Assessing primary care organization and performance: Literature synthesis and proposition of a consolidated framework
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Many frameworks describing primary care (PC) organization exist. This study proposes a consolidated framework based on the synthesis of published frameworks for the assessment of primary care organization and performance. APPROACH: We conducted a review of the literature to identify relevant existing frameworks that aimed to describe PC organization or/and monitor its activities. First, we extracted all domains from the frameworks and then hierarchically organized them into domains, dimensions and elements. Second, we mapped key domains. Third, we grouped together domains covering the same field to build a consolidated framework. Finally, the consolidated framework was assessed by 10 international experts in PC evaluation using a survey. RESULTS: We retained seven frameworks. The consolidated framework comprises four domains: 1) population needs; 2) organization and structure of PC practices; 3) delivery of PC services and 4) patient and population health outcomes. We added five connecting constructs to the framework in order to link the domains: accessibility, appropriateness, productivity, efficiency, effectiveness, equity and integration. None of the previously published frameworks encompassed all domains, dimensions and elements of the new consolidated framework. CONCLUSION: We propose a consolidated framework of PC organization based on the synthesis of seven published frameworks. This unitary framework may provide a foundation for comparative assessment across various contexts to support researchers and policy makers.
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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 | 0.165 | 0.189 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.050 | 0.038 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 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".