Unveiling the Spatial Dimensions of Hospitalization and Resource Utilization in Rural Ontario: A Comprehensive Analysis of Travel Time, Socio-demographic Factors, and Healthcare System Usage
Bibliographic record
Abstract
The provision of equitable and appropriate health and care services to rural communities is a critical component of the Canadian health system. Rural communities face severe pressures from poor access to health services and health policies that reinforce these challenges. Consequently, rural health inequalities are a concern for policymakers and researchers interested in rural populations. Accessibility of appropriate health services varies due to spatial (e.g., geographic barriers such as distance, climate, or natural features) and non-spatial factors (e.g., socio-demographic characteristics and health service planning). This thesis comprises three studies that collectively develop a comprehensive picture of rural health and care service accessibility in Ontario, Canada. The first study is a scoping review of how rural high-resource healthcare users are defined in the literature. Despite its frequent use, no standardized definition of high resource use exists. The second study evaluates accessibility to Emergency Departments (EDs) at the small-area level using a three-step floating catchment area (3SFCA) method. This method calculates accessibility scores based on supply, demand, and travel time, identifying changes in ED service access among rural residents by analyzing data from the 2011 Canadian Census Health and Environment Cohorts (CanCHEC) and the National Ambulatory Care Reporting System (NACRS) from 2018/19 to 2020/21. The third study explores the influence of spatial accessibility and socio-demographic factors on attachment to primary care using Geographic Weighted Regression (GWR). A mixed-methods approach examines how socio-demographic variables, such as material deprivation, residential instability, and ethnic concentration, correlate with primary care attachment. Findings reveal significant relationships between higher levels of deprivation, instability, and decreased attachment rates, underscoring the role of socio-economic factors in healthcare disparities. This research highlights substantial variations in healthcare access among rural residents. Local factors and spatial non-stationarity significantly impact healthcare utilization. Tailored healthcare policies are crucial to addressing disparities in ED use and primary care access. These findings contribute to a nuanced understanding of neighborhood-level healthcare variability, offering insights for improving service delivery and advocating for targeted strategies to enhance accessibility and reduce disparities, ultimately improving public health outcomes in rural Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".