What Do Primary Care Providers Think About Implementing Breast Cancer Survivorship Care?
Bibliographic record
Abstract
Purpose: As cancer centres move forward with earlier discharge of stable survivors of early-stage breast cancer (bca) to primary care follow-up, it is important to address known knowledge and practice gaps among primary care providers (pcps). In the present qualitative descriptive study, we examined the practice context that influences implementation of existing clinical practice guidelines for providing such care. The purpose was to determine the challenges, strengths, and opportunities related to implementing comprehensive evidence-based bca survivorship care guidelines by pcps in southeastern Ontario. Methods: Semi-structured interviews were conducted with 19 pcps: 10 physicians and 9 nurse practitioners. Results: Thematic analysis revealed 6 themes within the broad categories of knowledge, attitudes, and resources. Participants highlighted 3 major challenges related to providing bca survivorship care: inconsistent educational preparation, provider anxieties, and primary care burden. They also described 3 major strengths or opportunities to facilitate implementation of survivorship care guidelines: tools and technology, empowering survivors, and optimizing nursing roles. Conclusions: We identified several important challenges to implementation of comprehensive evidence-based survivorship care for bca survivors, as well as several strengths and opportunities that could be built upon to address those challenges. Findings from our research could inform targeted knowledge translation interventions to provide support and education for pcps and bca survivors.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".