295 DO WOMEN FOLLOWING TRADITIONAL CHINESE POSTPARTUM PRACTICES OBTAIN LESS CERVICAL CANCER SCREENING IN NORTH AMERICA?
Bibliographic record
Abstract
Introduction Asian Americans make up a sizable minority in the US, with Chinese currently making up the largest subgroup. However, despite this fact, the majority of Chinese women in North America underutilize Pap testing and carry a disproportionately higher rate of invasive cervical cancer. It has been suggested that certain cultural beliefs and practices can interfere with Western health care utilization among newly arrived immigrant communities. Methods Fifteen hundred thirty-two women ages 20 to 69 from Seattle, Washington and Vancouver, BC were interviewed in person in 1999, and data from 993 participants were analyzed. Results Women who observed postpartum rituals or who believed that certain aspects of their rituals help prevent them from obtaining cervical cancer did not show an underutilization of Pap testing. However, other factors, such as having a car in the household, speaking English, or being married, were highly predictive of Chinese women9s Pap test utilizations. Discussion Based on these findings, postpartum practices do not seem to negatively influence Pap test utilization among Chinese women in North America. Instead, clinicians serving Chinese in North America should focus more on economic, language, and acculturation as potential barriers for their patients9 health care utilization.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".