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The influence of studies in Cognitive Wellness University for the elderly people on maintaining their cognitive functions.

2017· article· en· W2732136063 on OpenAlexaboutno aff
Л.В. Усенко, G.S. Kanyuka, Д.В. Оленюк, O.O. Usenko, J.V. Silkina

Bibliographic record

VenueMedicni perspektivi · 2017
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionGerontologyPsychologyCognitive declinePopulationMedicineDementiaPsychiatry

Abstract

fetched live from OpenAlex

Progressive aging of the population is accompanied by age-related changes in the body, primarily from the central nervous system, which causes a decline in the cognitive health of man and society as a whole. The emergence of cognitive deficits leads to a decrease in a person's ability to think, learn, actively perceive information, make decisions, worsen other psycho-physiological functions.The aim of our study was to assess the state of cognitive functions of the elderly people, the dynamics of their changes, depending on the age stage of life, as well as under the influence of program exercises and specially designed trainings aimed at activating mental and physical activity. 165 students of the university aged 55-85 years took part in the study. Two groups of subjects were identified. The first one numbering 100 people we divided into 3 subgroups in order to identify phased age-related changes in cognitive functions and, depending on this definition, the need for preventive or corrective measures: 1 subgroup - 55-65 years, 2 subgroup - 66-75 years and 3 subgroup - 76 years and older. The study of their cognitive functions was determined upon admission to the university. The second group consisted of 65 people, whose indicators of cognitive functions were determined in dynamics: at admission to the university and at the completion of training. To assess the level of cognitive functions, we used a formalized screening technique - the Montreal Scale. The established dynamics of the components of cognitive functions, depending on age, makes it possible to differentially approach the choice of preventive or corrective measures aimed at activating cognitive functions, in each age group with an emphasis on those of them that have been changed to a greater extent. The effectiveness of the proposed structure of studies at the university for the elderly was shown.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.091
GPT teacher head0.389
Teacher spread0.298 · 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.

Study designQualitative
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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