Coach Mentorship Using the Adult-Oriented Sport Coaching Survey
Bibliographic record
Abstract
The Adult-Oriented Sport Coaching Survey (AOSCS) is an evidence-based assessment tool for coaches that stimulates self-reflection and learning of adult-oriented coaching practices. The AOSCS, comprised of 22 items that form five coaching themes, was used in this mixed-methods case study by a coach of adult skiers, who sought facilitation and mentorship from a coach developer to apply the AOSCS to strategically improve his coaching across a season. A pragmatic paradigm was adopted for this mixed-methods qualitatively dominant, sequential study. The coach and his skiers ( n = 10) completed the AOSCS at three points in time (pre-, mid-, and postseason). At each point, the coach journaled prior to debriefing with the coach developer for 20–50 min. Through reflexive thematic and correlation analyses, eight adult-oriented coaching practices were identified as strong points of integration. The findings indicated that working with the coach developer enabled the coach’s reflection of the AOSCS scores to refine, strategize, and align coaching with his adult skiers’ perceptions for adult-oriented coaching. This study outlined the facilitation of reflection on AOSCS scores can be used advantageously across a season to promote more deliberate coach learning and practice.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".