Do Investments in Mental Health Systems Result in Greater Use of Mental Health Services? National Trends in Mental Health Service Use (MHSU) in the Canadian Military and Comparable Canadian Civilians, 2002-2013
Bibliographic record
Abstract
Background:Mental disorders constitute a significant public health problem worldwide. Ensuring that those who need mental health services access them in an appropriate and timely manner is thus an important public health priority. We used data from 4 cross-sectional, nationally representative population health surveys that employed nearly identical methods to compare MHSU trends in the Canadian military versus comparable civilians.Method:The surveys were all conducted by Statistics Canada, approximately a decade apart (Military-2002, Military-2013, Civilian-2002, and Civilian-2012). The sample size for the pooled data across the surveys was 35,984. Comparisons across the 4 surveys were adjusted for differences in need in the 2 populations at the 2 time points.Results:Our findings suggested that first, in the Canadian military, there was a clear and consistent pattern of improvement (i.e., increase) in MHSU over the past decade across a variety of provider types. The magnitudes of the changes were large, representing an absolute increase of 7.15% in those seeking any professional care, corresponding to an 84% relative increase. Second, in comparable Canadian civilians, MHSU remained either unchanged or increased only slightly. Third, the increases in MHSU over time were consistently greater in the military than in the comparable civilian sample.Conclusions:Our findings point to advantages with respect to MHSU of the military mental health system over the civilian system in Canada; these advantages have widened substantially over time. These findings speak strongly to the potential impact of analogous changes in other health systems, both military and civilian.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".