Barriers and Facilitators to Delivering Multifactorial Risk Assessment and Communication for Personalized Breast Cancer Screening: A Qualitative Study Exploring Implementation in Canada
Bibliographic record
Abstract
Many jurisdictions are considering a shift to risk-stratified breast cancer screening; however, evidence on the feasibility of implementing it on a population scale is needed. We conducted a prospective cohort study in the PERSPECTIVE I&I project to produce evidence on risk-stratified breast screening and recruited 3753 participants to undergo multifactorial risk assessment from 2019-2021. This qualitative study explored the perspectives of study personnel on barriers and facilitators to delivering multifactorial risk assessment and risk communication. One focus group and three one-on-one interviews were conducted and a thematic analysis conducted which identified five themes: (1) barriers and facilitators to recruitment for multifactorial risk assessment, (2) barriers and facilitators to completion of the risk factor questionnaire, (3) additional resources required to implement multifactorial risk assessment, (4) the need for a person-centered approach, and (5) and risk literacy. While risk assessment and communication processes were successful overall, key barriers were identified including challenges with collecting comprehensive breast cancer risk factor information and limited resources to execute data collection and risk communication activities on a large scale. Risk assessment and communication processes will need to be optimized for large-scale implementation to ensure they are efficient but robust and person-centered.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".