Geographic Accessibility of Community Pharmacies in Ontario
Bibliographic record
Abstract
BACKGROUND: Proximity is an important component of access to healthcare services. Recent changes in generic pricing in Ontario have caused speculation about pharmacy closures. However, there is little information on the current geographic accessibility of pharmacies. Therefore, we studied geographic access to pharmacies and modelled the impact of possible closures. METHODS: We used location data on the 3,352 accredited community pharmacies from the Ontario College of Pharmacists and population estimates at the census dissemination block level. Using network analysis, we determined the share of Ontario's population who reside in a census dissemination block within three road travel distances of a community pharmacy: 800 m (walking), 2 km and 5 km (driving). We then simulated the effects on these measures of 10% to 50% reductions in the number of community pharmacies in Ontario. RESULTS: Approximately 63.6% of the Ontario population reside in a dissemination block located within walking distance of one or more pharmacies; 84.6% and 90.7% reside within 2-km and 5-km driving distances, respectively. Randomly removing 30% of Ontario's community pharmacies reduces these estimates to 56.0%, 81.4% and 89.0% for each distance, respectively; a 50% reduction results in 48.3%, 77.1% and 87.2%, respectively. CONCLUSIONS: Pharmacies are geographically accessible for a majority of the Ontario population. Moreover, it appears that modest closures would have only a small impact on geographic access to pharmacies. However, closures may have other impacts on access, such as cost, waiting time and reduced patient choice.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".