Attaching Patients In Primary Care Through Centralized Waiting Lists: Seven Canadian Provinces Compared
Bibliographic record
Abstract
Canada has the lowest rate of attachment to primary care providers among OECD countries, which makes access and continuity of care problematic.To address this important issue, seven Canadian provinces have implemented centralized waiting lists (CWLs) for unattached patients in primary care.Introduced at different times, no two provinces' CWLs are exactly alike.The main goal of these CWLs is to reduce the number of unattached patients.In some provinces, CWLs also serve to monitor primary care activity or prioritize vulnerable patients.Societal pressure and broader primary care reform influenced the implementation of the CWLs in each province.Monitoring, in terms of data collected and purpose, differs between provinces.The interprovincial comparison enables identification of strengths, weaknesses, opportunities and threats during implementation and at each step of the CWLs: registration, patient assessment and attachment.Common issues with CWLs across provinces include the importance of monitoring to facilitate implementation, the need for specific measures to ensure access for vulnerable and complex patients, and the shortage of primary care providers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".