Patient Satisfaction With Breast Cancer Follow-Up Care Provided By Family Physicians
Bibliographic record
Abstract
PURPOSE: There is little evidence to document patient satisfaction with follow-up care provided by family physicians (FPs)/general practitioners (GPs) to breast cancer patients. We aimed to identify determinants of satisfaction with such care in low-income, medically underserved women with breast cancer. METHODS: This was a cross-sectional study of 145 women who reported receiving follow-up care from an FP/GP. Women were enrolled in California's Breast and Cervical Cancer Treatment Program and were interviewed by phone 3 years after their breast cancer diagnosis. Cleary and McNeil's model, which states that patient satisfaction is a function of patient characteristics, structure of care, and processes of care, was used to understand the determinants of satisfaction. Stepwise logistic regression was used to identify significant predictors. RESULTS: Of the patients interviewed, 73.4% reported that they were extremely satisfied with their treatment by the FP/GP. Women who were able to ask their family physicians questions about their breast cancer had six times greater odds of being extremely satisfied compared with women who were not able to ask any questions. Women who scored the FP higher on the ability to explain things in a way she could understand had higher odds of being extremely satisfied compared with women who scored their family physicians lower. CONCLUSIONS: FPs/GPs providing follow-up care for breast cancer patients should encourage patients to ask questions and must communicate in a way that patients understand. These recommendations are congruent with the characteristics of patient-centered communication for cancer patients enunciated in a recent National Cancer Institute monograph.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".