Learning From Practice: The Value of a Personal Learning Coach for High-Performance Coaches
Bibliographic record
Abstract
Multiple actors and roles are now recognized and promoted to support the development of coaches. Personal coaching is an emerging industry in many professional fields yet remains insignificant in sport coaching. The purpose of this study was to document and assess the value of a 12-month collaborative action research in which a high-performance rugby coach, with the support of a personal learning coach, aimed to learn from her coaching practice. This research was operationalized using an appreciative inquiry framework. Personal coaching was conducted according to the principles of narrative-collaborative coaching. Data collection included interviews, video observation, audio recordings of coaching conversations, notes from phone calls, and email exchanges. Results showed that this partnership created a safe and challenging learning space where different coaching topics were addressed, such as reflective practice, leadership, and mental preparation. A deductive analysis of the debriefing interview was completed using the value creation framework developed by Wenger and colleagues. This analysis indicated that the high-performance coach’s relationship with the personal learning coach enabled the development of five types of value: immediate, potential, applied, realised, and transformative. Therefore, it is suggested that narrative-collaborative coaching can complement existing formal and non-formal learning activities.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".