Personalizing Post-Treatment Cancer Care: A Cross-Sectional Survey of the Needs and Preferences of Well Survivors of Breast Cancer
Bibliographic record
Abstract
Background: Improved treatments resulting in a rising number of survivors of breast cancer (bca) calls for optimization of current specialist-based follow-up care. In the present study, we evaluated well survivors of bca with respect to their supportive care needs and attitudes toward follow-up with various care providers, in varying settings, or mediated by technology (for example, videoconference or e-mail). Methods: A cross-sectional paper survey of well survivors of early-stage pT1–2N0 bca undergoing posttreatment follow-up was completed. Descriptive and univariable logistic regression analyses were performed to examine associations between survivor characteristics, supportive care needs, and perceived satisfaction with follow-up options. Qualitative responses were analyzed using conventional content analysis. Results: The 190 well survivors of bca who participated (79% response rate) had an average age of 63 ± 10 years. Median time since first follow-up was 21 months. Most had high perceived satisfaction with in-person specialist care (96%, 177 of 185). The second most accepted model was shared care involving specialist and primary care provider follow-up (54%, 102 of 190). Other models received less than 50% perceived satisfaction. Factors associated with higher perceived satisfaction with non-specialist care or virtual follow-up by a specialist included less formal education (p < 0.01) and more met supportive care needs (p < 0.05). Concerns with virtual follow-up included the perceived impersonal nature of virtual care, potential for inadequate care, and confidentiality. Conclusions: Well survivors of bca want specialists involved in their follow-up care. Compared with virtual followup, in-person follow-up is perceived as more reassuring. Certain survivor characteristics (for example, met supportive care needs) might signal survivor readiness for virtual or non-specialist follow-up. Future work should examine multi-stakeholder perspectives about barriers to and facilitators of shared multimodal follow-up care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".