Implementation and evolution of a citizen council to support patient-oriented pharmacy practice research in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The Ontario Pharmacy Evidence Network (OPEN) introduced the OPEN Citizens' Council (OCC) in 2019 as a forum for citizen engagement. The OCC provides OPEN researchers with a mechanism for collecting citizens' perspectives on research priorities, strategies, data interpretation, and methods. OBJECTIVE: This paper examines the development and learning from OCC meeting data to provide insight into how a citizen advisory forum can enhance health practice research. METHODS: OCC evaluation used mixed methods. The Public and Patient Engagement Evaluation Tool (PPEET) and participant feedback interviews were offered to both OCC members and researcher presenters following OCC meetings. Descriptive statistical analysis of quantitative data (PPEET survey) was conducted. Inductive thematic analysis was used to conceptualize themes from qualitative semi-structured interviews from both OCC members and researcher presenters. RESULTS: Ongoing collection and analysis of survey data guided changes to OCC delivery. These included adjusting training, modifying meeting duration and scheduling, expanding preparation and discussion by posting materials online prior to meetings. Qualitative analysis of interview data led to development of four major themes: 1) rationale for participation in OCC, 2) OCC meeting experience, 3) lessons learned about citizen engagement, and 4) research impact and ramifications of citizen engagement (CE). CONCLUSION: Regular evaluation enabled council development that provided pharmacy researchers with a way to explore societal views on strategic and implementation research stages. By tapping into broad, civic knowledge, citizen engagement panels can complement other research engagement activities that include patients who have specific, lived experiences. Greater recognition of engagement types, activities, and associated value, along with resources to support collaborative initiatives, will lead to a more responsive research landscape.
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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 | 0.100 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".