Monitoring and Evaluation of Patient Engagement in Health Product Research and Development: Co-Creating a Framework for Community Advisory Boards
Bibliographic record
Abstract
PURPOSE: While patient engagement is becoming more customary in developing health products, its monitoring and evaluation to understand processes and enhance impact are challenging. This article describes a patient engagement monitoring and evaluation (PEME) framework, co-created and tailored to the context of community advisory boards (CABs) for rare diseases in Europe. It can be used to stimulate learning and evaluate impacts of engagement activities. METHODS: A participatory approach was used in which data collection and analysis were iterative. The process was based on the principles of interactive learning and action and guided by the PEME framework. Data were collected via document analysis, reflection sessions, a questionnaire, and a workshop. RESULTS: The tailored framework consists of a theory of change model with metrics explaining how CABs can reach their objectives. Of 61 identified metrics, 17 metrics for monitoring the patient engagement process and short-term outcomes were selected, and a "menu" for evaluating long-term impacts was created. Example metrics include "Industry representatives' understanding of patients' unmet needs;" "Feeling of trust between stakeholders;" and "Feeling of preparedness." "Alignment of research programs with patients' needs" was the highest-ranked metric for long-term impact. CONCLUSIONS: Findings suggest that process and short-term outcome metrics could be standardized across CABs, whereas long-term impact metrics may need to be tailored to the collaboration from a proposed menu. Accordingly, we recommend that others adapt and refine the PEME framework as appropriate. The next steps include implementing and testing the evaluation framework to stimulate learning and share impacts.
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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 | 0.479 | 0.417 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.007 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".