Perceived Need for and Perceived Sufficiency of Mental Health Care in the Canadian Armed Forces
Bibliographic record
Abstract
OBJECTIVES: Failure to perceive need for care (PNC) is the leading barrier to accessing mental health care. After accessing care, many individuals perceive that their needs were unmet or only partially met, an additional problem related to perceived sufficiency of care (PSC). The Canadian Armed Forces (CAF) invested heavily in workplace mental health in the past decade to improve PNC/PSC; yet, the impact of these investments remains unknown. To assess the impact of these investments, this study 1) captures changes in PNC/PSC over the past decade in the CAF and 2) compares current PNC/PSC between the CAF and civilians. METHODS: Data were drawn from the 2013 and 2002 CAF surveys and the 2012 civilian mental health survey (total N = ∼40 000), conducted by Statistics Canada using similar methodology. Exclusions were applied to the civilian sample to make them comparable to the military sample. Prevalence rates for No need, Need met, Need partially met, and Need unmet categories across service types (Information, Medication, Counselling and therapy, Any services) were calculated and compared between 1) the 2 CAF surveys and 2) the 2013 CAF and 2012 civilian surveys after sample matching. RESULTS: Reports of Any need and Need met were higher in the CAF in 2013 than in 2002 by approximately 6% to 8% and 2% to 8%, respectively, and higher in the CAF than in civilians by 3% to 10% and 2% to 8%, respectively. CONCLUSIONS: These results suggest that investments in workplace mental health, such as those implemented in the CAF, can lead to improvements in recognizing the need for care (PNC) and subsequently getting those needs met (PSC).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".