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Record W4400944569 · doi:10.1123/iscj.2023-0042

Developing Close, Trusting Coach–Athlete Relationships With High-Performance Adolescent Tennis Players

2024· article· en· W4400944569 on OpenAlexaffabout
Mikaela C. Papich, Gordon A. Bloom, Lea-Cathrin Dohme

Bibliographic record

VenueInternational Sport Coaching Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsMcGill University
Fundersnot available
KeywordsAthletesPsychologyApplied psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to understand why and how experienced tennis coaches developed quality relationships with their high-performance adolescent athletes that prioritized athletes’ needs and well-being. Five highly regarded Canadian tennis coaches of internationally ranked adolescent players engaged in two semistructured interviews and three story completion tasks. The data were analyzed using reflexive thematic analysis. Findings outlined that coaches unanimously believed establishing a close, trusting relationship with their adolescent athletes was fundamental to creating a caring environment in which empathy for athletes’ athletic, academic, and personal demands could be demonstrated. Coaches also described the difficulties of navigating these close relationships in a climate that is under severe scrutiny because of athlete maltreatment allegations. Examples of coaching behaviors that fostered closeness and maintained athlete safety included demonstrating care towards athletes’ social, emotional, academic, and athletic challenges, encouraging dialogue in which athletes expressed their wants and needs, and involving parents to help maintain transparency regarding the establishment of closeness. Uniquely, this study provides practical suggestions for how coaches can nurture closeness while promoting safe environments that prioritize athletes’ welfare.

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 score0.066
Threshold uncertainty score0.942

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.319
Teacher spread0.276 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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