Patterns of physician retirement and pre-retirement activity: a populationbased cohort study
Bibliographic record
Abstract
BACKGROUND: Knowing when physicians retire and how they practise in the pre-retirement years is important information for health human resource planning. We identified patterns of retirement for physicians in British Columbia and the determinants of when and how physicians retire. METHODS: For this population-based retrospective cohort study, we used administrative data to examine activity levels and to identify retirements among BC's practising physicians. We included all physicians who were at least 50 years of age as of March 2006 and who had received payments for clinical services in at least 1 year between 2005/06 and 2011/12. We defined retirement as a permanent drop in monthly payments to less than $1667/month ($20 000/yr). We examined the patterns and timing of retirement by age, sex, specialty and location using linear and logistic regression models. RESULTS: Of the 4572 physicians who met the inclusion criteria, 1717 (37.6%) retired during the study period. The average age at retirement was 65.1 (standard deviation 7.8) years. Controlling for other demographic and practice characteristics, we found that women and physicians working in rural areas retired earlier, by 4.1 (95% confidence interval [CI] -4.9 to -3.2) years and 2.3 (95% CI -3.4 to -1.1) years, respectively. We found no difference in retirement age by specialty. We identified 4 patterns of pre-retirement activity: slow decline, rapid decline, maintenance and increasing activity. About 40% of physicians (440/1107) reduced their activity levels by at least 10% in the 3 years preceding retirement. INTERPRETATION: During the study period, physicians in BC - particularly women and those in rural areas - retired earlier than indicated by licensure and survey data. Many physicians reduced their practice activity in the pre-retirement years. These trends indicate that forecasts relying on licensure "head counts" are likely overestimating current and future physician supply.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".