Interventions to improve outcomes for caregivers of patients with advanced cancer: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Family caregivers of patients with advanced cancer often have poor quality of life (QOL) and mental health. We examined the effectiveness of interventions offering support for caregivers of patients with advanced cancer on caregiver QOL and mental health outcomes. METHODS: We searched Ovid MEDLINE, EMBASE, Cochrane CENTRAL, and Cumulative Index to Nursing and Allied Health Literature databases from inception through June 2021. Eligible studies reported on randomized controlled trials for adult caregivers of adult patients with advanced cancer. Meta-analysis was conducted for primary outcomes of QOL, physical well-being, mental well-being, anxiety, and depression, from baseline to follow-up of 1-3 months; secondary endpoints were these outcomes at 4-6 months and additional caregiver burden, self-efficacy, family functioning, and bereavement outcomes. Random effects models were used to generate summary standardized mean differences (SMD). RESULTS: Of 12 193 references identified, 56 articles reporting on 49 trials involving 8554 caregivers were eligible for analysis; 16 (33%) targeted caregivers, 19 (39%) patient-caregiver dyads, and 14 (29%) patients and their families. At 1- to 3-month follow-up, interventions had a statistically significant effect on overall QOL (SMD = 0.24, 95% confidence interval [CI] = 0.10 to 0.39); I2 = 52.0%), mental well-being (SMD = 0.14, 95% CI = 0.02 to 0.25; I2 = 0.0%), anxiety (SMD = 0.27, 95% CI = 0.06 to 0.49; I2 = 74.0%), and depression (SMD = 0.34, 95% CI = 0.16 to 0.52; I2 = 64.4) compared with standard care. In narrative synthesis, interventions demonstrated improvements in caregiver self-efficacy and grief. CONCLUSIONS: Interventions targeting caregivers, dyads, or patients and families led to improvements in caregiver QOL and mental health. These data support the routine provision of interventions to improve well-being in caregivers of patients with advanced cancer.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.048 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".