Transforming care, engaging communities: conversation with the public on service change across UK
Bibliographic record
Abstract
Background The goal of Health for All requires health services to change and develop in line with emerging evidence of models of healthcare that prove effective and equitable. In practice, major changes to health services, such as the closure of hospitals, can be deeply unpopular and challenging for health systems to achieve. In 1999, political devolution in the UK created the potential for significant policy divergence between England, Scotland, Wales and Northern Ireland. Despite evidence that this ‘natural experiment’ has seen meaningful changes in policy approach, surprisingly little research has compared policy in the four countries. We compare and contrast the countries’ approaches to achieving meaningful public involvement within difficult and sometimes unpopular decisions on major service change. Methods This comparative qualitative study – consisting of a desk-based review of policy documents from the four health systems, plus qualitative interviews with key policy actors and stakeholders in all four countries – explores perceptions of how policy can best support health services to involve the public and patients in service change. Results We demonstrate that, despite some commonalities of process, the increasingly divergent health systems in each of the four nations take distinctive approaches to involving the public, particularly when it comes to who is permitted to speak for citizens within the decision-making process. Conclusions We present key lessons learned in each health system, and draw out more general recommendations for the enduring health policy dilemma of conducting constructive conversations on unpopular frontline service changes. Key messages: The devolved national health systems of England, Scotland, Wales and Northern Ireland engage the public on service changes differently The study enabled the identification of strength and weaknesses of each nation’s NHS engagement with the public
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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 | 0.060 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.058 | 0.076 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".