Mapping the social demography and location of HIV services across Toronto neighbourhoods
Bibliographic record
Abstract
In this paper we map the location and distribution of HIV service providers across Toronto neighbourhoods. Our analysis identified an uneven distribution of services across Toronto and a number of communities that are less accessible to HIV-related services. We subsequently identified three neighbourhood-level characteristics of the populations living within these communities (i.e. concentrated economic disadvantage, concentrated immigration, and residential instability). Our findings suggest a significant overlap in the location of HIV service providers and the clustering of neighbourhood-level demographic and socioeconomic factors. Some inaccessible neighbourhoods overlap with clusters of neighbourhoods with higher levels of concentrated disadvantage, immigration and percentage of black Canadians. Accessible neighbourhoods are located within the downtown core of Toronto and overlap with clusters of highly dense, younger neighbourhoods (with a high proportion of 15- to 34-year-olds who are unmarried). Our findings point to the need for policy-makers to integrate spatial analytic techniques into their examination of the types of neighbourhoods, and subsequently the community members that live within those neighbourhoods, that are potentially underserved with respect to health and social services.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".