Patient Function as a Health Indicator in Primary Care: Perspectives from Patients and Primary Care Teams
Bibliographic record
Abstract
Context: Inter-professional team-based care remains a focus of primary care transformation in Canada. Quality improvement aimed at optimizing team-based primary care requires health indicators that can be consistently measured and provide meaningful information on patient outcomes. The International Classification of Functioning, Disability, and Health, provides a conceptual model to measure health in primary care. Little is known about patients’ and providers’ understanding of function and its utility as a health indicator for all adult patients in primary care. Objectives: From the perspectives of patients and primary care teams, explore the: 1) understanding of the concept of function, and 2) utility of measuring function as a health indicator for adult patients in primary care. Design: A qualitative description study; part of a larger mixed methods study examining how function can be used in the performance measurement of primary care teams. Setting: Primary Care Networks (PCNs) in the Interior Health Region of British Columbia, Canada. Population Studied: 6 patient participants with experience in primary care transformation and 18 providers across 7 disciplines from two PCNs. Intervention: 24 semi-structured interviews. Outcome Measures: Description of study participants. A rich description of identified themes identified through an inductive qualitative thematic analysis. Results: Relating to the understanding of concept of function, four themes were identified: 1) Function is a bigger picture of health and the ability to live your best life, 2) Patient’s beliefs about their function are key, 3) Function is rarely talked about in primary care, and 4) Patient function is personal and influenced by many things. Relating to the utility of measuring function as a health indicator in primary care, five themes were identified: 1) Optimizing function is the whole point of primary care, 2) Measuring function will lead to more patient-centred care, 3) Bringing people to the table – team-based care, 4) Function is hard to integrate into care, and 5) Function supports quality improvement. Conclusions: Overall, the results of this study suggest there is value for patients and primary care teams to measure function as a health indicator of primary care for adult patients, however, the measurement and reporting of function should reflect the complex and high-paced environment of 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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".