The Relationship of Socio-Demographic and Health (Asthma, Diabetes, & High Blood Pressure) in Toronto - a Quantitative and Spatial Approach
Bibliographic record
Abstract
Many literature and research indicate that health rates frequently follow a distinguishing socioeconomic gradient, where health is influenced by an individual's socioeconomic status (SES) and those of lower SES are more at risk to illness. The purpose of this research was to assess (within a bivariate and spatial context) how sociodemographic profiles influence asthma, diabetes, and high blood pressure rates in the City of Toronto. The objectives of this study were summed up in two questions; (1) "is there a spatial pattern to the rates of diabetes, asthma, and high blood pressure at the neighbourhood level in Toronto?" and (2) "are any socio-economic variables that can explain the patterns observed? And if so, which ones?". Toronto health rate variables were obtained from the OCHPP while sociodemographic variables were acquired from open-source data archives for the year 2016. The analysis was multi-step where the initial steps (bivariate regression and PCA) were for data reduction. The third step - measures of spatial autocorrelation (i.e. Moran's I) were tested on dependent variables to explore whether any broad spatial patterns existed. The final multivariate analysis modeled relationships between health measures and the SES measures. The first question was found to be true, where the Moran's I test and tests for spatial dependency indicated the presence of spatial autocorrelation and spatial dependence. However, the second question had somewhat mixed results as there was a lack of spatial significance regarding the correlation of the sociodemographic variables and health rates. Future research should rely less on socioeconomic variables being the main indicator of health rates and assess other causations such as lifestyle variables or geographic factors.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.002 | 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".