Report of the 2016 Mental Health Expert Panel on suicide prevention in the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: An Expert Panel on suicide prevention convened October 23–26, 2016 to review current practices and recommend suicide prevention strategies for the Canadian Forces Health Services (CFHS). It included subject matter experts from Canada, the United States, and the United Kingdom, and representatives from Veterans Affairs Canada (VAC). Methods: We reviewed evidence and best practices for suicide prevention in civilian and military populations as well as the components of the CFHS mental health services and suicide prevention programs, and compared them to current evidence-informed best practices. We suggested improvements for CFHS mental health services and suicide prevention programs, and areas of future inquiry to improve suicide prevention. Results: Over the past 10 years there have been an average 16.6 suicide deaths annually among Canadian Armed Forces (CAF) regular force and primary reserves combined. Available mental health services for serving military personnel with suicidal behaviour exceed that for the Canadian civilian population. We identified many factors associated with suicidal behaviour, but acknowledged that it is extremely difficult to predict at an individual level. We agreed that the goal is to have no suicides in the CAF regular force population, but that not all suicides are preventable. We made 11 suggestions to improve suicide prevention in the CFHS. Discussion: The CFHS provides the highest quality mental health care for military personnel. Our recommendations are based on state-of-the-art research evidence, and their implementation will ensure that the CFHS leads the way in providing outstanding care for military personnel dealing with suicidal behaviour.
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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 | 0.051 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".