Spatial Accessibility to Primary Healthcare Services by Multimodal Means of Travel: Synthesis and Case Study in the City of Calgary
Bibliographic record
Abstract
Universal access to primary healthcare facilities is a driving goal of healthcare organizations. Despite Canada's universal access to primary healthcare status, spatial accessibility to healthcare facilities is still an issue of concern due to the non-uniform distribution of primary healthcare facilities and population over space-leading to spatial inequity in the healthcare sector. Spatial inequity is further magnified when health-related accessibility studies are analyzed on the assumption of universal car access. To overcome car-centric studies of healthcare access, this study compares different travel modes-driving, public transit, and walking-to simulate the multi-modal access to primary healthcare services in the City of Calgary, Canada. Improving on floating catchment area methods, spatial accessibility was calculated based on the Spatial Access Ratio method, which takes into consideration the provider-to-population status of the region. The analysis revealed that, in the City of Calgary, spatial accessibility to the primary healthcare services is the highest for the people with an access to a car, and is significantly lower with multimodal (bus transit and train) means despite being a large urban centre. The social inequity issue raised from this analysis can be resolved by improving the city's pedestrian infrastructure, public transportation, and construction of new clinics in regions of low accessibility.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".