Recruiting for Engagement in Health Policy
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
Background: Who participates in public and patient engagement processes, and in what capacity they participate, matters. The strategies employed to recruit participants shape the outcomes and legitimacy of engagement processes. We explore these issues through a case study of workshop recruitment. Methods: We conducted a mixed-methods study drawing on literature about existing theories of engagement, and integrated findings from the research team's own public engagement workshop in September 2022. We sought to align theoretical frameworks with practical approaches to recruiting for engagement. Results: There are inherent trade-offs in recruitment methods. While the theory of recruitment is valuable, practical implementation is complex and highly context-dependent. Engaging existing partners and fostering relationships beyond specific events is crucial. Hybrid workshops and low-barrier honoraria promote participation; however, decisions about location and time create barriers. Finally, balancing trusting relationships with critical perspectives can create tension. Discussion: Recruitment is foundational for the engagement process, and requires flexibility, responsiveness and a realistic understanding of barriers. Our study suggests that there is no universal formula for ideal participant makeup or event format. Meaningful engagement requires ongoing dialogue and constant adjustment based on practice. Policy makers can use these insights to align recruitment and engagement strategies with their goals in order to move beyond quick, technocratic fixes.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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