A comparative analysis of medically released men and women from the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: Musculoskeletal (MSK) injuries and mental health (MH) disorders are the leading causes of medical attrition in the Canadian Armed Forces (CAF). Historically, medical attrition rates have been higher for women than men. In order to better understand the medical release trends of men and women, a descriptive analysis of the medical reasons for release was undertaken. Methods: Administrative data sources within the Department of National Defence were used to identify medically released personnel together with their primary medical diagnosis and demographic characteristics, including sex, age, and rank. The analysis included 5,180 Regular Force personnel medically released between April 1, 2014 and March 31, 2017. Results: While overall trends in the reasons for medical release were sometimes similar for men and women, statistically significant differences between the medical release reasons of men and women were found in several of the sub-groups considered. These sub-groups included non-commissioned members (NCMs), officers, Air personnel, and members who had not deployed in the 10 years prior to their release, as well as personnel in the earlier and later stages of their career. Discussion: An increased understanding of the differences between medically released men and women is important for the development of future injury and illness prevention strategies, which have the primary objective of improving the health and operational readiness of serving members, as well as a secondary objective of lowering medical attrition rates to improve overall retention in the CAF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".