Identifying Key Correlates of Social Well-Being among Canadian Armed Forces Veterans: An Analysis of the 2016 Life after Service Study
Bibliographic record
Abstract
Many Veterans experience disruptions to their social connections during military to civilian transition. As low social support has been associated with difficulties adjusting to civilian life, there is value in better understanding social support in Veteran populations. The purpose of this study was to identify correlates of social support among recent Canadian Armed Forces (CAF) Veterans. Data were collected as part of the 2016 Life After Service Survey, which was administered to a sample of CAF Regular Force Veterans. This study focuses on more recently released Veterans, 5 years prior to the survey (n = 1,723) to better reflect the impact of transitioning to civilian life. Social support was measured using the 10-item Social Provision Scale. Regression models examined the relative associations of individual, release, and post-transition characteristics (i.e., family/household composition and main activity) with social support. Models explained up to 23% of the variance in social support. Lower social support was associated with being: male, older, of noncommissioned rank at release, and released involuntarily (p ≤ .05). Family/household composition, most notably living with a partner, was also associated with greater social support, especially among Veterans who were not in the workforce. Veterans’ main activity in the last year explained the most variance in social support, with a strong association noted for satisfaction with one’s main activity (p ≤ .001). Social support is an important and modifiable factor in the transition to civilian life. Results point to specific subgroups who may be at risk for low social support after military service.Supplemental data for this article is available online at https://doi.org/10.1080/21635781.2021.2007182 .
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".