Building a successful program : perspectives of expert Canadian female coaches of team sports
Bibliographic record
Abstract
The purpose of this study was to assess the perceptions of expert coaches on the key elements for building their successful programs. Five female expert Canadian university coaches of team sports were individually interviewed with an open-ended approach. Data were analysed inductively, following the guidelines of Cote, Salmela, Baria, and Russell (1993) and of Cote, Salmela, and Russell (1995). The results of the analysis identified four key elements for the building of a successful program. First, coaches possessed a variety of personal attributes that enabled them to display appropriate leadership. Second, coaches possessed thorough organisational skills from which they set goals, planned the season, and prepared their team for games. Third, coaches had a personal desire to foster their players' individual growth, by empowering them and teaching them life skills. Finally, the aforementioned elements were interrelated and linked together by the coaches' vision, without which success was unlikely. Data also showed the correspondence of these four elements with a transformational leadership style that has been successfully used in business, military, industry, and educational settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".