5 Translating developmental neurotoxicity for the public: A large, international, randomized-control trial investigating children's environmental health literacy
Bibliographic record
Abstract
Objective: Exposure to toxic chemicals during early brain development increases the risk of neurodevelopmental problems in children. Parents' and prospective parents' understanding of the impact of toxic chemicals on brain development and the efficacy of translation tools for children's environmental health literacy are poorly understood. We developed and validated a questionnaire, PRevention of Toxic chemicals in the Environment for Children Tool (PRoTECT) to assess knowledge of toxic chemicals and neurodevelopment, intentions to reduce exposures to toxic chemicals, and preferences for actions by government and industry to prevent neurodevelopmental disorders. Using PRoTECT, we surveyed people of child-bearing age across five countries (Canada, United States (US), United Kingdom (UK), India, and Australia) to identify general patterns of responses on this questionnaire by demographic characteristics, including country, age, gender, parental status, pregnancy status, and education. We also employed a randomized control design to examine the efficacy of a knowledge translation video to instill knowledge and prompt behavioral changes to reduce exposures to toxic chemicals immediately following its presentation and after a six-week follow-up period. Participants and Methods: We recruited 15,594 participants, ages 18 to 45, via CloudResearch's Prime Panels between October-December 2021. After completing the PRoTECT survey, participants were randomly assigned to watch the video Little Things Matter: Impact of Toxic Chemicals on Brain Development (i.e., the experimental group) or to serve as the control group. Next, both groups answered a series of questions to assess their knowledge of toxic chemicals, their intentions to reduce exposures to toxic chemicals, and barriers to changing their behaviours. After six-weeks, we recontacted a subset (N=4,842) of participants to repeat PRoTECT and answer the same series of behavioural questions assessing whether they modified any of their behaviours to reduce exposure and why or why not. Results: Most participants (i.e., 75-85%) agreed that toxic chemicals can impact brain development and endorsed preferences (∼85%) for allocating more resources to prevent neurodevelopmental disorders, especially people with higher education, parents and pregnant women, and people who lived in India. Despite this, a large proportion of participants (∼50%) trusted industry and believed that government effectively regulated toxic chemicals. After the six-week follow-up, experimental participants showed greater changes in scores on PRoTECT (i.e., between 5-15% change), indicating greater knowledge about harms posed by toxic chemicals, more intentions to reduce exposure, and stronger preferences for prevention as compared to the control group. Differences were larger among people from the US, those who were more highly educated, and people in their thirties. However, the differences between groups in making behavioural changes to reduce exposures were attenuated at the six-week follow up as compared to baseline. Significant barriers to reduce exposure to toxic chemicals were reported by both groups and included cost, inconvenience, and not knowing how to determine whether a product is non-toxic or where to purchase non-toxic products. Conclusions: We observed greater knowledge and concerns about toxic chemicals among more affluent respondents, pregnant women and parents, and people living in India across both groups. While the video enhanced participants' knowledge about toxic chemicals and intentions to reduce exposure, they indicated that barriers hindered them from making behavioral changes.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".