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Record W4401688888 · doi:10.1123/iscj.2024-0003

Exploring Interpretations and Implications of Coaches’ Use of Humour in Three National Paralympic Teams

2024· article· en· W4401688888 on OpenAlexaff
Danielle Alexander-Urquhart, Marte Bentzen, Göran Kenttä, Gordon A. Bloom

Bibliographic record

VenueInternational Sport Coaching Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsPsychologySociology

Abstract

fetched live from OpenAlex

The purpose of this study was to explore interpretations and implications of head coaches’ use of humour in three national Paralympic teams from the perspective of athletes and integrated support staff. We conducted six focus groups with 19 Paralympic athletes and individual interviews with 10 support staff members across the teams. Our reflexive thematic analysis resulted in two overarching themes that helped us understand how humour influenced feelings of psychological safety in the team environment, as well as considerations or challenges with using humour as a coaching strategy, including miscommunication or misunderstanding. Relational awareness, emotional intelligence, and effective communication were identified as important coaching competencies to consider when implementing humour as a leadership behaviour, particularly in an environment where power differentials of status and disability were present. The study was among the first to explore interpretations and implications of humour as a coaching strategy from athletes and staff in the high-performance parasport context. Coaches who implement humour within their environments are encouraged to reflect on the receivers of the interaction and how to maximise the facilitative rather than debilitative functions of humour as a “double-edged sword” to ultimately promote team satisfaction, well-being, and success.

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 score0.082
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.156
GPT teacher head0.383
Teacher spread0.227 · 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
Published2024
Admission routes1
Has abstractyes

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