Bibliographic record
Abstract
Objective To assess young women's breast health knowledge and explore its relation to the use of screening mammography. Methods A convenience sample of 180 women aged 25-45 residing in Toronto, Canada, with no history of breast cancer and mammography received an information brochure and four questionnaires which assessed their knowledge of breast health, sociodemographic/clinical character istics, and intended decision to use screening mammography. Results Women's overall baseline knowledge score ranged from 0-18 with a mean of 10 and SD of 3.9. About half of the participants had a low score (0-9), 31 per cent did not know that the risk of breast cancer increases with age, 43 per cent did not know what was the appropriate age to start breast self examination (BSE), and 48 per cent did not know how frequently BSE should be performed. The overall knowledge score was significantly higher for those women who intended to use screening than those who did not (10.8 vs. 8.4, p<0.001). The intended use of screening was associated with women's knowledge ( p<0.001), self-perceived health ( p=0.02), perceived susceptibility to breast cancer ( p=0.04) and perceived usefulness of information ( p=0.001). Conclusion The study demonstrates that breast health knowledge is limited even among highly educated young women. This finding suggests the need for providing breast health education to women at earlier stage in their life when they have ample time to go through a logical series of decision stages on their way to adopting a new behaviour.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".