Exploring Dual Practice Dynamics in Healthcare Operations Management
Bibliographic record
Abstract
The Canadian healthcare system predominantly operates within a publicly funded model, where strict regulations prevent physicians from simultaneously practicing in both the public and private sectors. These restrictions are established to address concerns that allowing dual practice would incentivize physicians to prioritize their private service over their public practice, potentially compromising equitable access to healthcare for all members of society. However, literature on multi-channel service provision suggests that service systems allowing the operation of dual service channels tend to yield an overall higher level of welfare. This paper develops an analytical model to study the implications of dual practice in the Canadian healthcare system. Our model examines physician decision-making regarding capacity allocation across the public and private sectors, as well as the pricing decisions for private practice, and evaluates patient behavior in response to these decisions. Unlike prior studies, which often treat private and public providers as independent entities, our model assumes a single physician managing both service channels, resulting in a more nuanced analysis of dual practice dynamics. Using game-theoretic and queueing-based frameworks, we study the implications of different regulatory approaches to dual practice on service access, patient welfare, and physician welfare. Our findings contribute to the ongoing policy debate by providing insights into how dual practice might influence efficiency, equity, and accessibility in the healthcare system.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".