Defining Rational Hospital Catchments for Non-Urban Areas Based on Travel-Time
Bibliographic record
Abstract
Background: Cost containment typically involves rationalizing healthcare service delivery throughcentralization of services to achieve economies of scale. Hospitals are frequently the chosen site ofcost containment and rationalization especially in rural areas. Socio-demographic and geographiccharacteristics make hospital service allocation more difficult in rural and remote regions. Thisresearch presents a methodology to model rational catchments or service areas around ruralhospitals – based on travel time.Results: This research employs a vector-based GIS network analysis to model catchments thatbetter represent access to hospital-based healthcare services in British Columbia's rural andremote areas. The tool permits modelling of alternate scenarios in which access to different basketsof services (e.g. rural maternity care or ICU) are assessed. In addition, estimates of the percentageof population that is served – or not served -within specified travel times are calculated.Conclusion: The modelling tool described is useful for defining true geographical catchmentsaround rural hospitals as well as modelling the percentage of the population served within certaintime guidelines (e.g. one hour) for specific health services. It is potentially valuable to policy makersand health services allocation specialists.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".