Spatial Analysis of Access to Health and Care Services in Canada Across the Rural/Urban Spectrum
Bibliographic record
Abstract
Equal access to rural health services in Canada is significantly impacted by geography and the diversity of rural and remote settlements spread over vast areas.Most studies that examine access to health and care services treat rural and urban regions as dichotomies in statistical models.The first paper in this thesis is a scoping review of Canadian studies that employ a spatial approach to analysis of health service delivery or outcomes.The findings show that while spatial analytic techniques are being used to demonstrate multiple health inequities, there is room for improving the depth of analysis.A second paper conducts an exploratory spatial data analysis of local-level patterns of mammography participation rates.Results of a multiscale geographically weighted regression show considerable local variation in explanatory variables.This project demonstrates a scalable analytic framework to utilize existing health administrative databases and population statistics to inform health care access research in Ontario.I spent many years gathering the courage to take the step towards beginning a Master's degree, and this endeavor was made possible the day that Dr. Paul Peters decided to take a chance on me.For believing in me and seeing that I had what it would take to pull this off, I will forever be grateful.Thank you for the encouraging words, challenging me to view data from new perspectives, and the many coffee chats.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".