Association of practice size and pay-for-performance incentives with the quality of diabetes management in primary care
Bibliographic record
Abstract
BACKGROUND: Not enough is known about the association between practice size and clinical outcomes in primary care. We examined this association between 1997 and 2005, in addition to the impact of the Quality and Outcomes Framework, a pay-for-performance incentive scheme introduced in the United Kingdom in 2004, on diabetes management. METHODS: We conducted a retrospective open-cohort study using data from the General Practice Research Database. We enrolled 422 general practices providing care for 154,945 patients with diabetes. Our primary outcome measures were the achievement of national treatment targets for blood pressure, glycated hemoglobin (HbA(1c)) levels and total cholesterol. RESULTS: We saw improvements in the recording of process of care measures, prescribing and achieving intermediate outcomes in all practice sizes during the study period. We saw improvement in reaching national targets after the introduction of the Quality and Outcomes Framework. These improvements significantly exceeded the underlying trends in all practice sizes for achieving targets for cholesterol level and blood pressure, but not for HbA(1c) level. In 1997 and 2005, there were no significant differences between the smallest and largest practices in achieving targets for blood pressure (1997 odds ratio [OR] 0.98, 95% confidence interval [CI] 0.82 to 1.16; 2005 OR 0.92, 95% CI 0.80 to 1.06 in 2005), cholesterol level (1997 OR 0.94, 95% CI 0.76 to 1.16; 2005 OR 1.1, 95% CI 0.97 to 1.40) and glycated hemoglobin level (1997 OR 0.79, 95% CI 0.55 to 1.14; 2005 OR 1.05, 95% CI 0.93 to 1.19). INTERPRETATION: We found no evidence that size of practice is associated with the quality of diabetes management in primary care. Pay-for-performance programs appear to benefit both large and small practices to a similar extent.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".