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Record W7133003957

”Abre tu mente”. Una estrategia lúdico - pedagógica enfocada en la memoria instrumental en el adulto mayor de la Universidad del Valle seccional Palmira. : ”Abre tu mente”. Una estrategia lúdico - pedagógica enfocada en la memoria instrumental en el adulto mayor de la Universidad del Valle seccional Palmira.

2025· dissertation· es· W7133003957 on OpenAlexaboutno aff
Adeiba Yaneth Acosta Hoyos, Janina Utria Coronado

Bibliographic record

VenueRepositorio Institucional FULL · 2025
Typedissertation
Languagees
FieldMedicine
TopicAging, Health, and Disability
Canadian institutionsnot available
Fundersnot available
KeywordsMemoriaMemory testContext (archaeology)Lived experience
DOInot available

Abstract

fetched live from OpenAlex

Este estudio se centró en fomentar la memoria instrumental en adultos mayores mediante una estrategia lúdicopedagógica en la Universidad del Valle, seccional Palmira. La investigación abordó la importancia de mantener y mejorar las funciones cognitivas en la vejez, destacando el impacto positivo de actividades lúdicas en la retención de habilidades motoras. Se aplicaron el Cuestionario Internacional de Actividad Física (IPAQ) y la prueba de evaluación cognitiva Montreal (MoCA) para medir la condición física y cognitiva de los participantes antes y después de la intervención. Los resultados demostraron que el grupo experimental, que participó en la estrategia lúdico-pedagógica, logró una mejora significativa en la memoria instrumental en comparación con el grupo control. Estos hallazgos subrayan la efectividad de las intervenciones lúdicas en la promoción de la salud cognitiva en adultos mayores y la importancia de su implementación en programas educativos y de salud.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.003

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 head0.007
GPT teacher head0.312
Teacher spread0.304 · 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 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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