Developing an Institute of Medicine–Aligned Framework for Categorizing Primary Care Indicators for Quality Assessment
Bibliographic record
Abstract
The Institute of Medicine (IOM) framework has been used frequently to assess and monitor quality in secondary and tertiary care, but not in primary care. This article describes and proposes a conceptual framework for categorizing primary care indicators that align with the IOM's six aims for quality in healthcare performance (Safe, Effective, Patient-Centred, Timely, Efficient and Equitable.) Using an iterative process, the authors developed and compared a primary care framework for categorizing indicators in the Quality in Family Practice Book of Tools (QBT) with the IOM aims and other local healthcare systems frameworks (Integrated and Continuous, Appropriate Practice Resources). They also compared, cross-matched and analyzed their QBT categories and indicators with other international primary care assessment tools. And they compared the QBT titles and descriptions of groups of indicators with those published in the international tools.
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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 | 0.098 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".