Involving citizens in disinvestment decisions: what do health professionals think? Findings from a multi-method study in the English NHS
Bibliographic record
Abstract
Public involvement in disinvestment decision making in health care is widely advocated, and in some cases legally mandated. However, attempts to involve the public in other areas of health policy have been accused of tokenism and manipulation. This paper presents research into the views of local health care leaders in the English National Health Service (NHS) with regards to the involvement of citizens and local communities in disinvestment decision making. The research includes a Q study and follow-up interviews with a sample of health care clinicians and managers in senior roles in the English NHS. It finds that whilst initial responses suggest high levels of support for public involvement, further probing of attitudes and experiences shows higher levels of ambivalence and risk aversion and a far more cautious overall stance. This study has implications for the future of disinvestment activities and public involvement in health care systems faced with increased resource constraint. Recommendations are made for future research and practice.
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How this classification was reachedexpand
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 | 0.039 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".