Sharing of military Veterans’ mental health data across Canada: A scoping review
Bibliographic record
Abstract
Introduction: Data on Canadian military Veterans' mental health are needed to develop and improve mental health services. It is not clear to what extent such data are available and connected across sources. Methods: A scoping review of peer-reviewed journal articles (white literature) and government and organizational resources (grey literature) was conducted to identify sources of Canadian Veteran mental health data, linkages between them, and policies or guidelines related to information sharing across sources. Results: Ten different data sources related to military Veterans' mental health in Canada were found, particularly Statistics Canada data sets and administrative data sets such as those of Veterans Affairs Canada. These data sources are shared in a few ways that have advantages and disadvantages. No policies or guidelines were found that specifically address information sharing across these data sets. Discussion: Secure, Accessible, eFfective, and Efficient (SAFE) information sharing across these data sources is implied but not confirmed. Key challenges involve lack of centralization (or coordination), lack of (systematic) collaboration, lack of specific policies (and guidelines), and lack of standardization. It is recommended that consideration be given to establishing a repository of relevant data sets, policies, and guidelines for information sharing across all relevant data sets (addressing collaboration across Canadian jurisdictions and SAFEty measures) and standardization across such data sets.
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".