Prioritizing areas for quality marker development in children in UK general practice: extending the use of the nominal group technique
Bibliographic record
Abstract
BACKGROUND: There is a deficiency in the ability to measure the quality of care of children in primary care and there is no professional consensus in UK general practice regarding which quality markers should be used. OBJECTIVES: To prioritize clinical areas on which to focus quality marker development in paediatric primary care and to describe the challenges in generating professional consensus. METHODS: We convened an expert panel of GPs with a special interest in child health and using the nominal group technique (NGT), a well-established structured, multistep facilitated group meeting technique, we generated consensus around the key clinical areas to focus quality marker development. RESULTS: Twelve GPs participated in the expert panel. The eight items agreed by panellists as most important were early recognition of serious illness, whole practice involvement in safeguarding, health promotion, mental health, evidence-based management of common conditions, child and carer friendliness and safe and cost-effective prescribing. Panel members struggled to balance the broad clinical areas while attempting to focus on specific areas that are important. The main challenges included managing panel uncertainty, effective organization, presentation of items for review and group inclination to 'include everything'. CONCLUSIONS: This is the first consensus study of UK GPs to identify key areas to target quality marker development in children. By using the NGT, we have highlighted front-line health care professionals' priorities to improve the quality of care of children and identified the benefits and challenges of developing consensus in a broad topic area.
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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 | 0.277 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.012 |
| 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".