Communication with Breast Cancer Survivors
Bibliographic record
Abstract
Breast cancer survivors must manage chronic side effects of original treatment. To manage these symptoms, communication must include both biomedical and contextual lifestyle factors. Sixty breast cancer survivors and 6 providers were recruited to test a conceptual model developed from uncertainty in illness theory and the dimensions of a patient-centered relationship. Visits were audio-taped, then coded using the Measure of Patient-Centered Communication (Brown, Stewart, & Ryan, 2001 Brown, J., Stewart, M. and Ryan, B. 2001. Assessing communication between patients and physicians: The measure of patient-centered communication (MPCC), London, Ontario, , Canada: Thames Valley Family Practice Research Unit and Centre for Studies in Family Medicine. [Google Scholar]). Consultations were found to be 52% patient-centered. Chi-square Automatic Interaction Detection (CHAID) analysis showed that survivor self-reported fatigue level and conversation about symptoms were associated with survivor uncertainty, mood state, and survivor perception of patient-centered communication. Survivors may want to discuss persistent symptom concerns with providers, due to concerns about recurrence, and discuss lifestyle contextual concerns with others.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".