Development of practice-based quality indicators for the primary care of older adults: a RAND/UCLA Appropriateness Method study protocol
Bibliographic record
Abstract
INTRODUCTION: Older adults have high rates of primary care utilisation, and quality primary care has the potential to address their complex medical needs. Family physicians have different levels of knowledge and skills in caring for older patients, which may influence the quality of care delivery and resulting health outcomes. In this study, we aim to establish consensus on practice-based metrics that characterise quality of care for older primary care patients and can be examined using secondary, administrative data. METHODS AND ANALYSIS: We describe a two-round RAND/UCLA Appropriateness Method (RAM) study to assess the consensus of a technical expert panel. We will recruit pan-Canadian experts who demonstrate excellence in clinical practice or scholarship related to the primary care of older adults. A literature review will generate a candidate list of practice-based quality indicators. The first round aims to evaluate the appropriateness and importance of candidate indicators through an online questionnaire. We will then develop technical definitions for each endorsed indicator using ICES data holdings. Panellists will offer feedback on the technical definitions in a virtual synchronous meeting and provide ratings on the same criteria in a second questionnaire. ETHICS AND DISSEMINATION: Our study has been approved by the Hamilton Integrated Research Ethics Board (Project ID #15545). Findings will be disseminated via manuscripts, presentations and the lead author's thesis. TRIAL REGISTRATION NUMBER: ISRCTN17074347.
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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 | 0.189 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".