Breast Cancer Knowledge, Attitudes, and Early Detection Practices in United States-Mexico Border Latinas
Bibliographic record
Abstract
INTRODUCTION: Evidence suggests Latinas residing along the United States-Mexico border face higher breast cancer mortality rates compared to Latinas in the interior of either country. The purpose of this study was to investigate breast cancer knowledge, attitudes, and use of breast cancer preventive screening among U.S. Latina and Mexican women residing along the U.S.-Mexico border. METHODS: For this binational cross-sectional study, 265 participants completed an interviewer-administered questionnaire that obtained information on sociodemographic characteristics, knowledge, attitudes, family history, and screening practices. Differences between Mexican (n=128) and U.S. Latina (n=137) participants were assessed by Pearson's chi-square, Fischer's exact test, t tests, and multivariate regression analyses. RESULTS: U.S. Latinas had significantly increased odds of having ever received a mammogram/breast ultrasound (adjusted odds ratio [OR]=2.95) and clinical breast examination (OR=2.67) compared to Mexican participants. A significantly greater proportion of Mexican women had high knowledge levels (54.8%) compared to U.S. Latinas (45.2%, p<0.05). Age, education, and insurance status were significantly associated with breast cancer screening use. CONCLUSIONS: Despite having higher levels of breast cancer knowledge than U.S. Latinas, Mexican women along the U.S.-Mexico border are not receiving the recommended breast cancer screening procedures. Although U.S. border Latinas had higher breast cancer screening levels than their Mexican counterparts, these levels are lower than those seen among the general U.S. Latina population. Our findings underscore the lack of access to breast cancer prevention screening services and emphasize the need to ensure that existing breast cancer screening programs are effective in reaching women along the U.S.-Mexico border.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".