Accountability for the Psychological Costs of Military Service
Bibliographic record
Abstract
The extent to which the military has led developments in mental health is often a forgotten story.1 A current example is the series of papers2–6 in this edition that analyze the 2002 and 2013 studies of the Canadian Armed Forces (CAF). Here, epidemiology is used to provide answers to complex questions about the manifestations of psychiatric disorders and their changed patterns over time. These findings need to be considered in the context of a responsive series of service delivery innovations made by the CAF that would seldom occur in the civilian mental health systems. Such research is extremely difficult to do in civilian settings because of the fragmented management and funding streams and the lack of accountability that public scrutiny demands of the military.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.023 | 0.035 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".