<i>Information Needs of Women</i> At Risk of Breast Cancer
Bibliographic record
Abstract
PURPOSE: Information needs and current information sources related to healthy eating, active living, and healthy body weight were examined in women attending breast cancer risk assessment clinics. METHODS: Convenience sampling was used to recruit 257 women over 20 years old. The women completed a self-administered survey (52.8% response rate) containing both closed- and open-ended questions. Most respondents were 49 or younger, were English-speaking, reported annual family incomes over $140,000, and resided in urban communities. RESULTS: Participants reported a need for general information concerning healthy eating, active living, and healthy body weight. For example, they wanted information on reading food labels (51.0%), healthy recipes (51.0%), activities for increasing overall fitness (52.5%), and achieving healthy body weights (48.6%). They also wanted information concerning the relationships between cancer risk and specific foods and nutrients, such as antioxidants (65.0%), supplements (60.7%), phytochemicals (47.5%), and omega-3 fatty acids (45.5%). Participants most often turned to magazines, friends, and family members when they wanted information on healthy eating, active living, and healthy body weight. CONCLUSIONS: These findings present an opportunity for dietitians to enhance their leadership role in creating and disseminating evidence-based information to meet the expressed needs of women who may be at increased risk for breast cancer.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".