Are Neighborhood Features Associated With Premature Mortality in Toronto Neighborhoods?
Bibliographic record
Abstract
Objective: The relationship between neighborhood social and economic features and the residents’ premature mortality rate is a controversial topic that has brought concerns from many local governments. The purpose of this paper was to determine the impacts of three indicators, including numbers of health providers, numbers of drug arrests, and neighborhood equity scores on premature mortality in the 140 neighborhoods in the City of Toronto. Methods: Conducting regression analysis by using the data from January 2018 to December 2018 obtained from OpenData Toronto. The number of health providers, which shows how many medical service sectors the local community has is generated into a dummy variable (<1.5 or ≥1.5 health providers per 1000 people), and all datasets are cleaned into the same unit, which is per thousand people. Both single regressions and multiple regression are used to compare the change in premature mortality rate, which means the deaths occurred before 70 years old. Results: Taking all indicators into weighted consideration, the empirical evidence shows that the premature mortality rate increased by 4% on average with every one additional drug arrest incident occurring per thousand people while with every additional health provider per one thousand citizens, the premature mortality rate will decrease by 10% on average; In terms of neighborhood equity score, one point increase is associated with a roughly 1% decrease in premature mortality rate on average. Conclusion: Social and economic factors are closely associated with the local premature mortality rate and actively improving the local living conditions can decrease the premature mortality rate while preventing serious issues before it actually occurs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".