Work-related Experience and Financial Security of Veterans Affairs Canada Clients: Contrasting Medical and Nonmedical Discharge. Paper prepared under contract for the Research Directorate of Veterans Affairs
Bibliographic record
Abstract
Work-related Experience and Financial Security of Veterans Affairs Canada Clients: Contrasting Medical and Non-medical Discharge This paper is based on the VAC Canadian Forces Survey, a representative study of clients of Veterans Affairs Canada conducted in 1999, and compares those who report being discharged for medical reasons with those discharged for other reasons. We describe the characteristics associated with medical and non-medical discharge, and then examine post-discharge work experiences and perceived economic security. Those released for medical reasons are on average 6 years younger than those released for other reasons and had spent significantly fewer years in regular service (17.2 vs. 23.5 years) They are significantly less likely than those released for other reasons to be officers (16.4 vs. 31.4%). Upon release from the Canadian Forces, a significantly smaller proportion report having ever worked in civilian jobs (80.7 vs. 91.6%), at the time of the survey they had held more post-release jobs (2.86 vs. 2.73); and they were more likely to be either unemployed (9.3 vs. 7.6%) or inactive (39.0 vs. 37.7%) at time of survey. Those released for medical reasons are significantly less likely to report satisfaction with current
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".