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Record W2137913869 · doi:10.5539/ass.v8n9p23

Religious Coping as Mediator between Illness Perception and Health-Related Quality of Life among Chronic Kidney Disease Patients

2012· article· en· W2137913869 on OpenAlexvenueno aff
Norhayati Ibrahim, Asmawati Desa

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsCoping (psychology)Clinical psychologyPsychologyPerceptionDiseaseTimelineMedicineInternal medicine

Abstract

fetched live from OpenAlex

End-stage renal disease (ESRD) a raising global pandemic known to cause psychological dysfunction but not well studied. The purpose of this study was to measure the influence of illness perception and religious coping strategies on patients’ health-related quality of life (HRQoL) and to identify direct or indirect predictors of religious coping on illness perception and HRQoL of ESRD patients. This study involved 274 patients with ESRD who were on chronic maintenance dialysis. Test instruments used included Revised Illness Perception Questionnaire (IPQ-R) to measure patients’ perception towards the illness, Religious Coping Strategies questionnaire (RCS) to determine patients' nature of religious coping and Short-Form 36 (SF-36) questionnaire to measure their HRQoL. Results showed that almost all components of illness perception and religious coping strategies were significantly correlated with HRQoL in both aspects of physical component summary (PCS) and mental component summary (MCS). In addition, the findings showed religious coping as a mediator between several illness perception components namely timeline, illness coherence, personal control, consequences and cyclical with PCS and MCS. Attention should be given especially to illness perception and positive religious coping variables in any intervention program to improve the HRQoL of ESRD patients.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.383
Teacher spread0.343 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2012
Admission routes1
Has abstractyes

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