Examining the Burden of Disease and its Relationship to Socioeconomic Status From Environmental Noise Exposure in Toronto and London, Ontario
Bibliographic record
Abstract
Exposure to environmental noise pollution is considered an important public health issue in our world today. Environmental noise is sound that is generated by humans through things such as transportation networks and construction sites. Research has shown that exposure to noise can result in a variety of adverse health effects, such as annoyance, sleep disturbance, and ischemic heart disease. Furthermore, research has found that exposure to higher levels of noise is consistently associated with people who are of lower socioeconomic status. This study aims to determine the burden of disease rates in Toronto and London, Ontario, while also addressing the relationship between noise exposure and socioeconomic status. Based on a review of literature and building on existing work, burden of disease calculations were carried out to determine the amount of Disability Adjusted Life Years (DALYs) lost in each city, while a linear regression model was used to determine the relationship between exposure to noise and socioeconomic status. Burden of disease calculations demonstrated that 1188 and 1662 DALYs were lost in Toronto and London, respectively. In addition, this analysis of exposure to noise and socioeconomic status resulted in a moderate correlation of exposure to noise and low socioeconomic status. Indicating that as noise levels increase the socioeconomic status of an individual decreases. These results combined indicate that there is a double burden of poorer health and low socioeconomic status related to exposure to environmental noise pollution.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".