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Record W4416301486 · doi:10.47197/retos.v73.116808

Confirmatory factor analysis to validate the short version of the Ottawa mental skill for sports (OMSAT-3) in Portuguese athletes

2025· article· en· W4416301486 on OpenAlexaboutno aff
Carlos Silva, Diana Torres, Hugo Louro, Carla Chicau Borrego, Marco Batista

Bibliographic record

VenueRetos · 2025
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsPortugueseConfirmatory factor analysisAthletesConcurrent validityPsychometricsPsychometric testingStructural equation modelingCognition

Abstract

fetched live from OpenAlex

Introduction: The development of psychological skills plays an important role in improving performance and achieving excellent results in sports. Objective: The aim of this study was to validate the Portuguese short version of the Ottawa Mental Skills Assessment Tool (OMSAT-3). Methodology: By means of a confirmatory factor analysis (CFA), a total of 524 Portuguese athletes of both sexes aged between 12 and 42 (M = 19.21; SD = 5.46) were recruited for the present study. Results: The results indicate that the reduced version of the OMSAT-3 (30 items) has adequate psychometric qualities, enabling an assessment of Foundation, Psychosomatic and Cognitive Skills (SRMR = 0.052; CFI = 0.983; TLI = 0.979; RMSEA = 0.031 CI 90% [0.026, 0.037]; χ²/df = 1.52). There was high concurrent validity between the reduced version (30 items) and the full version (48 items). Conclusions: The results suggest that the reduced OMSAT-3 can be used with confidence to assess psychological skills in a sports context.

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)0.010
metaresearch head score (Gemma)0.023
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.331
Teacher spread0.316 · 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
Published2025
Admission routes1
Has abstractyes

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