Socioeconomic Influences on Vietnamese-Canadian Women's Breast and Cervical Cancer Prevention Practices: A Social Determinant's Perspective
Bibliographic record
Abstract
Breast cancer and cervical cancer are major contributors to morbidity and mortality for the Vietnamese Canadian women. Vietnamese women face multiple barriers to obtaining effective preventive care and treatment for these diseases. This paper reports the influence of socioeconomic factors on Vietnamese Canadian women's breast and cervical cancer screening behaviors. In-depth semistructured interviews were conducted with Vietnamese Canadian women and health care providers. The study revealed that low socioeconomic status is a major barrier to women's participation in breast and cervical cancer screening, despite the fact that health care in Canada is funded publicly by the Medicare system. The Vietnamese Canadian women and health care providers in the present study identified a number of major dimensions through which socioeconomic issues were associated with Vietnamese Canadian women's access to and use of health care for the prevention of breast and cervical cancer, including (a) financial concerns; (b) language, occupational opportunities, and downward mobility; (c) economics and women's households; and (d) low socioeconomic status and screening behaviors. Implications are discussed for increasing Vietnamese Canadian women's utilization of breast and cervical cancer screening services.
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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 | 0.001 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".