Beliefs about optimal age and screening frequency predict breast screening adherence in a prospective study of female relatives from the Ontario Site of the Breast Cancer Family Registry
Bibliographic record
Abstract
BACKGROUND: Although few studies have linked cognitive variables with adherence to mammography screening in women with family histories of breast and/or ovarian cancer, research studies suggest cognitive phenomena can be powerful adherence predictors. METHODS: This prospective study included 858 women aged 30 to 71 years from the Ontario site of the Breast Cancer Family Registry with at least one first-degree relative diagnosed with breast and/or ovarian cancer. Data on beliefs about breast cancer screening and use of mammography were obtained from annual telephone interviews spanning three consecutive years. Self-reported mammogram dates were confirmed with medical imaging reports. Associations between beliefs about breast cancer screening and adherence with annual mammography were estimated using polytomous logistic regression models corrected for familial correlation. Models compared adherers (N = 329) with late-screeners (N = 382) and never-screeners (N = 147). RESULTS: Women who believed mammography screening should occur annually were more likely to adhere to annual screening recommendations than women who believed it should happen less often (OR: 5.02; 95% CI: 2.97-8.49 for adherers versus late-screeners; OR: 6.82; 95% CI: 3.29-14.16 for adherers versus never-screeners). Women who believed mammography screening should start at or before age 50 (rather than after) (OR: 9.72; 95% CI: 3.26-29.02) were significantly more likely to adhere when compared with never-screeners. CONCLUSIONS: Study results suggest that women with a family history of breast cancer should be strongly communicated recommendations about initial age of screening and screening intervals as related beliefs significantly predict adequate adherence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".