Recreation-Based Programming for Veteran Families: Practitioners’ Perspective
Bibliographic record
Abstract
Reintegration presents many challenges for transitioning veterans and their families. Veterans return to civil life needing mental health and family services support. Recreation-based programs have been introduced to veterans and their families to help address their needs during and after reintegration. Few studies examine the perspectives of practitioners in veteran-serving organizations focusing on recreation-based programs. Interviews were conducted with eight practitioners and analyzed using qualitative coding. According to practitioners, veterans and their families experienced multiple benefits and learned new skills through the programs, including communication skills, better emotional response, reestablishment of family roles, and problem-solving skills. They also learned about mental health and had an opportunity to decompress and have fun with their families and other veterans. The practitioners also reported that the veterans expressed a desire to include family members in programs, learn more about mental health issues, and share their experiences with fellow veteran families. There were also several barriers experienced by the program providers, including limited funding and staff, and the ability to find housing suitable for the families.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".