Residential exposure to noise, green space, and children’s language acquisition
Bibliographic record
Abstract
BACKGROUND: Noise pollution has been linked to impaired development in a variety of language-related skills in laboratory settings. While studies have focused on school environments, residential noise exposure's impact remains underexplored. METHODS: We used multilevel regression models to examine the association between noise exposure measured using deterministic noise modelling and language development in kindergarten-aged children in Vancouver, Canada, between April 1, 2000 and December 31, 2005, measured through a questionnaire completed by kindergarten teachers (the Early Development Instrument). The models were adjusted for median income level and English as a Secondary Language (ESL) status, as well as random effects on teachers, and we explored the potential interaction effects of greenness, measured using satellite imagery. RESULTS: The study included 33,153 children for which there were data on noise exposure and indicators of language development. The mean noise level was 63.5 dB(A), and the mean percentage of greenness within a 250-meter radius buffer zone was 31.8 %. We found that an increase in residential exposure to noise independently increased the odds of not meeting developmental expectations in basic literacy (OR: 1.18, 95 % CI: 1.12-1.25), advanced literacy (OR: 1.11, 95 % CI: 1.07-1.16), and communication and general knowledge (OR: 1.10, 95 % CI: 1.06-1.14). Greenness was found to have interaction effects with basic and advanced literacy skills in noisy environment. CONCLUSION: This study found residential exposure to noise was associated with poorer language development outcomes, with interaction effects of greenness observed in literacy skills. Future studies should also examine the long-term effects of residential exposure to noise on language development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".