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Record W4407058011 · doi:10.31729/jnma.8885

Depression among Elderly of Chhayanath Rara Municipality, Mugu, Nepal: An Observational Study

2025· article· en· W4407058011 on OpenAlexaff
Nabina Malla, Lisasha Poudel, Sushma Pokhrel, Vidya Chaudhary, Prajita Mali

Bibliographic record

VenueJournal of Nepal Medical Association · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsLambton College
Fundersnot available
KeywordsMedicineDepression (economics)Observational studyGeriatric Depression ScaleElderly peopleDescriptive researchPublic healthPopulationDepressive symptomsGerontologyEnvironmental healthPsychiatryInternal medicineNursing

Abstract

fetched live from OpenAlex

Introduction: Depression is an important public health issue responsible for considerable morbidity and mortality among elderly. There were very few studies related with depression among elderly from rural community of Nepal. The objectives were to assess depression among elderly in Chhayanath Rara municipality. Methods: An observational cross-section study was conducted among 387 elderly through face-to-face interviews by using Geriatric Depression Scale-short scale (GSD 15) in a municipality of Nepal. Ethical Approval was taken from Institutional Review Committee (Reference Number: 079/80-015 ). Data were entered and analyzed into SPSS version 20. Descriptive statistics was employed to assess the prevalence of elderly depression. Results: The prevalence of depression among elderly was found to be 282 (72.87%; 95% CI: 68.44%-77.30%). Of those, 124 (32.04%) experienced mild depression, 115 (29.72%) had moderate depression, and 43 (11.11%) suffered from severe depression. Conclusions: The prevalence of depression in elderly was found higher compared to previous studies and other population.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.023
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.084
GPT teacher head0.422
Teacher spread0.338 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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