Examining Distance Effects on Hospitalizations Using GIS: A Study of Three Health Regions in British Columbia, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".