How long does it take patients to find a new primary care physician when theirs retires: a population-based, longitudinal study
Bibliographic record
Abstract
BACKGROUND: The retirement of a family physician can represent a challenge in accessibility and continuity of care for patients. In this population-based, longitudinal cohort study, we assess whether and how long it takes for patients to find a new majority source of primary care (MSOC) when theirs retires, and we investigate the effect of demographic and clinical characteristics on this process. METHODS: We used provincial health insurance records to identify the complete cohort of patients whose majority source of care left clinical practice in either 2007/2008 or 2008/2009 and then calculated the number of days between their last visit with their original MSOC and their first visit with their new one. We compared the clinical and sociodemographic characteristics of patients who did and did not find a new MSOC in the three years following their original physician's retirement using Chi-square and Fisher's exact test. We also used Cox proportional hazards models to determine the adjusted association between patient age, sex, socioeconomic status, location and morbidity level (measured using Johns Hopkins' Aggregated Diagnostic Groupings), and time to finding a new primary care physician. We produce survival curves stratified by patient age, sex, income and morbidity. RESULTS: Fifty-four percent of patients found a new MSOC within the first 12 months following their physician's retirement. Six percent of patients still had not found a new physician after 36 months. Patients who were older and had higher levels of morbidity were more likely to find a new MSOC and found one faster than younger, healthier patients. Patients located in more urban regional health authorities also took longer to find a new MSOC compared to those in rural areas. CONCLUSIONS: Primary care physician retirements represent a potential threat to accessibility; patients followed in this study took more than a year on average to find a new MSOC after their physician retired. Providing programmatic support to retiring physicians and their patients, as well as addressing shortages of longitudinal primary care more broadly could help to ensure smoother retirement transitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".