Serving Canadian Armed Force (CAF) members, veterans, and their families with experiences of trauma
Bibliographic record
Abstract
Canadian Armed Forces (CAF) members make the courage decision to protect Canada at \nall costs. Their decision entails risking the detriment of their physical and mental well-being. \nCAF members and veterans often face unique and traumatic experiences. The current CAF \nmodernization strategy has prioritized mental health care, implementing more holistic style \napproaches as it recognizes the growing need for continuous improvement to both the services \ndelivered to users and their quality of life. The current medical model of care has been critiqued \nfor perpetuating oppression and stigmatization of its users and does not allow for the full \nconsideration of socio-economic factors. The following Advanced Practicum report explores the \neffects of various external factors using principles of trauma-informed care and a Mad Studies in \nresponse to stressors such as childhood experience or gender on current experiences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".