The acquisition of coaching knowledge of a unique sample of expert team sport coaches /
Bibliographic record
Abstract
The purpose of this study was to identify how coaches who have surpassed their athletic achievements acquired their coaching knowledge. Six University coaches from basketball, volleyball, and hockey, with a combined total of fourteen coach of the year awards were selected to participate. Each coach was coaching at a higher level (e.g., University level) than he had competed as an athlete, had a winning percentage greater than .500 at the University level, and had been the head coach at their current program for a minimum of five years. Semi structured, open-ended interviews were conducted using an interview guide created exclusively for this study and based on the tenets of Cote, Salmela, Trudel, Baria, and Russell's (1995) Coaching Model and Chelladurai's (1978) Multidimensional Model of Leadership. Data analysis followed the guidelines forwarded by Cote, Salmela, Baria, and Russell (1993). Results of this analysis revealed three higher order categories which indicated the path coaches had taken to reach their current positions including the many ways knowledge was acquired. These were (a) career path which discussed the journey of knowledge acquisition of these coaches, from their earliest sport participation to their current coaching position, (b) personal factors, which included how the coaches' journey of knowledge acquisition had been influenced by who the coaches were, and (c) coaching knowledge, which involved the participants' current level of coaching knowledge. Despite the idiosyncratic nature of each coach's career progression many common themes emerged, including the different ways knowledge was acquired, the coaches' personal characteristics, and the level of coaching knowledge accumulated. Many of the findings that emerged were similar to those highlighted in previous studies pertaining to expert coach development (e.g., Cregan, Bloom, & Reid, in press; Salmela, 1994; Schinke, Bloom, & Salmela, 1995) which suggests that while athletic experiences may be helpful in expert coach development, they are not essential. The results provided evidence that sources of knowledge acquisition are accessible to aspiring coaches to acquire the necessary coaching knowledge, regardless of their athletic background. The current findings could potentially enhance the quality and standard of formal coach education and training programs in Canada by illustrating how a unique sample of coaches acquired the knowledge to achieve success at the university level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".