Pap Screening and Knowledge of Risk Factors for Cervical Cancer in Chinese Women in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: Cervical cancer is a significant cause of mortality and morbidity for Chinese Canadian women, due in part to inadequate Pap screening. A community-based survey was conducted involving 528 Chinese immigrant women residing in British Columbia, Canada, in order to identify barriers and facilitators to Pap testing. This paper addresses the relationship between knowledge level about cervical cancer risk factors and Pap screening practices. DESIGN: Female trilingual Chinese interviewers conducted personal at-home interviews about knowledge of cervical cancer risk factors, history of Pap screening, sociodemographic and acculturation factors. RESULTS: The average summary score for knowledge about cervical cancer risk factors was 5.2/10, and knowledge level was significantly associated with the woman's educational level and the gender of the doctor providing usual care. Seventy-four per cent of respondents reported ever having received a Pap test, and 56% reported having received a test within the last two years. Respondents with the highest knowledge were more likely to have ever received a Pap test (OR 6.4, 95% CI: 2.6, 15.9), and to have recently received a test (OR 3.1, 95% CI: 1.4, 6.7). CONCLUSION: The average knowledge level about cervical cancer risk factors is low in Chinese Canadian women, especially among those with less education and who receive their usual care from a male doctor. Knowledge of these risk factors influences Pap screening behaviour. Culturally and linguistically appropriate education interventions addressing Pap testing and risk factors for cervical cancer are needed in the Canadian Chinese community. Educational resources are also needed for their primary care givers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".