Tools to Regularly Measure Function for Adult Patients in Primary Care
Bibliographic record
Abstract
Context: Canada is investing in initiatives to improve primary care. To measure their impact, performance measurement systems require a comprehensive set of health indicators. To date, most primary care health indicators measure process of care, disease, and health service utilization, with a gap in measures of health outcomes. Function is a measure of patient health that could measure outcomes. Regularly measuring function in primary care has had limited success. For primary care teams to implement and use measures of function, they need to be appropriate (i.e. timely, meaningful and credible) and to be feasible in primary care. Objectives: To identify the most appropriate and feasible measures of function, for adult patients, that can be used as health indicator(s) in primary care. Design: Classic Delphi Setting: Primary care in Canada Population Studied: Expert panel: 12 Canadian academic leaders, with expertise/experience in team-based care, primary care, patient function, and/or performance measurement. Intervention: Rounds 1-3 identified potential measures of function and sought consensus on a finite set of (4-5) measures. Round 4 measured levels of agreement on the appropriateness and feasibility of using 5 patient-reported health measures (SF-36, SF-12, EQ-5D-5L, WHODAS 2.0, and WHOQOL BREF) to measure function in primary care. Outcome Measures: Round 1-3: Percent of respondents that would keep, modify, or remove a proposed measure with consensus set at 75%. Round 4: The percent of respondents that rated, on a 5-point Likert scale, the appropriateness and utility of the 5 measures, and the percent of respondents who ranked the measures from 1 (best) to 5 (worst). Results: Round 1-3: 41 potential measures were identified representing the 3 ICF domains. Consensus was reached to remove 13 measures with no consensus achieved for the remaining 28. Round 4: Measures rated the highest for appropriateness were the SF-12 (80%) and the SF-36 (70%). Measures rated the highest for feasibility were the SF-12 (100%) and the EQ-5D-5L (90%). Measures with the highest overall rankings were the SF-12 (90%) and the EQ-5D-5L (60%). Conclusions: Measuring function is complex with all domains of function deemed important to measure. All 5 patient-reported health measures were deemed at least slightly appropriate and feasible. The SF- 12 was shown to be the most appropriate and feasible measure of function that could be used as a health indicator in primary care.
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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 | 0.059 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".