Code Grey: Mapping Healthcare Service Deserts in Hamilton, Ontario and the Impact on Senior Populations
Bibliographic record
Abstract
Given the precedent findings of health inequalities in Hamilton, ON and the rapid increase of elderly populations in Canada as a whole, this article looks for areas of deficient health services within the Hamilton region, characterized as “healthcare deserts,” and examines the possible implications with respect to the residing senior populations. Maps were constructed by overlaying median household income and percentage of population over 65 with the locations of healthcare services frequented by seniors. Qualitative analysis revealed that the distribution of senior services has no correlation to the senior population, and that senior services tend to be concentrated in lower income areas. This research has exposed the existence of healthcare deserts in most regions of Hamilton except the downtown core. Since seniors are less able to travel longer distances, living far away from these services could act as a barrier, inhibiting access and reducing quality of life. Concerns regarding accessibility of health services will become more important as this demographic grows, so mapping services in this manner can inform urban planning to minimize the impacts of these deserts.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".