Cost effectiveness of cervical cancer screening among Chinese women in North America.
Bibliographic record
Abstract
BACKGROUND: Chinese North American women have high invasive cervical cancer rates and low screening rates. The cost-effectiveness of strategies to improve Pap testing rates for Chinese women living in Seattle, Washington and Vancouver, British Columbia was examined. OBJECTIVES: To calculate the costs and cost-effectiveness of implementing two strategies to motivate women to obtain a Pap smear. RESEARCH DESIGN: A three-armed randomized, controlled trial was conducted. Women in each of two interventions (high-intensity outreach and low-intensity mailing intervention) were compared to a group of women who received usual care. MEASURES: Costs were captured via a group discussion of costs, accounting records, sampling of staff time logs, and estimation of costs and task times. Effectiveness was measured as the proportion of women in each intervention arm who reported receiving a Pap smear since the trial began. Cost-effectiveness was calculated as the incremental cost of screening each additional woman between an intervention arm and the control arm. RESULTS: A greater percentage of women who received the outreach intervention had a Pap test than women who received mailed materials or women who were in the usual care arm. The intent-to-treat cost for each additional woman to be screened for a Pap test was $415 in the Outreach arm and $676 for the Direct Mailing arm. The outreach worker intervention, though more expensive overall, was more cost-effective than the mailing intervention. CONCLUSIONS: Outreach intervention is cost-effective for sponsors and should be considered as a strategy to motivate Chinese women living in North America to seek cervical cancer screening.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".