Designing centralized waiting lists for attachment to a primary care provider: Considerations from a logic analysis
Bibliographic record
Abstract
Access to a regular primary care provider is essential to quality care. In Canada, where 15 % of patients are unattached (i.e., without a regular provider), centralized waiting lists (CWLs) help attach patients to a primary care provider (family physician or nurse practitioner). Previous studies reveal mechanisms needed for CWLs to work, but focus mostly on CWLs for specialized health care. We aim to better understand how to design CWLs for unattached patients in primary care. In this study, a logic analysis compares empirical evidence from a qualitative case study of CWLs for unattached patients in seven Canadian provinces to programme theory derived from a realist review on CWLs. Data is analyzed using context-intervention-mechanism-outcome configurations. Results identify mechanisms involved in three components of CWL design: patient registration, patient prioritization, and patient assignment to a provider for attachment. CWL programme theory is revised to integrate mechanisms specific to primary care, where patients, rather than referring providers, are responsible for registering on the CWL, where prioritization must consider a broad range of conditions and characteristics, and where long-term acceptability of attachment is important. The study provides new insight into mechanisms that enable CWLs for unattached patients to work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".