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Record W2113550935

Anxiety And Depressive Symptoms And Health-Related Quality Of Life Status Among Patients With Cancer In Terengganu, Malaysia

2011· article· en· W2113550935 on OpenAlexaboutno aff
Pei Lin Lua, Wong Sok Yee, Neni Widiasmoro Selamat

Bibliographic record

VenueAsean Journal of Psychiatry · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsHospital Anxiety and Depression ScaleAnxietyMedicineDepression (economics)Quality of life (healthcare)MalayDepressive symptomsPsychiatryClinical psychology
DOInot available

Abstract

fetched live from OpenAlex

Objective: This study was aimed to determine the prevalence of anxiety and depressive symptoms , to examine their association with health-related quality of life (HRQoL) profiles and to determine the predictors on overall HRQoL. Methods: This was a cross-sectional study conducted in Hospital Sultanah Nur Zahirah, Kuala Terengganu, Malaysia. The Malay Hospital Anxiety and Depression Scale (HADS) and McGill Quality of Life Questionnaire (MMQoL) were administered to a sample of 150 cancer patients (mean age = 50.4 years). Chi-square test, correlation and multiple regression were utilised for data analysis. Results: The prevalence for mild anxiety and depressive symptoms was 30.7% and 23.3% respectively. The HADS-A correlated strongest with Total MMQoL Score (r = - 0.578) and Psychological Well-Being (r = -0.526). Only HADS-A (beta = - 0.486), and HADS-D (beta = -0.173) were significant in predicting overall health-related quality of life. Conclusion: Findings in our study indicated that the prevalence of anxiety and depressive symptoms in Terengganu cancer patients are moderate. If anxiety and depression are identified and treated, health-related quality of life among oncology patients appropriately could significantly be improved. ASEAN Journal of Psychiatry, Vol.12(1): Jan - June 2011: XX XX

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 score0.007
Threshold uncertainty score0.681

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 head0.017
GPT teacher head0.277
Teacher spread0.260 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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