Pregnant Women’s perceptions of exposure to brominated flame retardants
Bibliographic record
Abstract
BACKGROUND: Recent media reports on human studies associating brominated flame retardants (BFRs) in household products in pregnancy with urogenital anomalies in boys and endocrine disruption in both sexes. We sought to explore the perceptions of pregnant women of brominated flame retardant (BFR) exposure, in light of recent media reports on the adverse health effects of BFR exposure prenatally. METHODS: Pregnant women were recruited for interviews through posters and pamphlets in prenatal clinics, prenatal fairs and community centres. Interviews were audiotaped and transcribed verbatim for Charmaz-based qualitative analysis supported by NVIVO 10™. RESULTS: Theoretical sufficiency was reached after analyzing the interviews of 23 pregnant women. Themes co-constructed were: I-Lack of Awareness of BFRs; II-Factors Influencing BFR Exposure; III-Responsibility; IV-Informed Choice. Almost all participants felt it was difficult to make informed choices to avoid BFRs, and wanted communication from clinicians and regulation from governments regarding decreasing BFR exposure. CONCLUSION: Pregnant women in Canada may be unaware of the potential risks of exposure to BFRs. Professional organizations and governments should further study risk associated with BFR exposure in pregnancy and provide educational materials for pregnant women and clinicians regarding BFR exposure.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".