Mammography screening among Chinese‐American women
Bibliographic record
Abstract
BACKGROUND: Breast carcinoma is the most common major malignancy among several Asian-American populations. This study surveyed mammography screening knowledge and practices among Chinese-American women. METHODS: In 1999, the authors conducted a cross-sectional, community-based survey in Seattle, Washington. Bilingual and bicultural interviewers administered surveys in Mandarin, Cantonese, or English at participants' homes. RESULTS: The survey cooperation rate (responses among reachable and eligible households) was 72% with 350 eligible women (age >or= 40 years with no prior history of breast carcinoma or double mastectomy). Seventy-four percent of women reported prior mammography screening, and 61% of women reported screening in the last 2 years. In multivariate analysis, a strong association was found between mammography screening and recommendations by physicians and nurses (prior screening: odds ratio [OR], 16.0; 95% confidence interval [95% CI], 7.8-35.0; recent screening: OR, 7.0; 95% CI, 3.8-13.6). This finding applied to both recent immigrants (< 15 years in the U.S.) and earlier immigrants (>or= 15 years in the U.S.). Thirty-two percent of women reported that the best way to detect breast carcinoma was a modality other than mammogram. CONCLUSIONS: The authors recommend a multifaceted approach to increase mammography screening by Chinese-American women: recommendations from the provider plus targeted education to address the effectiveness of screening mammography compared with breast self examination and clinical breast examination.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".