Efficacy of Self-management Programs in Managing Side Effects of Breast Cancer
Bibliographic record
Abstract
Background: Self-management programs are interventions that support patient empowerment of independent health behaviors. As the number of individuals surviving cancer is increasing, these programs are becoming more popular in clinical settings. However, there is currently no systematic review that assesses the effectiveness of self-management programs, specifically for individuals with breast cancer. Purpose: To determine the efficacy of self-management programs in decreasing treatment-related side effects experienced by breast cancer survivors at any point after their cancer diagnosis. Methods: An electronic literature search was conducted within the CENTRAL (Cochrane Central Register for Controlled Trials), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Ovid EMBASE, and MEDLINE online databases. Randomized controlled trials that included women who had a primary diagnosis of breast cancer, who had partaken in a self-management program, and whose outcomes were compared with women not participating in such programs were included in this review. Two reviewers independently screened and selected studies to be included and assessed risk of bias. A GRADE (Grading of Recommendations, Assessment Development and Evaluation) analysis was performed. Standard mean difference (SMD) and standard deviations were used to present the results of a meta-analysis of key outcomes. Results: Nine trials were included in this review. Self-management interventions were found to significantly improve health-related quality of life, SMD (95% confidence interval [CI]) = 0.49 (0.16-0.82), P = .004; coping ability, SMD (95% CI) = 0.19 (0.03-0.34), P = .02; and fatigue, SMD (95% CI) = −0.94 (−1.69 to −0.18), P = .01. Conclusions: Self-management programs were found to be effective in improving the health-related quality of life, coping abilities, and fatigue in individuals who have experienced treatment side effects of breast cancer. However, studies included in this review had poor methodological quality. Therefore, the results of this review should be viewed with caution.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".