Veterans Health and Well-Being—Collaborative Research Approaches: Toward Veteran Community Engagement
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
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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