Prevention of Disability in Children: Elevating the Role of Environment
Bibliographic record
Abstract
Much public attention and many resources are focused on medical research to identify risk factors and mitigate symptoms of disability for individual children. But this focus will inevitably fail to prevent disabilities. Stephen Rauch and Bruce Lanphear argue for a broader focus on environmental influences that put entire populations at risk. They argue that identifying and eliminating or controlling environmental risk factors that incrementally increase the prevalence of disability is the key to preventing many disorders. Rauch and Lanphear examine emerging evidence that many disabilities of childhood have their roots in the environment--from toxins in air, water, and soil, to the stressors of poverty, to marketing practices that encourage unhealthy choices or discourage healthy ones. They review research on well-known environmental causes of disability, such as exposures to lead, cigarette smoke, and industrial air pollution. They point to new evidence suggesting that chemicals found in commonly used plastics may have subtle but serious effects on child development, and that many disabilities spring from the complex interplay of environmental risk factors and genetic susceptibility. Rauch and Lanphear make a case for turning our attention to societal or population-level interventions that would rely less on medical and genetic technology and more on policies and regulations that would reduce children's exposure to ubiquitous environmental risks. Examples include required testing of new chemicals for developmental toxicity before they are put on the market; zoning regulations that separate residential communities from industrial areas; and restrictions on advertising of unhealthy products, such as tobacco, alcohol, and junk foods, to children. Rauch and Lanphear outline and assess the effectiveness of interventions that could be adopted, and suggest what a healthy modern community might look like. Such interventions, they acknowledge, are likely to be highly controversial, require both long-term investments and shifts in societal thinking, and produce less well-defined outcomes than individual medical treatments. But in the long run, the authors contend, such interventions could prevent many of the disabilities that now afflict millions of children and adults.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".