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Application of a cognitive program with a comprehensive strategy feedback for Korean older adults

2023· article· en· W4389037635 on OpenAlexaboutno aff
Mi Kyeong Kim, Ji‐Hyuk Park, Dae‐Sung Han, Hae Yean Park

Bibliographic record

VenueGeriatric Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Research Foundation of KoreaMinistry of Science, ICT and Future Planning
KeywordsCognitionStroop effectPsychologyTest (biology)Intervention (counseling)Clinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

This study assesses the effect of a cognitive program with a comprehensive strategy feedback on the cognitive function and memory self-efficacy of community-dwelling older adults and explores its applicability in Korea. The study employed a group pre-posttest design on 10 cognitively normal older participants. The four-week cognitive program involved daily completion of cognitive tasks at home using CoTras-Pro 2 and remotely provided individual non-face-to-face feedback. Additionally, biweekly face-to-face group feedback sessions were conducted with five participants. The Korean version of the Montreal Cognitive Assessment, the Korean-Color Word Stroop Test, and the Memory Self-Efficacy Questionnaire were used. Post-interviews were conducted to collect feedback. The program exerted a notable positive impact on cognitive function and memory self-efficacy. A study designed as a large-scale program conducted in collaboration with community-based public and private organizations holds the potential to be modeled for similar intervention programs involving a large number of participants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.346
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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