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Record W2181877125 · doi:10.4172/2167-7182.1000174

Severity of Depression among Elderly Women Attending Holy Quran Memorization Centers in Saudi Arabia

2014· article· en· W2181877125 on OpenAlexaboutno aff
Areej M Al Qahtani

Bibliographic record

VenueJournal of Gerontology & Geriatric Research · 2014
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDepression (economics)MemorizationOmicsAlternative medicineTraditional medicinePsychiatryFamily medicineGerontologyBioinformaticsPathology

Abstract

fetched live from OpenAlex

Background: Depression is the most common mental health problem among the elderly, causing considerable morbidity worldwide, as well as increased healthcare costs. The purpose of this study was to determine the severity of depression among Saudi elderly women attending Quran Memorization Centers in the Kingdom of Saudi Arabia. Methods: We recruited 340 participants aged 65 years and older from 11 Quran Memorization Centers in the cities of Dammam, Khobar and Dhahran in the Eastern Province of Saudi Arabia. We used a structured interview questionnaire composed of socio-demographic characteristics and the Activity of Daily Living instrument to assess participants’ physical, social, and health-status conditions. In addition, we used the English version of the Geriatric Depression Scale and the Arabic version of Montreal Cognitive Assessment as the main screening instruments for depression and cognitive impairment respectively. Results: Severity of depression among all participants was 42.1%. Low monthly income, the absence of a caregiver, diabetes, disability, and sleep disturbance were identified as common factors associated with depression. Conclusion: The present study showed that depression in elderly females attending these centers was high and associated with multiple medical and socioeconomic characteristics, which is a cause of concern.

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)0.007
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 score0.050
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.400
Teacher spread0.348 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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