Bibliographic record
Abstract
In this chapter, we examine travel distance and its effect on total and avoidable hospitalizations using data from the capital health region in British Columbia, Canada. We developed a GIS procedure to connect distance-to-hospital with socioeconomic contexts of patient locations. The procedure includes geo-coding hospital locations and patient locations to determine travel distance for each hospitalization, generating several geographic barriers, such as mountain crossing, to assess their impedance, and linking patient neighborhood locations to socioeconomic variables of their locations. It was found that the overall hospitalization rates have an inverse relationship with distance-to-hospital, and living too close to a hospital may encourage utilization of hospital resources. Even though low-income patients are more likely to be hospitalized for avoidable conditions, the income effect influences different dimensions to those affected by the distance effect. Thus, it explicitly confirms the two aspects of the inverse of healthcare law that work simultaneously: those with lower socioeconomic status and those living in greater distance to hospitals tend to be less likely to access hospital care. Furthermore, the inclusion of physical barriers to our evaluation enhanced our understanding of local conditions and how they may affect hospitalizations.Request access from your librarian to read this chapter's full text.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".