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Record W7094222665

Informal caregiver burden among stroke patients in east-coast Peninsular Malaysia: a short-term longitudinal study

2023· dissertation· en· W7094222665 on OpenAlexaboutno aff

Bibliographic record

VenueUNDIP Institutional Repository (UNDIP-IR) (Diponegoro University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsStroke (engine)Caregiver burdenMalayActivities of daily livingLongitudinal studyPsychological interventionFamily caregiversStroke recovery
DOInot available

Abstract

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Background: Stroke is a leading cause of death and disability worldwide. Many stroke survivors require assistance for basic activities of daily living (ADL) and instrumental activities of daily living (IADLs). Stroke attacks happen suddenly, and family members must act as informal caregivers swiftly. Unfortunately, many caregivers feel upset or burdened during caring for stroke survivors. Studies on caregiver burden are vital in helping policymakers prioritise support and researchers develop interventions targeting stroke survivors and caregivers. This study aims to measure the burden among informal caregivers for stroke survivors in East Coast Peninsular Malaysia.
\nMethod: In this research, three related research articles were produced. First, a bibliometric analysis was done to measure the academic production and collaboration of the author, institutions, and countries. The publications with a title containing “stroke” and “caregiver” were searched using Clarivate’s Web of Science database. Second, a descriptive analysis was done to describe the distribution of stroke survivors, informal caregivers, and the burden of stroke caregivers. Stroke survivors and their caregivers were recruited from three East Coast Peninsular Malaysia hospitals. The caregiver burden was measured using the Malay version of Zarit Burden Interview (MZBI) and the Malay version of Caregiver Appraisal of Function and Upset (Malay-CAFU) via phone call four times within the first three months post-discharge. Third, using the same data, an inferential analysis was done using a linear mixed effect model to estimate the stroke caregiver burden trends and the effect of stroke survivors’ dependency level on the burden trajectory.
\nResult: In the bibliometric analysis, it was found that 678 publications dated from 1989 to 2022 with titles containing the terms “stroke” and “caregiver”. The publications were primarily published in the English language. The publications mainly were produced in the USA (28.6%), by The University of Toronto (9.5%), in ‘Topics in Stroke Rehabilitation’ journal (5.8%), and the most productive author was Tamilyn Bakas (3.1%). For the caregiver burden, 85 stroke survivors and 155 informal caregivers were recruited. On average, the stroke survivors had two caregivers, mainly female (58.1%). In the first three months, the burden was reduced, with the mean (SD) of MZBI reduced from 27.42 (12.73) in the first week to 17.77 (11.20) in the third month, while IADL Malay-CAFU Upset reduced from 1.14 (0.94) to 0.62 (0.64) and ADL Malay-CAFU from 1.36 (1.00) to 0.78 (0.65) in the same period. When accounted for the clustering effect using a linear mixed effect model, the MZBI shows a reduction from 1-week post-discharge to 3-month [beta = -10.76 (95% CI = -11.94, -9.57)], and Malay-CAFU at 3-month [beta = -0.68 (95% CI = -0.80, -0.57)]. The burden was higher among caregivers with dependent stroke survivors; however, the rate of reduction of burden was not significantly different.
\nConclusion: The studies on stroke caregivers were extensive; however, ongoing studies on the field are essential. Areas of interest in the field include the experience of stroke caregivers, the level and the determinant of burden and the interventions in managing the burden. Many stroke survivors were taken care of by several informal caregivers, especially family members. However, the caregivers may feel burdened while giving care; however, the burden is usually reduced in the first three months post-stroke. Therefore, policymakers and healthcare providers should initiate support and interventions for caregivers and stroke survivors as early as at the time of diagnosis.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.239
Teacher spread0.224 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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