Influence of Nurses on Compliance with Breast Screening Recommendations in an Organized Breast Screening Program
Bibliographic record
Abstract
BACKGROUND: Evidence from breast screening trials has shown that a significant reduction in breast cancer mortality from screening can be achieved by regular attendance. Few studies have evaluated the influence of nurses on compliance with breast screening recommendations. METHODS: The cohort included 157,788 women ages 50 to 69 years who were screened at 1 of 9 regional cancer centers or 57 affiliated centers with nurses or 26 affiliated centers without nurses between January 1, 2002, and December 31, 2002, within the Ontario Breast Screening Program. These women were followed up prospectively for at least 30 months to compare compliance for annual and biennial screening recommendations among women who attended centers with and without nurses. The associations between type of screening center and the odds of compliance were modeled using mixed-effect logistic regression models. All P values are two-sided. RESULTS: Women attending a regional cancer center [odds ratios (OR), 1.96; 95% confidence interval (95% CI), 1.07-3.58] or affiliated center with nurses (OR, 1.75; 95% CI, 1.38-2.22) were significantly more likely to return within 18 months of their annual screening recommendation than women attending affiliated centers without nurses. In addition, women attending regional cancer centers (OR, 2.28; 95% CI, 1.34-3.89) or affiliated centers with nurses (OR, 2.30; 95% CI, 1.86-2.83) were significantly more likely to make a timely return within the recommended biennial screening interval of between 18 and 30 months. CONCLUSIONS: Breast screening programs should consider methods of integrating educational activities as provided by the nurses to improve compliance with 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 | 0.009 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".