Correlates of perceived military to civilian transition challenges among Canadian Armed Forces Veterans
Bibliographic record
Abstract
Introduction: Analyses of the Canadian Armed Forces Transition and Well-Being Survey (CAFTWS) were conducted to identify the most prominent challenges faced by Canadian Armed Forces (CAF) Veterans during their military to civilian transition, and to assess the associations of various characteristics, including release type and health status, with experiencing such challenges. Methods: Prevalence estimates and logistic regression analyses were computed on data from the CAFTWS, which was administered in 2017 to 1,414 Regular Force Veterans released from the CAF in the previous year. Results: The two (of seven) perceived transition challenges with the strongest associations with difficult post-military adjustment were loss of military identity (adjusted odds ratio [AOR] = 5.4) and financial preparedness (AOR = 2.3). In adjusted regression analyses, Veterans who had a non-commissioned rank, primarily served in the army, 10–19 years of service, a medical release, and poor physical or mental health, were more likely to report loss of military identity. Veterans who had a junior non-commissioned rank, a medical release, and poor physical or mental health were more likely to report challenges with financial preparedness. Furthermore, significant interaction effects between Veterans’ release type and their health status were observed. Discussion: This study extends prior research to inform ongoing efforts to support the well-being of CAF members adjusting to post-service life. Findings emphasize the importance of preparing transitioning service members and civilian communities for the social identity challenges they may encounter. Findings also support the value of programs and services that help prepare transitioning service members with managing finances, finding education and employment, relocating, finding health care providers, and understanding benefits and services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".