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Record W7162013680 · doi:10.82308/35599

Coach leadership experiences in the management of difficult athletes

2017· dissertation· en· W7162013680 on OpenAlexaboutno aff
William Heelis

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingAthletesThematic analysisLeagueNarrativeEmpirical researchSport psychology

Abstract

fetched live from OpenAlex

Coaching research has most often focused on positive coach and athlete behaviours and outcomes. However, less empirical attention has highlighted negative, problematic, and difficult athlete behaviours. Specifically, managing difficult athletes is part of a coach's role and responsibilities, so it is somewhat surprising that there is minimal research on the topic. Thus, the purpose of the present study was to investigate high performance coaches' experiences with difficult athletes, including how they effectively managed these individuals. Semi-structured interviews were conducted with eight Canadian Hockey League (CHL) coaches, who had an average of 21 years of coaching experience. The methodology of transcendental phenomenology (Moustakas, 1994) was used to better understand what difficult athletes were and how coaches managed these individuals by combining the strengths of thematic analysis with individual narrative accounts. The results indicated the coaches' common experiences with difficult athlete through five overarching themes: (a) instilling team culture, (a) difficult athlete characteristics, (c) fostering relationships, (d) managing difficult athletes, and (d) social influences and resources of difficult athletes. Specifically, difficult athletes were described as "negative star players" and "negative leaders" within the team, where they had a negative influence on teammates and impacted proper team functioning. The narrative accounts described that coaches learned how to manage difficult athletes through their personal experiences with them. The findings suggest that managing difficult athletes involves early identification, providing clear roles and expectations, enforcing consequences, and making progress through process goals to learn from mistakes. Coaches either transformed the difficult athlete behaviour by having them buy-in to team concepts or they were unable to make progress with them, which then led to the athlete being traded or deselected. The themes and narratives were synthesized to create the essence of the experience, which highlighted the coaches' commitment to athlete development by utilizing all of the resources at their disposal (e.g., assistant coaches, trainers, athlete leaders, billets). From a practical standpoint, this study provides insights for coaches, athletes, athletic directors, and general managers by highlighting the dynamic processes necessary to manage difficult athletes within an organization. As well, this study offers methodological implications for the application of transcendental phenomenology in the coaching sciences as an effective and systematic approach, along with theoretical implications for leader-member exchange theory within sport and group dynamics research.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.385
Teacher spread0.271 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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