Neighbourhood low income, income inequality and health in Toronto.
Bibliographic record
Abstract
OBJECTIVES: This study examines the association of neighbourhood low income and income inequality with individual health outcomes in Toronto, Canada's largest census metropolitan area. DATA SOURCES: The data are from the cross-sectional component of Statistics Canada's 1996/97 National Population Health Survey (NPHS) and the 1996 Census of Population. ANALYTICAL TECHNIQUES: Individual records for Toronto residents aged 12 or older who responded to the 1996/97 NPHS were augmented with aggregated data from the 1996 Census to provide information on the average socio-economic characteristics of the respondents' neighbourhoods. Hierarchical linear models were used to estimate the effect of low income and income inequality at the neighbourhood level on selected health outcomes. MAIN RESULTS: When individual low-income status and several other individual characteristics were taken into account, the neighbourhood low-income rate and income inequality were not associated with individuals' reported number of chronic conditions or distress. However, both low income and income inequality at the neighbourhood level remained significantly associated with poor self-perceived health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".