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Record W2033212463 · doi:10.1068/a3528

Examining Distance Effects on Hospitalizations Using GIS: A Study of Three Health Regions in British Columbia, Canada

2002· article· en· W2033212463 on OpenAlexaffabout
Ge Lin, Diane Allan, Margaret J. Penning

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGeocodingSocioeconomic statusGeographical distanceGeographic information systemLocationGeographyPerspective (graphical)Health careDemographyMedicineDemographic economicsEnvironmental healthComputer scienceEconomic growthCartographySociology

Abstract

fetched live from OpenAlex

We examine travel distance and its effect on total and on avoidable hospitalizations, using data from three health regions in British Columbia, Canada. We conceptualize distance to hospital in geographic and socioeconomic contexts from the care seeker's perspective, and develop a GIS procedure to generate variables for these contexts. The procedure includes geocoding hospital locations and patient locations to determine travel distance for each hospitalization, generating several geographic barriers such as mountain crossings, and linking patient-neighborhood locations to socioeconomic variables. The findings reveal that overall, hospitalization rates are inversely related to distance to hospital. Even though low-income patients are more likely to be hospitalized for avoidable conditions, the income effect influences different dimensions from those affected by the distance effect. A balanced approach may be needed to address issues appropriately at both the low and the high ends of physical accessibility.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.215
Teacher spread0.168 · 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.

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

Citations87
Published2002
Admission routes2
Has abstractyes

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