Progress of GP clusters 2 years after their introduction in Scotland: findings from the Scottish School of Primary Care national GP survey
Bibliographic record
Abstract
BACKGROUND: The concept of GP clusters is derived from 'quality circles' in general practice in Europe and Canada. GP clusters commenced across Scotland in 2016 to improve the quality of care of local populations. AIM: To determine GPs' views on clusters, and the robustness of bespoke questions about them. DESIGN & SETTING: A cross-sectional national survey of work satisfaction of GPs in Scotland took place, which was conducted in July 2018-October 2018. METHOD: An analysis of bespoke questions on GP clusters was undertaken. The questions were completed by quality leads (QLs) and all other GPs in a nationally representative sample of GPs. RESULTS: In total, 2456 responses were received from 4371 GPs (56.4%). QLs reported that clusters were meeting regularly, and were friendly and well organised but not always productive. Support for cluster activity (data, health intelligence, analysis, quality improvement methods, advice, leadership, and evaluation) was suboptimal. Factor analysis identified two separate constructs (cluster meetings [CMs] and cluster support [CS]), which were minimally influenced (<2%) by GP and practice characteristics. Non-QLs (75% of all GPs) were generally satisfied with the two-way communication with the cluster QLs, but the great majority (>70%) reported no positive changes in various aspects of quality improvement. Factor analysis of these items indicated two constructs (cluster knowledge and engagement [CKE] and cluster quality improvement [CQI]), which were minimally affected by GP and practice characteristics. CONCLUSION: GP clusters are 'up and running' in Scotland but are at an early stage in terms of perceived impact and appear to be in need of more support in order to improve quality of care. The bespoke questions developed on clusters have robust construct validity, suitable for future surveys.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".