Environmental Health Attitudes, Practices, and Educational Preferences: A National Survey of Reproductive-Aged Women in Canada
Bibliographic record
Abstract
Prenatal exposures to environmental toxicants can adversely affect fetal and child development and lead to increased risk of chronic disease. While regulatory action is essential to reduce sources of environmental toxicants, prenatal care presents an opportunity to educate, mobilize, and support prospective parents to reduce exposures to such hazards. As the first phase of an interdisciplinary research collaboration to inform the development of prenatal environmental health education strategy in Canada, we surveyed reproductive-aged female individuals. The online survey (July-September 2021) yielded a nationally representative sample of 1914 reproductive-aged females living in Canada. The questionnaire topics addressed the respondents' knowledge and perceptions of environmental health risks, preventive actions and related facilitators and barriers, information sources and preferences, reproductive history, and demographics. The analysis included bivariate and multivariate techniques. Our results suggest broad awareness among reproductive-aged females that exposure to toxicants can be harmful, and that reducing prenatal exposures can benefit child health. However, fewer than half of respondents felt that they had enough knowledge to take protective measures. Despite high levels of preference for prenatal care as an ideal context for learning about environmental health risks and protective measures, fewer than one in four respondents had ever discussed environmental health concerns with a healthcare provider. Our findings reveal a knowledge-action gap and a corresponding opportunity to improve environmental health education and advocacy in prenatal care in the Canadian context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".