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Record W2165085345 · doi:10.5539/gjhs.v7n3p161

The Impact of Depression and Malnutrition on Health-Related Quality of Life Among the Elderly Iranians

2014· article· en· W2165085345 on OpenAlexvenueno aff
Sareh Keshavarzi, Seyed Mehdi Ahmadi, Kamran Bagheri Lankarani

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
FundersShiraz UniversityShiraz University of Medical Sciences
KeywordsMalnutritionDepression (economics)Geriatric Depression ScaleMedicineQuality of life (healthcare)GerontologyDepressive symptomsMental healthCross-sectional studyActivities of daily livingPsychiatryInternal medicineAnxiety

Abstract

fetched live from OpenAlex

INTRODUCTION: The present study aimed to assess the association between nutritional status and depressive symptoms among elderly Iranians and to explore their impact on their Health-Related Quality of Life (HRQoL). METHODS: In this cross-sectional study, 447 elders aging from 55 to 85 years were randomly selected and completed the Iranian version of Geriatric Depression Scale-15 (GDS), Mini Nutritional Assessment (MNA), and the Iranian version of Short Form Health Survey (SF-36). RESULTS: Out of the 447 elderly, 72.1% were female with the mean age of 65.99 ± 7.89 years. The prevalence of depression was 38.1%. In addition, the SF-36 sub-scores tended to be lower among the elders with depressive symptoms according to GDS. The Physical Functioning (PF), Bodily Pain (BP), Role Physical (RP), Role Emotional (RE), and Mental Health (MH) dimensions of the SF-36 were also statistically poorer in the elders with depression. The mean MNA score was 24.6 ± 2.7; 35.4% of the participants were malnourished or at risk of malnutrition and 64.6% were adequately nourished. The sub scores of SF-36 were significantly lower in the elders with impaired nutritional status. CONCLUSIONS: Considering the importance of the association among psychological and nutritional problems and HRQoL in caring for and promoting the welfare of the elders, this study provided fundamental information and a basis for further evaluation of this issue in developing and undeveloped countries.

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)0.009
metaresearch head score (Gemma)0.001
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 score0.109
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.053
GPT teacher head0.419
Teacher spread0.366 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

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