Exploring Interpretations and Implications of Coaches’ Use of Humour in Three National Paralympic Teams
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".