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Record W2799762619

스마트폰 사용 및 인지기능이 노인의 우울감, 고독감에 미치는 영향

2017· article· ko· W2799762619 on OpenAlexaboutno aff
황순현, 이혜진, 하은희, 김소형, 정근경, 최효진

Bibliographic record

Venue고령자·치매작업치료학회지 · 2017
Typearticle
Languageko
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineCognitive impairmentCognitionPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

목적 본 연구의 목적은 노인의 스마트폰의 사용 유무에 따른 인지기능과 우울감, 고독감의 관계를 파악 하고자 하며, 추후 노인 작업치료를 시행함에 있어 노인의 스마트폰 사용에 대한 고려를 장려하기 위함이다. 연구방법 2016년 8월 18일부터 9월 21일까지 부산 소재의 K복지관, G양로원, 김해 소재의 H아파트, D아파 트의 노인정에서 65세 이상 노인 총 54명을 대상으로 실시하였다. The Korean Version of Montreal Cognitive Assessment(MoCA-K), Korea UCLA Lonelines scale(UCLA 고독감 척도), Geriartric Depress Scale Short Korea Version(GDSSF-K)를 사용하여 평가를 하였으며, 빈도분석과 t-test, 회귀분석을 이용하여 분석하였다. 연구결과 스마트폰 사용유무에 따른 인지기능(p<0.001)과 우울감(p<0.05), 고독감(p<0.001)은 유의미한 차이 를 보였다. 인지기능과 고독감 사이에는 유의미한 상관관계를 보였지만 인지기능과 우울감, 우울감 과 고독감 사이에는 유의미한 상관관계를 보이지 않았다. 결론 본 연구를 통해, 스마트폰 사용이 노인의 인지기능 유지 및 향상이 된다는 것을 알 수 있었으며, 노인의 우울감과 고독감을 낮춰준다는 점을 알 수 있었다. 향후 건강한 노년을 위해 노인의 스마트 폰 사용을 권장하고 스마트폰 교육이 활성화 될 것을 기대한다.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0120.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.045

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 head0.156
GPT teacher head0.552
Teacher spread0.396 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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