Pregnant women’s navigation of information on everyday household chemicals: phthalates as a case study
Bibliographic record
Abstract
BACKGROUND: Current developments in science and the media have now placed pregnant women in a precarious situation as they are charged with the responsibility to navigate through information sources to make the best decisions for her pregnancy. Yet little is known regarding how pregnant women want to receive and use health information in general, let alone information regarding the uncertain risks to pregnancy in everyday household products such as phthalates found in cosmetics and canned food liners. Using phthalates as an example, this study investigated how pregnant women obtain, evaluate, and act on information regarding their pregnancy. METHODS: Pregnant women were recruited using pamphlets and posters distributed in prenatal clinics, prenatal fairs and physician offices in Southwestern Ontario Canada. Research participants were engaged in 20 to 40 min semi-structured interviews regarding their use of information sources in pregnancy, particularly regarding phthalates in cosmetics and canned food liners. Interviews were transcribed verbatim and analyzed using constructivist grounded theory techniques supported by NVivo 9™ software. RESULTS: Theoretical sufficiency was reached after 23 pregnant women were interviewed and their transcripts analyzed. Three overlapping themes resulted from the co-constructed analysis: I-Strength of Information Sources; II-Value Modifiers; and III-Deciding to Control Exposure. The research participants reported receiving information from a wide range of sources that they perceived varying in strength or believability. They then described the strategies employed to increase the validity of the message in order to avoid risk exposure. Pregnant women preferred a strong source of information such as physician, government but frequently used weak sources such as the internet or the opinions of friends. A model was developed from the relationship between themes that describes how pregnant women navigate the multiple sources of information available to them. CONCLUSION: Our study provides insight into how pregnant women receive, appraise, and act on information regarding everyday household chemicals. Clinicians and their professional organizations should produce specific educational materials to assist women in understanding exposure to everyday products in pregnancy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".