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Record W2134741448 · doi:10.22059/jmlm.2013.35696

تأثیر آموزش هوش هیجانی بر مهارت های روانی نوجوانان ورزشکار

2013· article· fa· W2134741448 on OpenAlexaboutno aff
لیلا ریاحی فارسانی, احمد فرخی, ابوالفضل فراهانی, پروانه شمسی پور

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languagefa
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The aim of the present research was to investigate the effect of emotional intelligence training on mental skills in athlete teenagers. For this purpose, 80 volunteer students (40 boys and 40 girls, mean age of 15±0.53 years) were selected with simple random sampling method and divided into two experimental and control groups. Bradberry and Greaves emotional intelligence questionnaire was used to measure emotional intelligence and Ottawa Mental States Assessment Tool (OMSAT 3) to measure mental skills. Also, the questionnaires used in Iran were reliable and valid. The program of emotional intelligence skill training to teenager athletes lasted 10 sessions. Data were analyzed using consistency of variances, Kolmogorov Smirnov test and independent t test (the mean comparisons of the two groups based on the difference of scores between pretest and posttest) at P<0.05. Results showed a significant difference between posttest means in the two groups in four components of emotional intelligence (self-awareness, self-management, social awareness and relationship management) and mental skills (P˂0.05). Thus, it seems that the training of emotional intelligence skills is one of the important parts of mental preparation that is necessary to achieve optimum athletic performance.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.007
Open science0.0080.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4280.014

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 head0.441
GPT teacher head0.630
Teacher spread0.189 · 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
Published2013
Admission routes1
Has abstractyes

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