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Record W4399793008 · doi:10.15332/2422474x.9889

Impact of a physical exercise program on resilience level

2024· article· en· W4399793008 on OpenAlexaff
Karollyni Bastos Andrade Dantas, Francisco Prado Reis, Marco Antônio Almeida Santos, Aline Geovanna Peixoto Duarte, Luana Santos Costa, Lúcio Flávio Gomes Ribeiro da Costa, Natália De Góes Lima, Raissa Pinho Morais, Estélio Henrique Martin Dantas

Bibliographic record

VenueCuerpo Cultura y Movimiento · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsImpact
Fundersnot available
KeywordsResilience (materials science)Physical fitnessPsychologyPhysical activityApplied psychologyComputer sciencePhysical medicine and rehabilitationPhysical therapyMedicinePhysics

Abstract

fetched live from OpenAlex

Aging is a physiological process. In view of the changes that affect the elderly, there is a need for resilience in this context, essential for well-being. Objective: To analyze the resilience rates of the elderly, before and after the practice of physical activity. This is a quasi-experimental, quantitative, descriptive cross-sectional study, carried out with 100 elderly people. The participants completed the anamnesis and the “Wagnild and Young Resilience Scale”. There is an age group of 65 to 69 years old (37.8%), self-declared white (38.9%), user of UBS Augusto Franco (Augusto Franco) (62.2%), married (40%) , with incomplete primary education (26.7%), retired (40%), with a monthly family income of up to 2 minimum wages (57.8%), with the majority having increased levels of resilience subfacets: serenity, sense of life, self-confidence, self-sufficiency, perseverance. The data obtained indicate an increase in resilience with the practice of exercise, contributing to the improvement of health.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
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)0.0030.000

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 head0.059
GPT teacher head0.474
Teacher spread0.415 · 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
Published2024
Admission routes1
Has abstractyes

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