Social environment and asthma: associations with crime and No Child Left Behind programmes
Bibliographic record
Abstract
BACKGROUND: The relationship between asthma and socio-economic status remains unclear. The authors investigated how neighbourhood, school and community social environments were associated with incident asthma in Southern California schoolchildren. METHODS: New-onset asthma was measured over 3 years of follow-up in the Children's Health Study cohort. Multilevel random-effects models assessed associations between social environments and asthma, adjusted for individual risk factors. At baseline, subjects resided in 274 census tracts (ie, neighbourhoods) and attended kindergarten or first grade in one of 45 schools distributed in 13 communities throughout Southern California. Neighbourhoods and communities were characterised by measures of deprivation, income inequality and racial segregation. Communities were further described by crime rates. Information on schools included whether a school received funding related to the Title 1 No Child Left Behind programme, which aims to reduce academic underachievement in disadvantaged populations. RESULTS: Increased risk for asthma was observed in subjects attending schools receiving Title I funds compared with those from schools without funding (adjusted HR 1.71, 95% CI 1.14 to 2.58), and residing in communities with higher rates of larceny crime (adjusted HR 2.02, 95% CI 1.08 to 3.02 across the range of 1827 incidents per 100,000 population). CONCLUSIONS: Risk for asthma was higher in areas of low socio-economic status, possibly due to unmeasured risk factors or chronic stress.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".