Policy changes and physicians opting out from Medicare in Quebec: an interrupted time-series analysis
Bibliographic record
Abstract
BACKGROUND: In all Canadian provinces, physicians can decide to either bill the provincial public system (opt in) or work privately and bill patients directly (opt out). We hypothesized that 2 policy events were associated with an increase in physicians opting out in Quebec. METHODS: and a regulatory clampdown forbidding double billing that was implemented by Quebec's government in 2017. We used interrupted time-series analyses of the Quebec government's yearly list of physicians who chose to opt out from 1994 to 2019 to analyze the relation between these events and physician billing status. RESULTS: The number of family physicians who opted out increased from 9 in 1994 to 347 in 2019. Opting out increased after the Chaoulli ruling, and our analysis suggested that between 2005 and 2019, 284 more family physicians opted out than if pre-Chaoulli trends had continued. The number of specialist physicians who opted out rose from 23 in 1994 to 150 in 2019. Our analysis suggested that an additional 69 specialist physicians opted out after the 2017 clampdown on double billing than previous trends would have predicted. INTERPRETATION: We found that the number of physicians who opted out increased in Quebec, and increases after 2 policy actions suggest an association with these policy interventions. Opting out decisions are likely important inputs into decision-making by physicians, which, in turn may influence the provision of publicly funded health care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".