Development and Validation of the Prevention of Toxic Chemicals in the Environment for Children Tool: A Questionnaire for Examining the Community's Knowledge of and Preferences Toward Toxic Chemicals and Children's Brain Development
Bibliographic record
Abstract
Early-life exposures to toxic chemicals can adversely impact brain development. Understanding people's knowledge of the impact of toxic chemicals on brain development is critical to reduce widespread exposure to chemicals. Yet it is unknown what people know about risks of toxic chemicals and how to reduce exposures. We developed and validated the questionnaire, PRevention of Toxic chemicals in the Environment for Children Tool (PRoTECT), to examine people's knowledge and attitudes about the influence of toxic chemicals on child development. We used best practices for developing and validating scales. First, we drafted items to assess knowledge of the impact of toxic chemicals on brain development, levels of concern regarding exposures, and preferences for prevention of neurodevelopmental disorders. Second, we received feedback on item clarity from five focus groups consisting of 46 community participants. In addition, 17 experts completed a content validity scale for each item and provided qualitative feedback. We administered the revised 18-item questionnaire to 190 participants of child-bearing age for scale development, and using exploratory factor analysis, we found evidence for a four-factor model of PRoTECT, RMSR = 0.05, of which 16 of the 18 items had adequate content validity with loadings >0.40 on a derived factor. We discuss future directions and applications of PRoTECT.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".