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Record W1556936433

Percepção da dor e estados de humor em atletas lesionados

2006· article· es· W1556936433 on OpenAlexaboutno aff
Alexandro Andrade, Sabrina de Oliveira Sanches, Viviane Pacheco Gonçalves, Evânea Scopel, Daniela Schramm Szeneszi

Bibliographic record

VenueAcceda (Universidad de Las Palmas de Gran Canaria) · 2006
Typearticle
Languagees
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyHumanitiesPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Este estudo objetivou identificar a relação entre os estados de humor de atletas de futebol lesionados e a implicação da dor, decorrente do processo de lesão, no cotidiano desses atletas. A amostra foi constituída de 14 atletas de futebol de campo amadores e profissionais do gênero masculino com idade média de 18,7 anos (-15+23), todos apresentando lesões decorrente da prática esportiva e queixa de dor. Para obtenção dos dados referentes aos estados de humor utilizou-se Profile of Mood State (POMS) traduzido por Peluso (2003). A avaliação da influência da dor no cotidiano dos atletas foi realizada por meio da utilização do item “Impacto da dor na vida do paciente” presente no questionário intitulado McGill Pain Questionnaire (versão Brasileira) traduzido e adaptado por Castro (1999). Os dados foram tratados com estatística descritiva (media, freqüência e desvio padrão) e inferencial (ANOVA). Os resultados indicam que metade dos atletas apresentavam lesões leves ou estavam no processo final de recuperação. Assim, não foi verificado alterações no estado de humor e prejuízo no cotidiano destes atletas, exceto o prejuízo no trabalho. Embora a estatística inferencial não tenha demonstrado diferenças significativas, a analise qualitativa dos dados permitiu observar que os atletas que apresentaram perda de dias e prejuízo no trabalho e maior auto-cobrança para voltar a jogar demonstraram alterações de humor.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.312
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

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
Published2006
Admission routes1
Has abstractyes

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