Improving Cancer Awareness among Asian Americans Using Targeted and Culturally Appropriate Media: A Case Study
Bibliographic record
Abstract
Reaching Asian Americans with cancer awareness messages is critical to improving cancer detection and reducing risk. Two separate targeted media campaigns, sponsored by the Asian Tobacco Education, Cancer Awareness, and Research (ATECAR), were implemented to increase cancer awareness among Chinese, Koreans, Vietnamese, and Cambodians residing in Philadelphia and the surrounding counties. These campaigns, based on Rogers’s diffusion and innovation theoretical model, were culturally sensitive, multilingual, and implemented over an extended time frame using print articles and a radio series under the respective general headings ATECAR Link and Voice of ATECAR. The series covered a range of topics that included tobacco smoking and health, cervical and breast cancer, clinical trials, and cancer information. Despite a reputation for noninvolvement in mainstream cancerrelated media issues, results of the campaigns reflected an exceptional response from the targeted communities. The results suggest that wellplanned, community-based media campaigns can have positive impacts on cancer awareness in Asian communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".