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Record W2968391783 · doi:10.1111/cag.12557

Geographic accessibility to primary care providers: Comparing rural and urban areas in Southwestern Ontario

2019· article· en· W2968391783 on OpenAlexaffvenueabout
Tayyab Shah, Andrew Clark, Jamie A. Seabrook, Shannon L. Sibbald, Jason Gilliland

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteWestern University
Fundersnot available
KeywordsCensusGeospatial analysisGeographyCatchment areaAgency (philosophy)PopulationDistribution (mathematics)InequalityRural areaSocioeconomicsPrimary careEnvironmental healthMedicineCartographyDrainage basinFamily medicineSociology

Abstract

fetched live from OpenAlex

This research examines geographical accessibility to primary care providers (PCPs) across urban and rural areas of Southwestern Ontario and examines variations in the distribution of PCPs in relation to the senior population (aged 65 years and older). Information about PCP practices was provided by the HealthForceOntario Marketing and Recruitment Agency. Population data were obtained from the 2016 Census of Canada. To calculate scores for accessibility to PCPs (i.e., PCPs/10,000 population), we applied the enhanced 2‐step floating catchment area method with distance decay effect within a global service catchment of 30‐minute drive time. A geospatial mapping approach revealed disparities in the distribution of PCPs with a pattern of higher spatial accessibility in or around major urban areas in Southwestern Ontario. Comparative analyses were performed in association with the seniors’ population to identify how accessibility scores were mismatched with the population needs. The outcome of this study will assist researchers and health service planners to better understand the distribution of existing PCPs to address inequalities, particularly in rural areas.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.236
Teacher spread0.224 · 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 teacher head, not a consensus.

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

Citations65
Published2019
Admission routes3
Has abstractyes

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