Supported self-management for cancer survivors to address long-term biopsychosocial consequences of cancer and treatment to optimize living well
Bibliographic record
Abstract
PURPOSE OF REVIEW: As individuals are living longer with cancer as a chronic disease, they face new health challenges that require the application of self-management behaviors and skills that may not be in their usual repertoire of self-regulatory health behaviors. Increasing attention is focused on supported self-management (SSM) programs to enable survivors in managing the long-term biopsychosocial consequences and health challenges of survivorship. This review explores current directions and evidence for SSM programs that enable survivors to manage these consequences and optimize health. RECENT FINDINGS: Cancer survivors face complex health challenges that affect daily functioning and well being. Multiple systematic reviews show that SSM programs have positive effects on health outcomes in typical chronic diseases. However, the efficacy of these approaches in cancer survivors are in their infancy; and the 'one-size' fits all approach for chronic disease self-management may not be adequate for cancer as a complex chronic illness. This review suggests that SSM has promising potential for improving health and well being of cancer survivors, but there is a need for standardizing SSM for future research. SUMMARY: Although there is increasing enthusiasm for SSM programs tailored to cancer survivors, there is a need for further research of their efficacy on long-term health outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".