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Record W4393199411 · doi:10.3389/fpsyg.2024.1354129

Exploring a practitioner-athlete relationship and facilitated learning throughout a psychological skills training program

2024· article· en· W4393199411 on OpenAlexaff
Xiao Zhang, Morgan Rogers, Penny Werthner

Bibliographic record

VenueFrontiers in Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyDirectiveApplied psychologySport psychologyQuality (philosophy)Medical educationComputer science

Abstract

fetched live from OpenAlex

Psychological skills training (PST) programs have been consistently reported as an important part of preparation for optimal performance in high performance sport. However, there is much less research about the quality and characteristics of the working relationship between a sport psychology practitioner (SPP) and an athlete and, importantly, how that relationship facilitates learning. Therefore, the purpose of the present paper was to explore the working relationship between a SPP and a volleyball player and how that working relationship facilitated the learning processes utilized by this player, as she prepared for the demands of her sport and life. An instrumental case study methodology with a qualitative description approach was employed to illustrate different aspects of the evolving relationship and the athlete's experiences. The results of this case reflect an approach that combined features of both a directive approach in teaching specific psychological skills and a less directive and more collaborative approach, which, in turn, allowed an athlete to begin to learn how to guide their own learning.

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)0.014
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0050.003
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.157
GPT teacher head0.419
Teacher spread0.261 · 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 designQualitative
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

Citations3
Published2024
Admission routes1
Has abstractyes

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