Exploring hospital accessibility in British Columbia's Lower Mainland
Bibliographic record
Abstract
This thesis investigates healthcare accessibility in the Lower Mainland of British Columbia, which is a geographic area consisting of the regional districts of Metro Vancouver and the Fraser Valley. The research addresses three key questions: (1) What methods and metrics can be used to measure healthcare accessibility, and how can they be adapted to the Lower Mainland 's urban context? (2) What is the current state and spatial distribution of access to healthcare services in the Lower Mainland? (3) How does accessibility change with demographic factors and under disaster scenarios? The Enhanced Two-Step Floating Catchment Area (E2SFCA) method, adapted with a distance decay function, was employed to measure accessibility across Traffic Analysis Zones (TAZs) in the Lower Mainland using network information provided by TransLink. The analysis incorporated total bed capacity in hospitals with emergency rooms as a proxy for healthcare supply, and used normalized scores to compare the results across several case studies. The scenarios evaluated include a baseline assessment, a focus on senior and employed populations, and disaster simulations such as Disaster Response Routes (DRRs) and bridge closures following earthquakes. Results reveal significant spatial disparities in healthcare accessibility. While central and well-connected areas exhibit high accessibility, peripheral regions, such as Chilliwack and areas east of Abbotsford, face notable disadvantages, particularly for seniors. Disaster scenarios highlight the vulnerability of certain regions, where restricted routes and infrastructure disruptions can severely limit access to healthcare services. Despite the limitations of the network model, which excluded local roads and public transit, the findings underscore critical gaps in healthcare and transportation planning. By providing a detailed spatial understanding of healthcare accessibility, this thesis offers valuable insights for policymakers, healthcare providers, and urban planners in developing inclusive and disaster-resilient systems.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".