Experiences and lessons learned from a <scp>patient‐engagement</scp> service established by a national research consortium in the U.S. Veterans Health Administration
Bibliographic record
Abstract
Introduction: Meaningful engagement of patients in the research process has increased over the past 20 years. Few accounts are available of engagement infrastructure and processes used by large research organizations. The Pain/Opioid Consortium of Research (Consortium) is a U.S. Department of Veterans Affairs (VA) research network that provides infrastructure to accelerate health research and implementation of evidence-based health care. The Consortium's key activities include facilitating Veteran-engaged research and building community between Veterans and VA researchers. This report sought to describe experiences and lessons learned from the first 3 years of a national research engagement service, featuring a Veteran Engagement (VE) Panel, established by the Consortium. Methods: We gathered authors' experiences to describe development and operation of the Consortium's VE Panel. Engagement staff collected program evaluation data about partners (Veterans and researchers), projects about which the VE Panel consulted, and meeting attendance during operation of the engagement service. Results: We created a 12-member VE Panel; all of whom had lived experience with chronic pain, prescription opioid medication use, or opioid use disorder. Engagement staff and VE Panel members implemented an engagement service operational model designed to continuously learn and adapt. The panel consulted on 48 projects spanning the research process. Seventy-eight percent of panel members, on average, attended each monthly meeting. VE Panel members and participating researchers reported high satisfaction with the quality, ease, and outcomes of their engagement service experiences. Conclusions: This work provides an illustrative example of how a national research consortium facilitated Veteran-engaged research and built community between Veterans and VA researchers by developing and operating an ongoing engagement consulting service, featuring a VE Panel. The service, designed as a learning community, relied on skilled engagement staff to cultivate high quality experiences and outcomes for all partners.
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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 | 0.027 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.011 |
| 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".