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Record W4379598345 · doi:10.1093/jnci/djad075

Interventions to improve outcomes for caregivers of patients with advanced cancer: a meta-analysis

2023· review· en· W4379598345 on OpenAlexafffund
Ronald Chow, Jean Mathews, Emily YiQin Cheng, Samantha Lo, Joanne Wong, Sorayya Alam, Breffni Hannon, Gary Rodin, Rinat Nissim, Sarah Hales, Dio Kavalieratos, Kieran L. Quinn, George Tomlinson, Camilla Zimmermann

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2023
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSinai Health SystemPrincess Margaret Cancer CentrePublic Health OntarioUniversity Health NetworkUniversity of TorontoQueen's University
FundersCanadian Institutes of Health ResearchUniversity of TorontoPrincess Margaret Cancer FoundationUniversity Health Network
KeywordsMedicineMeta-analysisAnxietyPsychological interventionQuality of life (healthcare)Mental healthRandomized controlled trialDepression (economics)Confidence intervalMEDLINEPhysical therapyPsychiatryInternal medicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.032
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.048
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.205
GPT teacher head0.458
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations83
Published2023
Admission routes2
Has abstractyes

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