Qualitative Exploration of Engaging Patients as Advisors in a Program of Evidence Synthesis
Bibliographic record
Abstract
BACKGROUND: There is an increasing expectation for research to involve patient stakeholders. Yet little guidance exists regarding patient-engaged research in evidence synthesis. Embedded in a learning health care system, the Veteran Affairs Evidence Synthesis Program (ESP) provides an ideal environment for exploring patient-engaged research in a program of evidence synthesis. OBJECTIVE: The objective of this study was to explore views, barriers, resources, and perceived values of engaging patient advisors in a national program of evidence synthesis research. METHODS: We conducted 10 qualitative interviews with ESP researchers and 2 focus groups with patient stakeholder informants. We queried for challenges to patient involvement, resources needed to overcome barriers, and perceived values of patient engagement. We analyzed qualitative data using applied thematic and matrix techniques. RESULTS: Patient stakeholders and researchers expressed positive views on the potential role for patient engagement in the Veteran Affairs ESP. Possible contributions included topic prioritization, translating findings for lay audiences, and identifying clinically important outcomes relevant to patients. There were numerous barriers to patient involvement, which were more commonly noted by ESP researchers than by patient stakeholders. Although informants were able to articulate multiple values, we found a lack of clarity around measurable outcomes of patient involvement in systematic reviews. CONCLUSIONS: The research community increasingly seeks patient input. There are many perceived and actual barriers to seeking robust patient engagement in systematic reviews. This study outlines emerging practices that other evidence synthesis programs should consider, such as the careful selection of stakeholders; codeveloped expectations and goals; and adequate training and appropriate resources to ensure meaningful engagement.
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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 | 0.117 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".