Barriers and opportunities for breast cancer organizations to focus on environmental health and disease prevention: a mixed-methods approach using website analyses, interviews, and focus groups
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most commonly diagnosed cancer among women worldwide and most cases are not due to high risk inherited genes. In response, breast cancer activists successfully advocated for innovative research on environmental chemical exposures as a possible cause. Since then, new evidence supports hypotheses that common industrial and consumer chemicals are linked to the disease, and expert panels recommend reducing exposures. We evaluated whether these research results and recommendations are translated back into the work of breast cancer organizations and what barriers and opportunities influence their ability to focus on environmental factors. METHODS: We used a Python script to evaluate the frequency of environmental terms on the websites of 81 breast cancer organizations (> 14,000 associated URLs) and conducted two focus groups and 20 interviews with leaders of breast cancer organizations. We also analyzed the frequency of terms on two trusted, national cancer websites. RESULTS: 40% of organizations include information on environmental chemicals on their websites, but references are infrequent and rarely cite specific chemicals of concern. Most organizations (82%) discuss other risk factors such as exercise, diet, family history, or genetics. From interviews and focus groups, we identified four types of barriers to addressing environmental chemicals: 1) time and resource constraints, 2) limited knowledge of the state of the research and lack of access to experts, 3) difficulties with messaging, including concern that cultural and economic factors make it difficult for individuals to reduce their exposures, and 4) institutional obstacles, such as the downplaying of environmental risks by industry interests. Participants expressed the desire for easy-to-adopt educational programs and increased federal funding for scientist-advocate research partnerships. CONCLUSION: Our research underscores the need for environmental breast cancer experts and trusted cancer organizations to increase research translation activities so that breast cancer organizations can communicate new science on environmental factors in their online and in-person work. Moreover, our research highlights how most groups are focusing on providing resources to diagnosed women, including addressing problems with healthcare access, which displaces their ability to work on breast cancer prevention.
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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 | 0.106 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".