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Record W4405442294 · doi:10.3390/socsci13120676

Korean American Immigrant Women’s Mammography Use in Korea: Factors Associated with Medical Tourism

2024· article· en· W4405442294 on OpenAlexaboutno aff
Mi Hwa Lee, Joseph R. Merighi, Leslie E. Cofie, Bryan L. Rogers

Bibliographic record

VenueSocial Sciences · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
FundersAmerican Cancer Society
KeywordsImmigrationMedicineMammographyBreast cancerQuarter (Canadian coin)Logistic regressionDemographyHealth careFamily medicineAsian americansGerontologyEnvironmental healthCancerEthnic groupEconomic growthGeographyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.424
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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