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Record W2263986083

Sport psychological skills that discriminate between successful and less successful female university field hockey players : sports psychology

2010· article· en· W2263986083 on OpenAlexaboutno aff
A. Kruger

Bibliographic record

VenueAfrican Journal for Physical Health Education Recreation and Dance · 2010
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsField hockeySport psychologyPsychologyApplied psychologyIce hockeyNeed for achievementCompetitive sportClinical psychologyAthletesSocial psychologyPhysical therapyPhysical medicine and rehabilitationAdvertising
DOInot available

Abstract

fetched live from OpenAlex

Sport psychology plays an important and ever-increasing role in competitive sport. The objective of this study was to determine the sport psychological skills that discriminate significantly between successful and less successful female university field hockey players in order to emphasize the characteristics that need to be addressed in sport psychological skills training (SPST) sessions. The subjects consisted of 106 female university hockey players, categorized into a successful (players from the A division) and less successful group (players from the B division). The sport psychological skill (SPS) levels measured with the Psychological Skill Inventory (PSI) and the Ottawa Mental Skills Assessment Tool-3 (OMSAT-3) from the two groups were compared and reported. The results indicated that the successful group had better results in 66.7% of the variables that were measured in the study. Practical significance was found in four of the 18 psychological variables that included achievement motivation, goal-directedness, goal-setting and fear control. Furthermore, six variables discriminate significantly between the successful and less successful female hockey players, which included achievement motivation, stress reactions, fear control, self-confidence, mental rehearsal as well as imagery.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.820

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.390
Teacher spread0.351 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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