Using the Internet to Identify Women's Sources of Breast Health Education and Screening
Bibliographic record
Abstract
Health professionals, women's groups, the media and the Internet have all played a role in educating the public about breast health and breast screening methods. Yet, with all the information that is available to women, their participation rates have been less than optimal. This paradox has resulted in the need to learn more about the sources that influence women to participate in breast screening. In an innovative study using the Internet, over 800 women, primarily from Canada and the United States, were surveyed about their knowledge, attitudes and influences regarding their breast screening practices. Current health status, screening practices and influences of various health professionals on women's health promotion activities were analyzed. Comparisons of the women's perceptions across age groups and national differences between Canadian and American respondents are presented. Women in the older age group reported receiving more encouragement for breast screening activities from physicians, nurses and others than did younger women. American respondents reported perceiving more support from nurses for breast screening than did their Canadian counterparts. A high number of American respondents reported having been diagnosed with breast cancer, while only a small number of Canadian respondents reported this diagnosis. The results from this study can be used in planning health promotion activities relevant to various populations of women. Benefits and limitations of using the Internet as a research medium are briefly discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".