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Record W3130175690 · doi:10.36953/ecj.2015.se1651

The relationship between early maladaptive schemas and mental skills with goal orientation of footballers

2015· article· en· W3130175690 on OpenAlexaboutno aff
Asma Kazemi, Alireza Kakavand, Rokhsareh Fazli

Bibliographic record

VenueEnvironment Conservation Journal · 2015
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyLeagueAnxietyFootballDescriptive statisticsApplied psychologyAthletesSample (material)Schema (genetic algorithms)Clinical psychologyStatisticsPhysical therapy

Abstract

fetched live from OpenAlex

This study aims to investigate the relationship between early maladaptive schemas and mental skills by interpreting the footballer's competitive anxiety and goal orientation. The research methodology is descriptive using correlational models. According to the predictor variables of early maladaptive schemas and mental skills, goal orientation and competitive anxiety are predicted in a sample of footballers in the Premier League and Azadegan League. The research population consisted of all footballers in the Premier League and Azadegan League. Considering the formula, the sample size was chosen 200 using convenience sampling method from Premier and Azadegan Leagues’ football teams. The measurement tools include Young Schema Questionnaire-Short Form (YSQ-SF; Young, 1998), The Ottawa Mental Skills Assessment Tool 3 (OMSAT-3), Task and Ego Orientation in Sport Questionnaire (TEOSQ) and Competitive State Anxiety Inventory-2 (CSAI-2). In the statistical analysis of the data, descriptive and inferential indicators and methods were used. The research hypotheses were tested by standardized multiple regression analysis and finally, the conclusion based on the hypotheses was that there was a relationship between some components of early maladaptive schemas and mental skills with goal orientation of footballers.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.064
GPT teacher head0.309
Teacher spread0.245 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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