CUP study: protocol for a comparative analysis of centralised waitlist effectiveness, policies and innovations for connecting unattached patients to primary care providers
Bibliographic record
Abstract
INTRODUCTION: Access to a primary care provider is a key component of high-functioning healthcare systems. In Canada, 15% of patients do not have a regular primary care provider and are classified as 'unattached'. In an effort to link unattached patients with a provider, seven Canadian provinces implemented centralised waitlists (CWLs). The effectiveness of CWLs in attaching patients to regular primary care providers is unknown. Factors influencing CWLs effectiveness, particularly across jurisdictional contexts, have yet to be confirmed. METHODS AND ANALYSIS: A mixed methods case study will be conducted across three Canadian provinces: Ontario, Québec and Nova Scotia. Quantitatively, CWL data will be linked to administrative and provider billing data to assess the rates of patient attachment over time and delay of attachment, stratified by demographics and compared with select indicators of health service utilisation. Qualitative interviews will be conducted with policymakers, patients, and primary care providers to elicit narratives regarding the administration, use, and access of CWLs. An analysis of policy documents will be used to identify contextual factors affecting CWL effectiveness. Stakeholder dialogues will be facilitated to uncover causal pathways and identify strategies for improving patient attachment to primary care. ETHICS AND DISSEMINATION: Approval to conduct this study has been granted in Ontario (Queens University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board, file number 6028052; Western University Health Sciences Research Ethics Board, project 116591; University of Toronto Health Sciences Research Ethics Board, protocol number 40335), Québec (Centre intégré universitaire de santé et de services sociaux de l'Estrie, project number 2020-3446) and Nova Scotia (Nova Scotia Health Research Ethics Board, file number 1024979).
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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 | 0.119 | 0.108 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.172 | 0.027 |
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".