Empowering cancer survivors to meet their physical and psychosocial needs: An implementation evaluation
Bibliographic record
Abstract
Our Wellness Beyond Cancer Survivorship Program was established and evaluated as a quality improvement project. Individualized survivorship care plans for survivors and primary care providers included cancer surveillance recommendations and survivors' self-reported physical and psychosocial needs. At the discharge visit, an oncology nurse reviewed the care plan and symptom management strategies with survivors. We assessed the physical and psychosocial needs and feelings of empowerment of 70 breast and 53 colorectal cancer survivors on entry into the program and one year after discharge to primary care. Survivors were months to 10 or more years since the end of active treatment, with colorectal cancer survivors referred sooner (average 1.2 years). At baseline, colorectal cancer survivors reported little concern about their needs (scores <1.0 out of 5.0) and breast cancer survivors reported some concern about sleep disturbances, weight changes, memory/concentration changes, and fear of recurrence (scores 1.0 to 1.5 out of 5.0). All survivors reported feeling empowered (>3.0 out of 4.0). Needs and empowerment levels were mostly unchanged one year later. Colorectal cancer survivors showed a statistically significantly increased fear of recurrence at one year. In summary, cancer survivors with a survivorship care plan continued to feel empowered one year following discharge.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".