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ANÁLISE DO MEDO DE CAIR EM IDOSAS PRATICANTES DE EXERCÍCIOS FÍSICOS ESEDENTÁRIAS

2018· article· en· W2918090514 on OpenAlexaboutno aff
José Henrique Piedade Cardoso, Luana Martins de Paula, Silas de Oliveira Damasceno, Henrique Martins Ungri, Ronaldo Valdir Briani, Ana Caroline Rippi Moreno, Laís Manata Vanzela, Franciele Marques Vanderlei, Giovana Gomes dos Santos, Bianca Yumie Eto, Cláudia Regina Sgobbi de Faria

Bibliographic record

VenueColloquium Vitae · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPhysical Education and Gymnastics
Canadian institutionsnot available
Fundersnot available
KeywordsTinetti testFear of fallingBalance (ability)Elderly peopleGerontologyMedicinePsychologyPhysical activityCognitionPhysical therapyPoison controlInjury preventionPsychiatry

Abstract

fetched live from OpenAlex

The incidence of falls is high in elderly and the fear of falls is between the universal occurences of falls in this population. In this sense, the study has by objective evaluate the balance and the fear of falls in elderly woman participants of physical activity and sedentaries. Were evaluated 30 ederly of feminine sex divided into two groups: therapeutic exercise and sedentarism. All groups were evaluated using the Montreal Cognitive Assessment (MoCA), Falls Efficacy Scale (FES-I-Brasil) and Tinetti Balance Scale. The results showed significative difference on fear of falls (20.09±3.7 vs 37.72±8.7; p=0.000), balance and march (27.77±0.5 vs 18.40±3.9; p<0.000) in favor to active elderly woman. For the variables age (68.04±5.3 vs 72.54±8.4; p=0.04) and cognition (28.31±1.5 vs 24.45±1.8; p=0.44), significative diferences were not observed. At this form, we conclude that physically active elderly woman presented less preocupation with the possibility of falls and better balance levels.

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)0.002
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.362
Teacher spread0.342 · 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
Published2018
Admission routes1
Has abstractyes

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