Primary care quality indicators for children: measuring quality in UK general practice
Bibliographic record
Abstract
BACKGROUND: Child health care is an important part of the UK general practice workload; in 2009 children aged <15 years accounted for 10.9% of consultations. However, only 1.2% of the UK's Quality and Outcomes Framework pay-for-performance incentive points relate specifically to children. AIM: To improve the quality of care provided for children and adolescents by defining a set of quality indicators that reflect evidence-based national guidelines and are feasible to audit using routine computerised clinical records. DESIGN AND SETTING: Multi-step consensus methodology in UK general practice. METHOD: Four-step development process: selection of priority issues (applying nominal group methodology), systematic review of National Institute for Health and Care Excellence (NICE) and Scottish Intercollegiate Guidelines Network (SIGN) clinical guidelines, translation of guideline recommendations into quality indicators, and assessment of their validity and implementation feasibility (applying consensus methodology used in selecting QOF indicators). RESULTS: Of the 296 national guidelines published, 48 were potentially relevant to children in primary care, but only 123 of 1863 recommendations (6.6%) met selection criteria for translation into 56 potential quality indicators. A further 13 potential indicators were articulated after review of existing quality indicators and standards. Assessment of the validity and feasibility of implementation of these 69 candidate indicators by a clinical expert group identified 35 with median scores 8 on a 9-point Likert scale. However, only seven of the 35 achieved a GRADE rating >1 (were based on more than expert opinion). CONCLUSION: Producing valid primary care quality indicators for children is feasible but difficult. These indicators require piloting before wide adoption but have the potential to raise the standard of primary care for all children.
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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 | 0.056 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".