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Record W4415140413 · doi:10.33024/hjk.v19i7.1369

Hubungan tingkat depresi dengan fungsi kognitif pada lansia

2025· article· en· W4415140413 on OpenAlexaboutno aff
Isratul Hasbiah, Nurilla Kholidah, Audy Oktavioni Tiara Putri

Bibliographic record

VenueHOLISTIK JURNAL KESEHATAN · 2025
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)CognitionSpearman's rank correlation coefficientRank correlationElderly peopleQuality of life (healthcare)Cognitive impairment

Abstract

fetched live from OpenAlex

Background: Aging causes a decline in physical and psychological functions, including depression and cognitive impairment in the elderly. Depression worsens cognitive decline, negatively impacting the quality of life of the elderly. With the increasing number of elderly people in Indonesia, it is important to understand the relationship between depression and cognitive function to improve their care and quality of life. Purpose: To determine the association between the level of depression and cognitive function in elderly residents. Method: This research used an analytical design with a cross-sectional study approach. The sample size consisted of 74 elderly people selected using a purposive sampling technique. Data collection used the Geriatric Depression Scale-15 (GDS-15) questionnaire to assess depression levels and the Montreal Cognitive Assessment (MoCA-INA) questionnaire to assess cognitive function in the elderly. Data analysis used the Spearman rank correlation test with SPSS software. Results: The majority of respondents were female (36 respondents (48.6%), followed by 47 elderly respondents (63.5%), with no education (25 respondents (33.8%), and 49 respondents (66.2%). The Spearman rank correlation test showed a p-value (0.021) ≤ α (0.05), indicating a significant relationship between depression levels and cognitive function in the elderly. Conclusion: There is a significant relationship between the level of depression and cognitive function in the elderly, appropriate intervention efforts to manage depression in the elderly are very necessary to maintain and improve their cognitive function and quality of life. Keywords: Cognitive Function; Depression; Elderly. Pendahuluan: Penuaan menyebabkan penurunan fungsi fisik dan psikologis, termasuk depresi dan gangguan fungsi kognitif pada lansia. Depresi memperburuk penurunan fungsi kognitif, yang berdampak negatif pada kualitas hidup lansia. Dengan meningkatnya jumlah lansia di Indonesia, penting untuk memahami keterkaitan antara depresi dan fungsi kognitif agar dapat meningkatkan penanganan dan kualitas hidup mereka. Tujuan: Untuk mengetahui hubungan antara tingkat depresi dengan fungsi kognitif pada lansia. Metode: Penelitian desain analitik dengan pendekatan cross sectional study. Jumlah sampel penelitian terdiri dari 74 lansia yang diambil dengan teknik purposive sampling. Pengumpulan data mengguanakan menggunakan kuesioner Geriatric Depression Scale-15 (GDS-15) untuk menilai tingkat depresi dan kuesioner Montreal Cognitive Assesment (MoCA-INA) untuk menilai fungsi kognitif lansia. Analisis data mengguanakan uji korelasi Spearman rank dengan software SPSS. Hasil: Responden terbanyak adalah berjenis kelamin perempuan sebanyak 36 responden (48.6%), kelompok lanjut usia yaitu 47 responden (63.5%), riwayat pendidikan tidak sekolah sebanyak 25 responden (33.8%), dan memiliki riwayat penyakit kronis sebanyak 49 responden (66.2%). Berdasarkan hasil uji korelasi spearman rank menunjukkan nilai p (0.021) ≤ α (0.05), artinya adanya hubungan yang signfikan antara tingkat depresi dan fungsi kognitif pada lansia. Simpulan: Adanya hubungan signifikan antara tingkat depresi dan fungsi kognitif pada lansia, upaya intervensi yang tepat untuk mengelola depresi pada lansia sangat diperlukan guna mempertahankan dan meningkatkan fungsi kognitif serta kualitas hidup mereka. Kata Kunci: Depresi; Fungsi Kognitif; Lansia.

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)0.001
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 score0.148
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.328
Teacher spread0.308 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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