Qualitative Evaluation of Care Plans for Canadian Breast and Head-and-Neck Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Survivorship care plans (scps) have been recommended as a way to ease the transition from active cancer treatment to follow-up care, to reduce uncertainty for survivors in the management of their ongoing health, and to improve continuity of care. The objective of the demonstration project reported here was to assess the value of scps for cancer survivors in western Canada. METHODS: The Alberta CancerBridges team developed, implemented, and evaluated scps for 36 breast and 21 head-and-neck cancer survivors. For the evaluation, we interviewed 12 of the survivors, 9 nurses who delivered the scps, and 3 family physicians who received the scps (n = 24 in total). We asked about satisfaction, usefulness, emotional impact, and communication value. We collected written feedback from the three groups about positive aspects of the scps and possible improvements (n = 85). We analyzed the combined data using qualitative thematic analysis. RESULTS: Survivors, nurses, and family physicians agreed that scps could ease the transition to survivorship partly by enhancing communication between survivors and care providers. Survivors appreciated the individualized attention and the comprehensiveness of the plans. They described positive emotional impacts, but wanted a way to ensure that their physicians received the scps. Nurses and physicians responded positively, but expressed concern about the time required to implement the plans. Suggestions for streamlining the process included providing survivors with scp templates in advance, auto-populating the templates for the nurses, and creating summary pages for physicians. CONCLUSIONS: The results suggest ways in which scps could help to improve the transition to cancer survivorship and provide starting points for larger feasibility studies.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 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".