Competition and compromise in negotiating the new governance of medical performance: the clinical governance and revalidation policies in the UK
Bibliographic record
Abstract
This article explores the development of two policies for the governance of medical performance in the UK: the Department of Health's (DH) clinical governance policy and the medical profession's revalidation policy. After discussing the institutional context in which each of these policies emerged, we examine how and why they were constructed. While the clinical governance policy was in large part a swift reaction to high-profile cases of medical misconduct in the late 1990s, revalidation was the profession's response to the politicisation of its self-regulatory apparatus. The profession took notably longer than the DH to piece together its policy as a result of internal disagreements about the role clinical standards should play in the evaluation of a doctor's fitness to practice. Following the Fifth Report of the Shipman Inquiry in late 2004, the government stepped in and eventually introduced legislation that modifies the profession's policy. With clinical governance, the state - via arms-length regulatory organisations - has entered the clinic in new ways, strengthening hierarchy-based forms of governance in the governance of medical performance. However, the success of hierarchical forms of governance is likely to be restricted by the lack of a clear system of sanctioning and the state's reliance on a lengthy chain of command in the National Health Service for the implementation of clinical standards.
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 | 0.089 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.037 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".