Attaching Patients in Primary Care Through Centralized Waiting Lists
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. Le taux d'inscription à un professionnel de la santé en première ligne au Canada est le plus bas parmi les pays de l'OCDE, ce qui soulève un important problème d'accessibilité et de continuité aux soins de première ligne. Pour répondre à cette préoccupation, sept provinces canadiennes ont mis en place des listes d'attente centralisées (LAC) pour les patients non-affiliés à un professionnel de la santé en première ligne. Les LAC ont été implantées à différents moments, et diffèrent beaucoup d'une province à l'autre. Le principal objectif des LAC est de diminuer le nombre de patients non-affiliés, mais dans certaines provinces elles peuvent également servir à surveiller les activités de la première ligne ou à prioriser les patients vulnérables. La pression sociale et d'importantes réformes des soins de première ligne ont influencé l'implantation des LAC. Le monitorage, en termes de données collectées et d'utilisation, diffère d'une province à l'autre. La comparaison interprovinciale permet l'identification des forces, faiblesses, opportunités et menaces à l'implantation et à chaque étape de la LAC : l'enregistrement, l'évaluation du patient et l'affiliation. L'importance de la surveillance afin de faciliter l'implantation, le besoin d'interventions spécifiques pour garantir l'accès pour les patients vulnérables et complexes et le manque de prestataire de soins de première ligne sont quelques exemples des problématiques des LAC communes à toutes les provinces.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".