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Análise da consistência interna e fatorial confirmatório do IMPRAFE-126 com praticantes de atividades físicas gaúchos

2008· article· pt· W2131863904 on OpenAlexaff
Marcos Alencar Abaíde Balbinotti, Marcus Levi Lopes Barbosa

Bibliographic record

VenuePsico-USF · 2008
Typearticle
Languagept
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsStructural equation modelingHumanitiesPsychologyPhysical activityMathematicsPhilosophyPhysical therapyStatisticsMedicine

Abstract

fetched live from OpenAlex

Neste estudo, a motivação é entendida à luz da teoria da Autodeterminação. O objetivo deste estudo é verificar os índices de consistência interna e fatorial confirmatório do IMPRAFE-126. Utilizou-se uma amostra de 1.377 sujeitos, gaúchos, de ambos os sexos e com idades variando de 13 a 83 anos. Os resultados dos índices alfa de Cronbach (superiores a 0,89) foram satisfatórios. A adequação do modelo em seis dimensões foi testada e a validade confirmatória foi assumida para a amostra geral (x2/gl=2,520; GFI=0,859; AGFI=0,854; RMSEA=0,065) e para os sexos masculino (x2/gl=3,905; GFI=0,885; AGFI=0,881; RMSEA=0,066) e feminino (x2/gl=4,337; GFI=0,840; AGFI=0,831; RMSEA=0,068). Esses resultados indicam que o IMPRAFE-126 é um instrumento promissor e que pode ser oportunamente utilizado por psicólogos do esporte ou educadores físicos, aqueles particularmente interessados em avaliar os níveis de motivação de atletas ou praticantes de atividade física e esporte em geral. Entretanto, outros estudos de validade, fidedignidade e de normas devem ser conduzidos a fim de poder-se publicá-los em um futuro próximo.

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)0.013
metaresearch head score (Gemma)0.047
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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.047
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.323
Teacher spread0.277 · 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

Citations28
Published2008
Admission routes1
Has abstractyes

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