Family Physicians Attaching New Patients From Centralized Waiting Lists: A Cross-Sectional Study
Bibliographic record
Abstract
PURPOSE: In response to more than 15% of Canadians not having a family physician, 7 provinces have implemented centralized waiting lists for unattached patients. The aim of this study is to analyze the association between family physicians' characteristics and their participation in centralized waiting lists. METHODS: Cross-sectional observational study using administrative data in 5 local health networks in Quebec, between 2013 and 2015. All physicians who had attached at least 1 patient were included (n = 580). Multivariate linear regressions for the number of patients and proportion of vulnerable patients attached per physician were performed. RESULTS: Physicians with more than 20 years of experience represented more than half of those who had participated in the centralized waiting lists and physicians in traditional primary care models represented more than 40%. Physicians' number of years of practice, primary care model, local health network, and the number of physicians participating in the centralized waiting lists per clinic influenced physicians' participation. Physicians with 0 to 4 years of experience and those practicing in network clinics were found to attach more patients. Practicing in a Centre Locaux de Services Communautaires (local community service center) was associated with attaching 19% more vulnerable patients compared with practicing in a Family Medicine Unit (teaching unit). CONCLUSION: Centralized waiting lists seem to be used by early career physicians to build up their patient panels. However, because of the large number of them participating in the centralized waiting lists, physicians with more experience and those practicing in traditional models of primary care might be of interest for future measures to decrease the number of patients waiting for attachment in centralized waiting lists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".