Views of Family Physicians about Survivorship Care Plans to Provide Breast Cancer Follow-Up Care: Exploration of Results from a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The U.S. Institute of Medicine recommends that cancer patients receive survivorship care plans, but evaluations to date have found little evidence of the effectiveness of such plans. We conducted a qualitative follow-on study to a randomized controlled trial (rct) to understand the experiences of family physicians using survivorship care plans to support the follow-up of breast cancer patients. METHODS: A subset of family physicians whose patients were enrolled in the parent rct in Ontario and Nova Scotia were eligible for this study. In interviews, the physicians discussed survivorship care plans (intervention) or usual discharge letters (control), and their confidence in providing follow-up cancer care. RESULTS: Of 123 eligible family physicians, 18 (10 intervention, 8 control) were interviewed. In general, physicians receiving a survivorship care plan found only the 1-page care record to be useful. Physicians who received only a discharge letter had variable views about the letter's usefulness; several indicated that it lacked information about potential cancer- or treatment-related problems. Most physicians were comfortable providing care 3-5 years after diagnosis, but desired timely and informative communication with oncologists. CONCLUSIONS: Although family physicians did not find extensive survivorship care plans useful, discharge letters might not be sufficiently comprehensive for follow-up breast cancer care. Effective strategies for two-way communication between family physicians and oncologists are still lacking.
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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 | 0.077 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".