Primary care performance of alternatively licenced physicians in Ontario, Canada: a cross-sectional study using administrative data
Bibliographic record
Abstract
OBJECTIVES: Medical Regulatory Authorities (MRAs) provide licences to physicians and monitor those physicians once in practice to support their continued competence. In response to physician shortages, many Canadian MRAs developed alternative licensure routes to allow physicians who do not meet traditional licensure criteria to obtain licences to practice. Many physicians have gained licensure through alternative routes, but the performance of these physicians in practice has not been previously examined. This study compared the performance of traditionally and alternatively licenced physicians in Ontario using quality indicators of primary care. The purpose of this study was to examine the practice performance of alternatively licenced physicians and provide evaluative evidence for alternative licensure policies. DESIGN: A cross-sectional retrospective examination of Ontario health administrative data was conducted using Poisson regression analyses to compare the performance of traditionally and alternatively licenced physicians. SETTING: Primary care in Ontario, Canada. PARTICIPANTS: All family physicians who were licenced in Ontario between 2000 and 2012 and who had complete medical billing data in 2014 were included (n=11 419). OUTCOME MEASURES: Primary care quality indicators were calculated for chronic disease management, preventive paediatric care, cancer screening and hospital readmission rates using Ontario health administrative data. RESULTS: Alternatively licenced physicians performed similarly to traditionally licenced physicians in many primary care performance measures. Minimal differences were seen across groups in indicators of diabetic care, congestive heart failure care, asthma care and cancer screening rates. Larger differences were found in preventive care for children less than 2 years of age, particularly for alternatively licenced physicians who entered Ontario from another Canadian province. CONCLUSIONS: Our findings demonstrate that alternatively licenced physicians perform similarly to traditionally licenced physicians across many indicators of primary care. Our study also demonstrates the utility of administrative data for examining physician performance and evaluating medical regulatory policies and programmes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".