Canadian major junior ice hockey coaches' perceptions regarding the identification, management, and transformation of difficult athletes
Bibliographic record
Abstract
Difficult athletes withhold effort, possess negative emotions, mistreat teammates, defy coaches, and break team rules (Cope, Eys, Schinke, & Bosselut, 2010). These difficult behaviours will disrupt team functioning if they are not identified and managed properly. Given that most coaches will encounter difficult athletes during their careers, it is surprising that little empirical attention has examined their role(s) in managing these individuals. The purpose of this study was to explore this topic from the perspective of expert Canadian Hockey League (CHL) coaches. Using a transcendental phenomenological approach (cf. Moustakas, 1994), we interviewed eight CHL head coaches and inductively analyzed the interview data. The results indicated that the management of difficult athletes involved a 6-step process: (1) early identification, (2) addressing concerns, (3) providing clear expectations and roles, (4) enforcing consequences, (5) making progress through process goals, and (6) transforming difficult behaviours. This presentation will explain how coaches can effectively manage difficult athletes, including the importance for coaches to develop their emotional intelligence to recognize and then manage difficult athletes—sooner rather than later. Moreover, these results point to the importance of coaches reaching out to athlete leaders, teammates, trainers, and billets to help transform difficult athlete behaviours. These findings have practical application for all coaches as well as other members of the sport environment, such as assistant coaches, general managers, and athletic directors so they can support head coaches in the management of difficult athletes.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".