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Record W2171849755 · doi:10.1111/hdi.12318

Burden and coping strategies among <scp>J</scp>ordanian caregivers of patients undergoing hemodialysis

2015· article· en· W2171849755 on OpenAlexvenueno aff
Eman Alnazly

Bibliographic record

VenueHemodialysis International · 2015
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisPsychosocialMedicineCoping (psychology)Caregiver burdenPsychological interventionSocial supportFamily caregiversNursing Interventions ClassificationDescriptive statisticsClinical psychologyGerontologyPsychiatryDiseaseInternal medicinePsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Recent studies reported hemodialysis patients' sufferings from physical and psychosocial issues, but few studies reported family-caregiver burdens. This study aims to explore the burdens and coping strategies of caregivers of patients receiving hemodialysis. Caregivers of patients undergoing hemodialysis (n = 139) at 3 dialysis units were given 3 forms: Caregiver and Patient Characteristics, Oberst Caregiving Burden Scale Difficulty Subscale, and Ways of Coping Questionnaire. Descriptive statistics, correlational analysis, and multiple regression analysis were performed. The Oberst Caregiving Burden Scale was significantly related to self-controlling (r = 0.20) and seeking social support (r = 0.17). Caregiver burden was positively and significantly correlated with self-controlling coping subscale, with t = 1.10, P = 0.05, and β = 0.25. Living with the patient was the only variable that was a significant predictor of burden, with t = 2.96, P = 0.00, and β = 0.331. Living with patients predicted caregiver burden, and the burden scale correlated with self-controlling. The findings contribute to the evidence on the adverse health effects of caregivers of patients receiving hemodialysis. This study suggests that nursing interventions should target caregiver knowledge for better coping.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.252
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations66
Published2015
Admission routes1
Has abstractyes

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