Advanced Nurse Practitioner (ANPs) experiences of the Quality and Outcomes Framework (QOF) Scheme: a UK case study
Bibliographic record
Abstract
OBJECTIVES: The primary focus of pay-for-performance (P4P) schemes in the UK has traditionally been related to the public health and inclusion elements related to the activities of doctors with comparatively less attention given to nursing care as a component of the scheme. However, nursing is an integral part of healthcare delivery in the National Health Service and nurses constitute the major group of healthcare professionals in most countries. Our aim was to explore advanced nurse practitioner (ANPs) experiences of the Quality and Outcomes Framework (QOF), using the Implicit Leadership Theory (ILT) frame. METHODS: We used a case study approach. Six articles on the QOF work were synthesised, focused on ANPs and their leadership potential in healthcare. Evidence showed that despite having importance in delivering the activities of QOF, nursing activities overlooked. We undertook a thematic synthesis of these papers, with a specific focus ANPs' leadership development in Long Term Conditions (LTC) care within general practice and capacity to influence the healthcare system. FINDINGS: Six themes were identified: (1) sensitivity, patient-centred care, context and continuity of care; (2) intelligence-leaders capable of making strategic decisions in healthcare settings, (3) dedication, trust, equity and equality, (4) dynamism of nursing, (5) tyranny, guise of teamwork, collaboration and (6) nursing and healthcare leadership. CONCLUSIONS: Nurses in leadership roles created good working relationships, coped with conflicts and contributed to shared objectives and were sympathetic collaborators. Using the six ILT characteristics, we found that nurses were collaborators. Future P4P schemes should benefit from a collective lens of healthcare personnel when focusing on quality initiatives and improving the delivery of healthcare activities.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".