Korean American Immigrant Women’s Mammography Use in Korea: Factors Associated with Medical Tourism
Bibliographic record
Abstract
This study examined social determinants associated with Korean American immigrant women’s mammography use in Korea after immigration to the United States. Data from a cross-sectional survey were obtained from 187 women in Los Angeles County, California. More than one-quarter (28.3%) of the respondents reported returning to Korea for a mammogram after immigrating to the United States. Multivariable logistic regression revealed that compared to those who had their first mammogram in Korea, Korean American immigrant women who had their first mammogram in the United States were less likely to return to Korea for subsequent screenings (AOR = 0.02, 95% CI: <0.001, 0.05); also, those who had employer-based health insurance in the United States were less likely to get a mammogram in Korea after immigration (AOR = 0.01, 95% CI: <0.01, 0.18). Findings suggest that women familiar with the Korean healthcare system and who are uninsured or have inconsistent healthcare coverage in the United States may seek care in Korea. To promote adherence to breast cancer screening guidelines among Korean American immigrant women residing in the United States, greater access to free or low-cost screening services and breast cancer screening education is warranted to reduce the risk of later stage breast cancer detection resulting from medical tourism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".