Global randomized controlled trial of knowledge translation of children’s environmental health
Bibliographic record
Abstract
Objectives Toxic chemicals can harm children’s brain development, but the public’s understanding of these harmful impacts is largely unknown. People’s knowledge of toxic chemicals and their awareness of how to reduce children’s exposure was examined. This study also assessed whether a video was efficacious in increasing knowledge about toxic chemicals and brain development and encouraging behavioral change to reduce exposure to toxic chemicals. Methods 15,594 participants of child-bearing age (18–45 years old) from five countries (Canada, United States, United Kingdom, India, and Australia) were surveyed via CloudResearch’s Prime Panels®. After completing a baseline survey, Prevention of Toxic Chemicals in the Environment for Children Tool (PRoTECT), participants were randomly assigned to watch a knowledge translation video (experimental group) or serve as a control group. Next, participants were asked about barriers and intentions to reduce exposure to toxic chemicals. After 6 weeks, a subset (n = 4,842) of participants were surveyed with PRoTECT and asked whether they modified behaviors to reduce exposure to toxic chemicals or plan to speak to their healthcare provider (HCP) about toxic chemicals. Results Participants expressed strong preferences for lowering exposures and preventing disabilities. Participants who knew more about the impact of toxic chemicals on children’s health were more likely to prefer investing in prevention and reducing their exposures. Participants who viewed the video showed significantly greater changes in PRoTECT scores. At the 6-week follow-up, no differences in behavioral changes were observed by group assignment, but two-thirds of all participants reported making changes to reduce their exposures and half intended to speak with their HCP. Conclusion There were significant differences in knowledge and preferences by group assignment, but systemic barriers, such as cost of non-toxic products and difficulty determining how and where to buy them, hindered people from making changes to reduce their exposures to toxic chemicals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 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".