Influence of physician and patient characteristics on adherence to breast cancer screening recommendations
Bibliographic record
Abstract
Identifying physician and patient characteristics is important in implementing effective, targeted strategies to improve breast cancer detection rates through increased screening recommendations and uptake. The purpose of this study was to determine whether Ontario physicians recommend breast screening using mammography every 2 years for women aged 50-69 as encouraged by the Ontario Breast Screening Program. This study also aimed to identify physician and patient characteristics that may influence adherence to these recommendations. The study design was a cross-sectional study. Using the Canadian Medical Directory-Ontario database, 3063 questionnaires were mailed to all active general and family practitioners. A response rate of 38% (N = 939) was achieved. Adherence to screening was defined as recommending screening to women aged 50-69 only, every 2 years as outlined by the Ontario Breast Screening Program. Bivariate analyses and unconditional logistic regression were used to assess physician adherence to screening guidelines. Only 38.9% of physicians followed recommended breast screening guidelines. After adjusting for physician sex and age, predictors of screening adherence include physicians working in academic or research centers (odds ratio 8.3, 95% confidence interval 1.7-39.7) and those reporting that over 31% of their patients to be of low-income (odds ratio 1.6, 95% confidence interval 1.1-2.4). Compared with physicians working in a rural/town setting (<10 000 people), those located in a large city (>100 000 people) were less likely to adhere to screening guidelines (odds ratio 0.5, 95% confidence interval 0.3-0.7). A low proportion of Ontario physicians adhere to recommended breast screening guidelines. Future research into effective strategies to increase adherence should take into account practice location, setting and patient characteristics.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".