Correlates of perceived military to civilian transition challenges among Canadian Armed Forces Veterans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it