Co-creation of patient engagement quality guidance for medicines development: an international multistakeholder initiative
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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