Employing the CLAS-GRS to Stimulate Coach Self-Reflection and Interpersonal Development
Bibliographic record
Abstract
Building on the growing body of research highlighting the critical role of coaches in shaping youth athletes' experiences and developmental outcomes (e.g., Côté et al., 2022; Dorsch et al., 2020; Jowett, 2017), previous studies have underscored the importance of positive coach-athlete relationships (i.e., interpersonal interactions), linking them to psychosocial benefits; such as improved social skills, team cohesion (Evans et al., 2015; Fraser-Thomas et al., 2005), and intrinsic motivation (Adie & Jowett, 2010). Previous literature also highlights that reflective practice enables coaches to critically evaluate their experiences, leading to improved decision-making, enhanced interpersonal relationships, and greater adaptability in various sporting contexts (e.g., Nelson & Cushion, 2006; Whitehead et al., 2016; Winfield et al., 2013). Despite such benefits, extant literature exhibits that traditional coach education programs often overemphasize professional knowledge (i.e., technical and tactical skills) at the expense of interpersonal and intrapersonal development, which are equally vital for effective coaching (Côté & Gilbert, 2009). Further, the pre-brief – evaluation - debrief model, widely used in fields such as medicine (e.g., Zhou et al., 2021) and education (Ministry of Education, 2022), serves as a framework for structured reflection that enhances performance and learning. This model has been increasingly recognized in sport as a valuable tool for pedagogical certification (Coaching Association of Canada, 2016). This study integrates the Coach Leadership Assessment System – Global Rating Scale (CLAS-GRS; Lefebvre et al., 2023), a method designed to facilitate self-reflection, and observe and enhance coaches' interpersonal skills, into a novel coach reflection tool (i.e., the Coach Leadership Reflection; CLR). The tool incorporates structured reflective practices, including pre-briefing, in-situ observation, and debriefing sessions, along with the use of reflection cards. Consequently, this study explores how the Coach Leadership Reflection (i.e., CLR) tool can stimulate critical reflection and foster coaches' interpersonal and intrapersonal growth. Specifically, the current research aimed to (a) investigate the effectiveness of a coach reflection tool (i.e., Coach Leadership Reflection), and (b) gain insight into coaches’ experiences engaging in interpersonal and intrapersonal development opportunities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".