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Record W4285393574 · doi:10.21203/rs.3.rs-1829650/v1

The geographic distribution of pharmacies in Canada

2022· preprint· en· W4285393574 on OpenAlexafffundabout
Paul Grootendorst

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Toronto
FundersAssociation des pharmaciens du Canada
KeywordsPharmacyPer capitaMedical prescriptionService (business)BusinessDistribution (mathematics)MedicinePharmacistPopulationNova scotiaGeographyFamily medicineSocioeconomicsDemographyEnvironmental healthNursingMarketing

Abstract

fetched live from OpenAlex

Abstract Background Pharmacists are assuming an increasingly important role in the provision of primary care services. This raises questions about access to pharmacy services among those with medical needs. There is evidence on proximity of residents of Ontario and Nova Scotia to community pharmacies. There is little evidence for the rest of Canada. Methods We investigated availability of pharmacist services, both the number of community pharmacies and their hours of operation, at both the provincial and sub-provincial (Forward Sortation Area, FSA) level in Canada. We estimated regression models to assess associations of FSA-level service availability and medical need as measured using the share of residents aged 65 + and median household income. Results There are between 2.0 and 3.3 community pharmacies per 10,000 population, depending on the province. There are also provincial variations in the number of hours that pharmacies are open. Quebec pharmacies were open a median of 75 hours a week. In Manitoba, pharmacies were open a median of 53 hours a week. The per capita number of pharmacies and their total hours of operation at the FSA level tend to be higher in less affluent regions and in which the share of residents is aged 65 or older. Provincial differences in pharmacy availability were still evident after controlling for medical need. Conclusion Community pharmacies in Canada tend to locate where health needs (as measured using advance age and low income) are greatest. The impact on patient health outcomes of these pharmacy locational patterns remains an area for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.140
GPT teacher head0.532
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes3
Has abstractyes

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