Labour market outcomes of Veterans
Bibliographic record
Abstract
Introduction: Employment is important to health, well-being, and adjustment from military to civilian life. Given the importance of employment, we examine Veteran labour force outcomes in Canada. Methods: We examined labour market indicators from the 2010 and 2013 Life After Service Studies cross-sectional Survey on Transition to Civilian Life, along with the 2013 Income Study for Canadian Regular Force Veterans (released since 1998). Results: In Canada, most Regular Force Veterans surveyed were employed after release and satisfied with their work – both employment and satisfaction rates grew over time. The unemployment rate did not differ from that of the general Canadian population. However, Veterans were more likely than the general Canadian population to experience activity limitations at work. Variations in outcomes were found across diverse groups of the population. For example, unemployed Veterans were younger at release, had the fewest years of service, and were more likely to have served in the Army than employed Veterans. Veterans who were not in the labour force were older and had more years of service, and many were experiencing barriers to work. Employment rates were lower among female Veterans and among medically released Veterans. Discussion: Labour market outcomes vary across sub-groups of the Veteran population, suggesting targeted approaches to improve labour market outcomes. Findings suggest that the prevention of work disability is important for improving outcomes. Best practices in preventing work disability include restructuring compensation to recognize varying degrees of earnings capacity and to encourage labour market engagement and supported employment programs.
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 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) | 0.001 | 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".