Exploring Canadian Elite Female Youth Hockey Teams' Shared Leadership Through Coach and Athlete Leaders' Experiences
Bibliographic record
Abstract
Effective coach and athlete leadership is fundamental to optimal sport performance (Cotterill & Fransen, 2016). Guided by emerging frameworks (e.g., Fransen et al., 2014, 2017), this study explored shared coach and athlete leadership within Canadian elite youth female hockey teams. Fifteen coach and athlete-leader dyads (i.e., N=30) were purposefully sampled from youth female high-performance leagues (i.e., U18 AAA) for representation across Canada. Participants engaged (individually) in semi-structured interviews focused on their sport experiences, leadership approaches (e.g., implicit/explicit), and team outcomes (e.g., performance, positive youth development). Results emerged through four interconnected themes: (a) establishing a shared structure: collective collaboration (b) building a shared philosophy: ‘we before me’, (c) developing a shared foundation: caring and supporting, and (d) attaining shared goals: (re-) defining success. Findings advance understanding of shared coach and athlete leadership, offer practical implications to enhance leadership development, and provide insights for fostering healthy shared leadership models.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".