A pay for performance scheme in primary care: Meta-synthesis of qualitative studies on the provider experiences of the quality and outcomes framework in the UK
Bibliographic record
Abstract
BACKGROUND: The Quality and Outcomes Framework (QOF) is an incentive scheme for general practice, which was introduced across the UK in 2004. The Quality and Outcomes Framework is one of the biggest pay for performance (P4P) scheme in the world, worth £691 million in 2016/17. We now know that P4P is good at driving some kinds of improvement but not others. In some areas, it also generated moral controversy, which in turn created conflicts of interest for providers. We aimed to undertake a meta-synthesis of 18 qualitative studies of the QOF to identify themes on the impact of the QOF on individual practitioners and other staff. METHODS: We searched 5 electronic databases, Medline, Embase, Healthstar, CINAHL and Web of Science, for qualitative studies of the QOF from the providers' perspective in primary care, published in UK between 2004 and 2018. Data was analysed using the Schwartz Value Theory as a theoretical framework to analyse the published papers through the conceptual lens of Professionalism. A line of argument synthesis was undertaken to express the synthesis. RESULTS: We included 18 qualitative studies that where on the providers' perspective. Four themes were identified; 1) Loss of autonomy, control and ownership; 2) Incentivised conformity; 3) Continuity of care, holism and the caring role of practitioners' in primary care; and 4) Structural and organisational changes. Our synthesis found, the Values that were enhanced by the QOF were power, achievement, conformity, security, and tradition. The findings indicated that P4P schemes should aim to support Values such as benevolence, self-direction, stimulation, hedonism and universalism, which professionals ranked highly and have shown to have positive implications for Professionalism and efficiency of health systems. CONCLUSIONS: Understanding how practitioners experience the complexities of P4P is crucial to designing and delivering schemes to enhance and not compromise the values of professionals. Future P4P schemes should aim to permit professionals with competing high priority values to be part of P4P or other quality improvement initiatives and for them to take on an 'influencer role' rather than being 'responsive agents'. Through understanding the underlying Values and not just explicit concerns of professionals, may ensure higher levels of acceptance and enduring success for P4P schemes.
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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 | 0.133 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".