The Development and Reliability Testing of a Tool to Assess Women’s Perceptions and Avoidance of Endocrine Disruptors in Personal and Household Products
Bibliographic record
Abstract
Personal care and household products (PCHPs) often contain endocrine-disrupting chemicals (EDCs) that pose health risks, especially for women. Women, frequent users of PCHPs, are exposed to approximately 168 chemicals daily. However, there are gaps in understanding women’s knowledge, risk perceptions, and beliefs regarding these chemicals, as well as how these constructs influence their avoidance behavior. Existing questionnaires on EDCs in PCHPs lack reliability and comprehensiveness. Guided by the Health Belief Model, this study developed a self-administered questionnaire targeting four key constructs: knowledge, health risk perceptions, beliefs, and avoidance behaviors related to six EDCs commonly found in PCHPs (lead, parabens, Bisphenol A, phthalates, triclosan, and perchloroethylene). The questionnaire was distributed to 200 women at in-person events and online. The internal consistency of the constructs was tested using Cronbach’s alpha. The questionnaire included six items assessing knowledge, seven items on risk perceptions, five items on beliefs, and six items on avoidance behavior for each endocrine-disrupting chemical. Cronbach’s alpha values indicated strong reliability across all constructs. This newly developed questionnaire offers a reliable tool for assessing women’s knowledge, risk perceptions, beliefs, and behaviors toward EDCs in PCHPs. These findings could inform public health research and intervention strategies aimed at reducing women’s exposure to EDCs.
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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 | 0.037 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".