Foreword to the Second Volume of the Special Issue on 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 design 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. This 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: veteran microdosing and psychedelic self-medication; a historical view of the impact of education exchange between U.S. and South Korean military nurses; strategies for engaging veterans in research of a theater-based intervention for PTSD; interprofessional approaches to addressing veteran identity considerations through collaborations between chaplain service and psychologists in the VA Healthcare System; an international perspective exploring a community collaborative with veterans in Montréal, Canada; efforts to build long-term and sustainable models for veteran engagement in health services research; community-engaged strategies to address veteran homelessness within broader housing stability efforts; and examining the role of veteran peer mentorship programs in alcohol recovery. These projects represent an emerging movement and offer a multidisciplinary roadmap toward honoring veterans voices in research, clinical services, and program development.
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 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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".