Centralized Waiting Lists for Unattached Patients in Primary Care: Learning from an Intervention Implemented in Seven Canadian Provinces
Bibliographic record
Abstract
Introduction: Centralized waiting lists (CWLs) are one solution to reduce the problematic number of patients without a regular primary care provider. This article describes different models of CWLs for unattached patients implemented in seven Canadian provinces and identifies common issues in the implementation of these CWLs. Methods: Logic models of each province's intervention were built after a grey literature review, 42 semi-structured interviews and a validation process with key stakeholders were performed. Results: Our analysis across provinces showed variability and common features in the design of CWLs such as same main objective to attach patients to a primary care provider; implementation as a province-wide program with the exception of British Columbia; management at a regional level in most provinces; voluntary participation for providers except in two provinces where it was mandatory for providers to attach CWL patients; fairly similar registration process across the provinces; some forms of prioritization of patients either using simple criteria or assessing for vulnerability was performed in most provinces except New Brunswick. Conclusion: Despite their differences in design, CWLs implemented in seven Canadian provinces face common issues and challenges regarding provider capacity to address the demand for attachment, barriers to the attachment of more vulnerable and complex patients as well as non-standardized approaches to evaluating their effectiveness. Sharing experiences across provinces as CWLs were being implemented would have fostered learning and could have helped avoid facing similar challenges.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".