Young women's responses to smoking and breast cancer risk information
Bibliographic record
Abstract
Current evidence confirms that young women who smoke or who have regular long-term exposure to secondhand smoke (SHS) have an increased risk of developing premenopausal breast cancer. The aim of this research was to examine the responses of young women to health information about the links between active smoking and SHS exposure and breast cancer and obtain their advice about messaging approaches. Data were collected in focus groups with 46 women, divided in three age cohorts: 15-17, 18-19 and 20-24 and organized according to smoking status (smoking, non-smoking and mixed smoking status groups). The discussion questions were preceded by information about passive and active smoking and its associated breast cancer risk. The study findings show young women's interest in this risk factor for breast cancer. Three themes were drawn from the analysis: making sense of the information on smoking and breast cancer, personal susceptibility and tobacco exposure and suggestions for increasing awareness about tobacco exposure and breast cancer. There was general consensus on framing public awareness messages about this risk factor on 'protecting others' from breast cancer to catch smokers' attention, providing young women with the facts and personal stories of breast cancer to help establish a personal connection with this information and overcome desensitization related to tobacco messages, and targeting all smokers who may place young women at risk. Cautions were also raised about the potential for stigmatization. Implications for raising awareness about this modifiable risk factor for breast cancer are 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".