine delays in making the initial treatment contact for various mental disorders in a military population. DESIGN: A cross-sectional analysis was conducted using data from the Canadian Community Health Survey-Canadian Forces Supplement. SUBJECTS AND MEASURES: Participants (N = 8441) were assessed for mood and anxiety disorders, using the World Health Organization's Composite International Diagnostic Interview. Those meeting criteria for at least 1 disorder in their lifetime were included in the analyses. RESULTS: :The majority (82%-100%) of military members with a DSM-IV disorder eventually seek treatment. However, there are significant delays in seeking treatment. Median delays for major depressive disorder, generalized anxiety disorder, posttraumatic stress disorder, panic disorder, and social phobia are 3, 3, 7, 8, and 26 years, respectively. For deployment related posttraumatic stress disorder, longer delays are associated with being in an older age cohort, being male, not having comorbid panic disorder, and shorter military service duration. Across all disorders, longer delays are associated with being in an older age cohort, shorter military service duration, and earlier age of onset. CONCLUSIONS: Failure to initiate treatment in a timely manner is a major mental health service access issue in the military context. Interventions that aim to shorten treatment delays are needed and should target military members most at risk for delaying treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".