The effectiveness of psychoeducational interventions on caregiver‐oriented outcomes in caregivers of adult cancer patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Cancer caregiving can result in increased psychosocial distress and poor health-related quality of life (QOL). Psychoeducation has been shown to be effective in enhancing caregiving-oriented outcomes. A systematic assessment of the overall effect of psychoeducational intervention (PEI) and identification of individual intervention characteristics that may contribute to the effectiveness of PEI is needed. METHODS: For this meta-analysis, relevant articles were identified through electronic databases using key search terms and their medical subject heading such as "family caregiver," "cancer," and "psychoeducational intervention." RESULTS: Twenty-eight controlled trials with 3876 participants were included. PEIs had beneficial effect on depression (Standardized Mean Difference [SMD] -0.26; 95% CI = -0.50 to -0.01, p < 0.04), anxiety (SMD -0.41; 95% CI = -0.82 to 0.01, p < 0.05), caregiver burden (SMD -0.84; 95% CI = -1.22 to -0.46, p < 0.0001) and QOL (SMD 0.59, 95% CI 0.24-0.93; p < 0.0009) at the immediate post-intervention period. At longer-term follow-up, the effectiveness of PEI was maintained on QOL (SMD 0.39, 95% CI = -0.00 to -0.77, p < 0.05), and anxiety (SMD -0.57; 95% CI = -1.09 to -0.06, p < 0.03). Moderation analysis showed that intervention characteristics such as studies conducted in high-income countries, group intervention and studies that focused on specific and mixed cancers explain some of the high variations observed among the included studies. CONCLUSIONS: PEI may benefit caregivers of cancer patients through the significant effects on caregiver burden, QOL, anxiety, and depression. The findings from the moderation analysis may be important for the design of future interventions.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".