Characteristics of patient enrolment policies in primary care: a qualitative analysis of 15 schemes from 12 high-income countries
Bibliographic record
Abstract
A previously published review on primary care enrolment schemes found substantial variations in their characteristics, each with the potential to affect their ability to ensure people have access to primary care and continuity in their care. For this Review, we developed a typology informed by qualitative analysis of 15 schemes in 12 high-income countries for effective comparisons and informed policy making. We obtained policy documents from the websites of relevant government organisations and used inductive coding and thematic analysis to classify and group the schemes according to their core characteristics. Two distinctive features emerged: whether patients could attend other practices (ie, restricted or unrestricted) and whether there were different types of financial or non-financial incentives to enrol or attend enrolled practices, resulting in eight clusters of enrolment types. The identified typologies can assist researchers and policy makers in comparing and interpreting outcomes reported in the literature on patient enrolment schemes, including their impact on continuity of care. This study is particularly relevant to primary care reforms considering patient enrolment.
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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 | 0.042 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".