Predisposing Factors Associated with Compliance to Biennial Breast Screening among Centers with and without Nurses
Bibliographic record
Abstract
BACKGROUND: Previous research suggests that predisposing factors such as previous screening experience, participation in preventive health behaviors, and knowledge/beliefs about breast cancer and screening influence a woman's decision to make a timely return for a second screen. METHODS: A stratified random sample of compliers and noncompliers to biennial screening were selected from a cohort of 51,242 women ages 50 to 65 years who had their initial screen at the Ontario Breast Screening Program. In total, 1,901 women were telephone-interviewed. The associations between predisposing factors and compliance were estimated separately for centers with and without nurses using logistic regression analyses adjusted for demographics and smoking status. RESULTS: Women screened at nurse centers were less likely to comply if they thought women should stop having mammograms before age 70 years [odds ratio (OR), 0.39; 95% confidence interval (95% CI), 0.19-0.79], did not consider mammograms very likely to find cancer (OR, 0.73; 95% CI, 0.56-0.95), felt their likeliness of getting breast cancer was below average (OR, 0.69; 95% CI, 0.54-0.89), or believed a high-fat diet was not an important risk factor for breast cancer (OR, 0.59; 95% CI, 0.36-0.97). Women attending nurse centers were significantly more likely to comply if they sometimes had thoughts or worries about developing breast cancer (OR, 1.40; 95% CI, 1.10-1.80). CONCLUSIONS: Nurses at screening centers may reinforce a woman's knowledge or beliefs about breast cancer or screening and as a result increase their compliance to biennial breast 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".