More Than Just Counting Deaths: The Evolution of Suicide Surveillance in the Canadian Armed Forces
Bibliographic record
Abstract
Suicide prevention and surveillance are of primary concern to the Canadian Armed Forces (CAF) and to the CAF Health Services (CFHS). Suicide surveillance has been conducted on behalf of the CFHS by the Directorate of Force Health Protection for nearly 30 years. Over time, multiple changes have occurred within CAF: changes in its military role (from a primarily peacekeeping role to one also involving active combat), changes in operational tempo, temporal changes in at-risk subpopulations, as well as increased awareness and concern with suicide and suicide prevention. This has resulted in the annual reporting of CAF suicide rates and the evolution of the report's content to respond to the needs of its end users. More recently, Regular Force Army and Combat Arms males have been identified as being at significantly higher risk of suicide, relative to their counterparts, as well as to the Canadian general population. However, this trend has been fairly stable. To optimize the use of limited epidemiologic resources and to shift the focus from the rates themselves towards a better understanding of what they represent and how they can be modified, the suicide surveillance portfolio is evolving to include complementary data sources and elements. This paper describes the different data sources that constitute the CAF's enhanced suicide surveillance portfolio, the value-added evidence generated by the use of complementary data collection methods and sources, and how this evidence is used by CAF leadership in their efforts to prevent suicide amongst those who serve.
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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 | 0.002 | 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) | 0.000 | 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".