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Record W1946945854 · doi:10.4236/ape.2015.52014

The Effects of Mental Imagery and Cardiac Coherence on Mental Skills of Tunisian Karate Players at School Age

2015· article· en· W1946945854 on OpenAlexaff
Sabeur Hamrouni, Jaouad Alem, Sylvain Baert, Ines Bouguerra

Bibliographic record

VenueAdvances in Physical Education · 2015
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCoachingPsychologyMental imageRelaxation (psychology)AthletesCognitionClinical psychologyApplied psychologyPhysical therapyPsychiatrySocial psychologyPsychotherapistMedicine

Abstract

fetched live from OpenAlex

The aim of our research consisted in checking the influence of the mental coaching using the mental imagery and relaxation based on cardiac coherence, on the improvement of the cognitive, emotional and behavioural reactions of the Tunisian karate Elite at school age. Our study was about to check if the Tunisian karate Elite at school age who had undergone a mental coaching has a better mental profile than their counterparts of other sporting disciplines and karate players with usual training method. Our sample consisted on an experimental group (N = 24 athletes) and control group (N = 22 athletes), all aged from 16 to 19 years. The experimental group followed for 10 months a psychological and mental coaching. The control group continued to train normally for the same period of time. To assess the mental skills of the participants, we used the OMSAT-3 (Guelmami et al., 2014). Our study showed that a 10 month mental coaching was needed to improve 4 mental skills, i.e. “goal setting” “relaxation”, “imagery” and “mental practices”. Our findings sustained the importance of a three-dimensional mental coaching in relation to one basic skill (goal setting), a psychosomatic skill (relaxation) and two cognitive skills (imagery and mental practice).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.007
GPT teacher head0.324
Teacher spread0.317 · 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 teacher head, 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

Citations1
Published2015
Admission routes1
Has abstractyes

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