Veterans Health and Well-Being—Collaborative Research Approaches: Toward Veteran Community Engagement
Bibliographic record
Abstract
Veteran community engagement is an evolving discipline informed by traditional community-based participatory research, veteran studies, and veterans themselves. This Special Issue suggests that research collaborations including military veterans, soldiers, and their families as co-researchers is a critical next step toward a designing thinking perspective in social and healthcare systems for this population. This Special Issue was conceptualized through a veteran community-academic partnership formed over a decade ago. We briefly describe the activities of this partnership from 2008 to present in order to frame the praxis considerations within this issue. The partnership hosted several Warrior Summit conferences from 2013 to present, with the last of this series calling for academic contributions. The resulting papers drawn from the conference and other authors form this issue, and include a wide range of topics: Arts- and theater-based interventions for PTSD; engaging veteran college students in higher education; combining strengths of the chaplaincy and psychology to address changes in veteran identity after moral injury; multi-sector community coalitions for veteran reintegration in the US and Canada; veteran volunteering as a reintegration strategy; examining experiences of US military nurses; veteran collaboratively designed mindfulness groups in a VA healthcare system; engaging veterans on Community Advisory Boards; using photovoice to highlight veterans issues; collaborative research on veteran homelessness; veteran self-medication with psychedelics; community engaged addictions research; and collaboratively designing veteran peer support curricula. These projects represent an emerging movement and offer a multidisciplinary roadmap toward assisting and honoring veterans in their transition back into the civilian world.
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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 | 0.127 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.025 | 0.031 |
| Scholarly communication | 0.036 | 0.027 |
| Open science | 0.006 | 0.048 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".