Involving Stakeholders in Healthcare Decisions - The Experience of the National Institute for Health and Clinical Excellence (NICE) in England and Wales
Bibliographic record
Abstract
Appeal to "stakeholders" and involving them in decisions and the processes through which decisions are made are becoming touch stones of "best practice," both clinical and managerial, in health care. Few organizations have sought to integrate stakeholders, especially patients and their caregivers, more completely than the National Institute for Health and Clinical Excellence (NICE) in England and Wales. This article outlines the circumstances in which NICE was created (1999) and the means through which it has created truly effective involvement of its many stakeholder groups. Key messages are that client involvement in decision-making is possible and can work well, but it demands commitment from the entire organization, specific managerial arrangements and, depending on the circumstances, it can be costly. Trust is an important ingredient of success.
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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 | 0.061 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".