Purpose after service through sport: A social identity-informed program to support military veteran well-being.
Bibliographic record
Abstract
Veterans who transition out of the military often face substantive challenges during their move to civilian life, which include identifying appropriate opportunities for employment, supporting their respective families, and developing high-quality social connections within their civilian lives more generally. The importance of social connectivity, in particular, has recently been highlighted as an important mechanism that can facilitate improved mental health and quality of life among veterans, and represents a viable target for intervention. The purpose of the study was to examine military veterans’ experiences of Purpose After Service through Sport (PASS) which is a sport-based program, in Canada, underpinned by the social identity approach. We recruited participants (Mage= 39.83, SD = 8.07, Myears of service= 15.63, SD = 9.60), and using semi-structured interviews and reflexive thematic analysis, identified several aspects of the program that participants experienced and considered important. These included a variety of positive benefits (mental and physical health, social connections, and access to resources), as well as military identity as a means of supporting social connectivity. Participants also commented on salient environmental features of the program that supported their involvement, as well as suggestions for program refinement. The study provides evidence for the feasibility and acceptability of the PASS program as well as insight into veterans’ experiences of this initiative. Future research should examine the efficacy/effectiveness of the PASS program to support effective transitions and quality of life outcomes among military veterans using causal (e.g., randomized trial) research designs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".