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Record W2953499568 · doi:10.31382/eqol.190601

The effects of Mindfulness-Based Cognitive Therapy (MBCT) on cognitive skills in young soccer players

2019· article· en· W2953499568 on OpenAlexaboutno aff
Seyed Mohammad Zadkhosh, Hassan Gharayagh Zandi, Majid Ghorbannejad

Bibliographic record

VenueExercise and Quality of Life · 2019
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulness-based cognitive therapyMindfulnessCognitionPsychologyCognitive therapyCognitive psychologyPsychotherapistClinical psychologyNeuroscience

Abstract

fetched live from OpenAlex

<p style="text-align: justify;">TThe aim of this study was to examine the effect of Mindfulness-Based Cognitive Therapy training on mental skills of young soccer players. For this purpose, 30 soccer players were assigned to an experimental group of 15 players (intervention based on Mindfulness) and control group of 15 players that participations’ age ranged from 17 to 20 years old in provincial competitions in the year 2017-18. Athletes in the experimental group participated in MBCT course over eight weekly sessions of 2 h each. The control group did not receive any intervention. Before and after interventions Ottawa mental skills assessment tool questionnaire (Omsat-3) were used to collect data. The results of MANOVA showed that significant improvement in all sub-scales scores; Focusing (P=0.001), Refocusing (P=0.001), Imagery (P=0.001), Mental practice (P=0.001), and Competition planning (P=0.001) in the experimental group than control group. According to these findings, we concluded that Mindfulness-Based Cognitive Therapy training is appropriate to improve the level of cognitive skills of soccer players.</p>

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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)0.0010.000

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 head0.028
GPT teacher head0.352
Teacher spread0.324 · 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 designNon-randomized trial
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

Citations9
Published2019
Admission routes1
Has abstractyes

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