Co-creation of patient engagement quality guidance for medicines development: an international multistakeholder initiative
Bibliographic record
Abstract
INTRODUCTION: Meaningful patient engagement (PE) can enhance medicines' development. However, the current PE landscape is fragmentary and lacking comprehensive guidance. METHODS: We systematically searched for PE initiatives (SYNaPsE database/publications). Multistakeholder groups integrated these with their own PE expertise to co-create draft PE Quality Guidance which was evaluated by public consultation. Projects exemplifying good PE practice were identified and assessed against PE Quality Criteria to create a Book of Good Practices (BOGP). RESULTS: Seventy-six participants from 51 organisations participated in nine multistakeholder meetings (2016-2018). A shortlist of 20relevant PE initiatives (from 170 screened) were identified. The co-created INVOLVE guidelines provided the main framework for PE Quality Guidance and was enriched with the analysis of the PE initiatives and the PE expertise of stakeholders. Seven key PE Quality Criteria were identified. Public consultation yielded 67 responses from diverse backgrounds. The PE Quality Guidance was agreed to be useful for achieving quality PE in practice, understandable, easy to use, and comprehensive. Overall, eight initiatives from the shortlist and from meeting participants were selected for inclusion in the BOGP based on demonstration of PE Quality Criteria and willingness of initiative owners to collaborate. DISCUSSION: The PE Quality Guidance and BOGP are practical resources which will be continually updated in response to user feedback. They are not prescriptive, but rather based on core principles, which can be applied according to the unique needs of each interaction and initiative. Implementation of the guidance will facilitate improved and systematic PE across the medicines' development lifecycle.
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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 | 0.253 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".