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Record W4378805360 · doi:10.37885/230512982

OS BENEFÍCIOS DA MUSCULAÇÃO PARA IDOSOS RELACIONADOS AS ADAPTAÇÕES FISIOLÓGICAS

2023· book-chapter· pt· W4378805360 on OpenAlexaff
F. W. Á. NASCIMENTO, Ayolsé Andrade Pires dos Santos

Bibliographic record

VenueEditora Científica Digital eBooks · 2023
Typebook-chapter
Languagept
FieldSocial Sciences
TopicPhysical Education and Gymnastics
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPsychologyPhysical activityGerontologyPhysicsMedicinePhilosophyPhysical therapy

Abstract

fetched live from OpenAlex

A prática da musculação recebe atualmente um destaque especial, em decorrência da evolução cientifica que apresentou nas últimas décadas com a publicação de inúmeras pesquisas e artigos sobre a série de benefícios da sua prática, principalmente por idosos. O presente estudo tem como objetivo discutir sobre os benefícios da musculação para a saúde dos idosos, por entendermos que o sedentarismo é uma das causas que favorece o surgimento de várias doenças para esta população. Trata-se de uma revisão bibliográfica embasada em artigos científicos, livros e monografias desenvolvidos até o ano de 2023. De acordo com os estudos abordados neste trabalho, a musculação demostrou ser um aliado tanto na melhora da mobilidade e do condicionamento físico quanto na melhora da autoestima dos idosos, além de ajudar na manutenção muscular, promove redução dos sinais e sintomas de várias doenças e condições crónicas como diabetes, obesidade, osteoporose, depressão e dentre outras patologias.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.081
GPT teacher head0.328
Teacher spread0.247 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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