Prescribing power and equitable access to care: Evidence from pharmacists in Ontario, Canada
Bibliographic record
Abstract
Allowing pharmacists to directly treat patients may increase equitable access to healthcare and improve patient outcomes, but raises concerns about supply-side moral hazard or patient substitution away from regular physician-based care. We study the effects of a 2023 policy allowing pharmacists to prescribe for minor ailments in Ontario, Canada. We use Advan foot traffic data to measure how this policy affected visits to pharmacies and generated spillover effects on visits to non-pharmacy medical facilities (Research, 2022). Allowing pharmacists to prescribe led to a 16% increase in total visits to pharmacies and a 3% increase in visits to other providers. These increases were concentrated in materially deprived neighborhoods and benefited non-minority, non-immigrant populations the most. We use the policy as exogenous variation to identify substitution elasticities between pharmacy visits and traffic to other medical facilities. Overall, 20% of the increase in traffic to pharmacies spills over into increased use of outpatient-based care. Pharmacy traffic is a substitute for visits to hospitals and emergency departments, potentially as patients rely on pharmacists for triaging rather than emergency 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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".