Strengthening primary care through patient registration: a review of 10 countries
Bibliographic record
Abstract
Abstract Background Effective primary care requires continuity in the patient-provider relationship so that the primary care provider can act as the central coordinator of services. For this reason, some high-income countries have insisted on patient registration with a primary care team as part of different reform efforts. This paper develops a framework for analysing the characteristics of patient registration across countries; applies this framework to a selection of high-income countries that have introduced registration; and identifies challenges related to registration and ongoing reform efforts. Methods Based on a literature review, 10 countries - Canada, Denmark, England, France, Germany, Israel, Norway, Sweden Switzerland, and the Netherlands - were selected for analysis. Information was collected using a standardized questionnaire completed by national researchers who reviewed relevant literature and policy documents to report on the establishment and evolution of the policy, the requirements for providers and patients, the benefits for patients, providers and payers, and its connection to primary care reform. Results Patient registration establishes a triangular accountability relationship between patients, providers and payers that many reform advocates claim is the key to achieving better continuity and coordination of care. Results will provide information about the introduction of patient registration in the included countries; the characteristics of patient registration agreements; quantitative indicators, such as the proportion of patients registered, the proportion of primary care providers registering patients, and the average list size of providers. Recent reform experiences and ongoing challenges will also be reviewed. Conclusions This study will allow us to assess patient registration in terms of its key characteristics and outcomes. A preliminary evaluation of the policy's strengths and weaknesses based on key reform criteria is also presented.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.045 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".