Knowledge of Antioxidants and Breast Cancer Risk Among Women Attending Breast Cancer Risk Assessment Clinics
Bibliographic record
Abstract
This qualitative study used semistructured interviews to examine the accuracy of knowledge concerning antioxidants and health among a convenience sample of 79 women attending a breast cancer risk assessment clinic. Despite a high level of familiarity (98%) with the word antioxidant, few participants could name more than one of these compounds and most relied on print media (41.6%) and radio/TV (22.2%) for antioxidant information. Thematic content analysis revealed participants' beliefs that antioxidants were strongly linked to reduced breast cancer risk and improved health. They described antioxidant functions that take place before (e.g., "Prevention . . . a best defense mechanism" and "To boost strength and good health") or after (e.g., "Fights diseases, free radicals, and cancer," "Acts as a cleanser or purifier," and "Undoes the harm that I am consciously or unconsciously doing to my body") a health threat. Participants' understandings of the links between antioxidant intake and breast cancer risk did not accurately reflect the scientific evidence. This large priority population group needs tailored, evidence-based nutrition communications to address inaccurate understandings about antioxidant intake and breast cancer risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| 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 | 0.000 |
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".