Self‐management interventions for cancer survivors: A systematic review and evaluation of intervention content and theories
Bibliographic record
Abstract
OBJECTIVE: Self-management has been proposed as a strategy to help cancer patients optimize their health and well-being during survivorship. Previous reviews have shown variable effects of self-management on outcomes. The theoretical basis and psychoeducational components of these interventions have not been evaluated in detail. We aimed to evaluate the evidence for self-management and provide a description of the components of these interventions. METHODS: We conducted a systematic review of self-management interventions for adults who had completed primary cancer treatment by searching MEDLINE, EMBASE, PsychINFO, CINAHL, Scopus, Cochrane Database of Systematic Reviews, National Institutes of Health Clinical Trials Registry, and Cochrane CENTRAL Registry of Controlled Trials. We included experimental and quasiexperimental designs. Data synthesis included narrative and tabular summary of results; heterogeneity of interventions and outcomes precluded meta-analysis. Study quality was evaluated using the Cochrane risk of bias tool or the risk of bias of nonrandomized studies tool. RESULTS: Forty-one studies published between 1994 and 29 March 2018 were included. Studies were predominantly randomized controlled trials and targeted to breast cancer survivors. A variety of intervention designs, psychoeducational components, and outcomes were identified. Less than 50% of the studies included a theoretical framework. There was variability of effects across most outcomes. Risk of bias could not be fully assessed. CONCLUSIONS: There are limitations in the design and research on self-management interventions for cancer survivors that hinder their translation into clinical practice. Further research is needed to understand if these interventions are an important type of support for cancer survivors.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".