Patient, Physician, and Community Factors Affecting Referrals to Specialists in Ontario, Canada
Bibliographic record
Abstract
QUESTION ADDRESSED: This population-based study examines the factors affecting referrals by primary care physicians (PCPs) to specialists. MATERIALS AND METHODS: Multilevel Poisson models were used to test the impact of patient, physician and community-level variables on the referral rate (the number of office-based specialist referrals per patient by the patient's customary PCP in fiscal year 1997/98). Patients from each of 6972 PCPs with sufficient data in Ontario were examined. RESULTS: The average patient had 0.56 referrals per year (range 0-61). Referrals were higher at ages 1 and 77 to 78, and among women of childbearing age. Chronic disease variables were strongly correlated with referral rates. Patients in poor neighborhoods had more referrals, because they had more chronic diseases. After controlling for disease, individuals in the top 9% wealthiest neighborhoods had 4% more referrals. Female physicians made 8% more referrals than men. Older physicians referred more because they saw older patients; after controlling for patient age, physician age had no effect. Referrals were 14% higher in cities with medical schools compared with other cities and 12% lower in small towns. However, local specialist supply was unrelated to referral rates. CONCLUSION: This study improves our understanding of the impact of physician gender and age on referrals. It suggests that community type, not specialist supply, predicts variations in referrals. Lastly, it identifies preferential access to specialists among high-income earners, even within Canada's universal health insurance system. However, this effect is modest, suggesting that the system does provide reasonably equitable access to referrals.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".