A social media approach to inform youth about breast cancer and smoking: An exploratory descriptive study
Bibliographic record
Abstract
Tobacco exposure during periods of breast development has been shown to increase risk of premenopausal breast cancer. An urgent need exists, therefore, to raise awareness among adolescent girls about this new evidence, and for adolescent girls and boys who smoke to understand how their smoking puts their female peers at risk for breast cancer. The purpose of this study was to develop two youth-informed, gender specific YouTube-style videos designed to raise awareness among adolescent girls and boys about tobacco exposure as a modifiable risk factor for breast cancer and to assess youths' responses to the videos and their potential for inclusion on social media platforms. Both videos consisted of a combination of moving text, novel images, animations, and youth-friendly music. A brief questionnaire was used to gather feedback on two videos using a convenience sample of 135 youth in British Columbia, Canada. The overall positive responses by girls and boys to their respective videos and their reported interest in sharing these videos via social networking suggests that this approach holds potential for other types of health promotion messaging targeting youth. The videos offer a promising messaging strategy for raising awareness about tobacco exposure as a modifiable risk factor for breast cancer. Tailored, gender-specific messages for use on social media hold the potential for cost-effective, health promotion and cancer prevention initiatives targeting youth.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".