Canadian Healthcare Professionals’ Views and Attitudes toward Risk-Stratified Breast Cancer Screening
Bibliographic record
Abstract
Given the controversy over the effectiveness of age-based breast cancer (BC) screening, offering risk-stratified screening to women may be a way to improve patient outcomes with detection of earlier-stage disease. While this approach seems promising, its integration requires the buy-in of many stakeholders. In this cross-sectional study, we surveyed Canadian healthcare professionals about their views and attitudes toward a risk-stratified BC screening approach. An anonymous online questionnaire was disseminated through Canadian healthcare professional associations between November 2020 and May 2021. Information collected included attitudes toward BC screening recommendations based on individual risk, comfort and perceived readiness related to the possible implementation of this approach. Close to 90% of the 593 respondents agreed with increased frequency and earlier initiation of BC screening for women at high risk. However, only 9% agreed with the idea of not offering BC screening to women at very low risk. Respondents indicated that primary care physicians and nurse practitioners should play a leading role in the risk-stratified BC screening approach. This survey identifies health services and policy enhancements that would be needed to support future implementation of a risk-stratified BC screening approach in healthcare systems in Canada and other countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".