A Coach’s Self-Initiated Professional Development Across a Ski Season: How Mentorship and Adult Athletes’ Input Supported the Use of Adult-Oriented Coaching Practices
Bibliographic record
Abstract
Coaches can use the Adult-Oriented Sport Coaching Survey (AOSCS; Rathwell et al., 2020) to reflect on their psychosocial coaching approaches with Masters athletes (MAs). This study explored how a coach used the AOSCS alongside a mentor for ongoing adult-oriented coach development. Grounded in a pragmatic paradigm, an inductive sequential mixed-method design was used. Across a 10-week season, a Masters alpine ski coach (with 5 years’ experience) worked with a mentor (with 18 years’ experience as an alpine ski coach developer). They met at three timepoints (pre-season, mid-season, post-season) to debrief on the coach’s personal AOSCS scores and the AOSCS scores from their 10 MAs (M = 52.30; 60% male). Debriefs were recorded and a reflexive thematic analysis was performed. Pearson’s correlations tested relationships across timepoints, preferences, and frequencies using MA’s AOSCS scores. Results indicated convergence between use and preferences of coaching, which were reviewed and refined to the strongest nine practices. Significant correlations with large effects (p < .05; r > .63) between MAs’ scores validated the use of the nine AOSCS coaching practices. Debrief result showed the coach used AOSCS scores to reflect on strategies to implement at specific timepoints. At pre-season, the coach created strategies that contextualized MAs’ needs. At mid-season, the coach assessed his performance and created skill development strategies. At post-season, the coach reflected on how his strategies supported his MAs. This study demonstrated how a coach reflected on AOSCS scores and then implemented tangible improvement strategies that were identified by his MAs and himself.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".