Primary care providers’ experience and satisfaction with personalised breast cancer screening risk communication: a descriptive cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To describe primary care providers' (PCPs) experience and satisfaction with receiving risk communication documents on their patient's breast cancer (BC) risk assessment and proposed screening action plan. DESIGN: Descriptive cross-sectional study. SETTING: A survey was distributed to all 763 PCPs linked to 1642 women participating in the Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation (PERSPECTIVE I&I) research project in Quebec, approximately 1-4 months after the delivery of the risk communication documents. The recruitment phase took place from July 2021 to July 2022. PARTICIPANTS: PCPs. MAIN OUTCOME MEASURES: Descriptive analyses were conducted to report participants' experiences and satisfaction with receiving risk communication. Responses to two open-ended questions were subjected to content analysis. RESULTS: A total of 168 PCPs answered the survey, from which 73% reported being women and 74% having more than 15 years of practice. Only 38% were familiar with the risk-based BC screening approach prior to receiving their patient risk category. A majority (86%) agreed with the screening approach and would recommend it to their patients if implemented at the population level. A majority of PCPs also reported understanding the information provided (92%) and expressed agreement with the proposed BC screening action plan (89%). Some PCPs recommended simplifying the materials, acknowledging the potential increase in workload and emphasising the need for careful planning of professional training efforts. CONCLUSION: PCPs expressed positive attitudes towards a risk-based BC screening approach and were generally satisfied with the information provided. This study suggests that, if introduced in Canada in a manner similar to the PERSPECTIVE I&I project, risk-based BC screening would likely be supported by most PCPs. However, they emphasised the importance of addressing concerns such as professional training and the potential impact on workload if the approach were to be implemented at the population level. Future qualitative studies are needed to further explore the training needs of PCPs and to develop strategies for integrating this approach with the high workloads faced by PCPs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".