Key issues for stakeholder engagement in the development of health and healthcare guidelines
Bibliographic record
Abstract
Established in 2015, the Multi-Stakeholder Engagement (MuSE) Consortium is an international network of over 120 individuals interested in stakeholder engagement in research and guidelines. The MuSE group is developing guidance for stakeholder engagement in the development of health and healthcare guideline development. The development of this guidance has included multiple meetings with stakeholders, including patients, payers/purchasers of health services, peer review editors, policymakers, program managers, providers, principal investigators, product makers, the public, and purchasers of health services and has identified a number of key issues. These include: (1) Definitions, roles, and settings (2) Stakeholder identification and selection (3) Levels of engagement, (4) Evaluation of engagement, (5) Documentation and transparency, and (6) Conflict of interest management. In this paper, we discuss these issues and our plan to develop guidance to facilitate stakeholder engagement in all stages of the development of health and healthcare guideline development.
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.
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 | 0.643 | 0.656 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.054 |
| Scholarly communication | 0.040 | 0.050 |
| Open science | 0.010 | 0.037 |
| Research integrity | 0.036 | 0.053 |
| Insufficient payload (model declined to judge) | 0.009 | 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".