Learning how to coach: the different learning situations reported by youth ice hockey coaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Large-scale coach education programs have been developed in many countries around the world to help prepare coaches for their important role. Coaches have said that they also learn to coach from experience, starting from when they were young athletes until their current coaching positions. Finally, in the last decade, Internet resources have begun to be promoted as valuable tools for learning. Most of the studies on coaches' development have focused on only one of these three ways of learning how to coach. Purpose: To explore the different learning situations in which youth ice hockey coaches learn to coach. Participants: 35 volunteer youth ice hockey coaches from five minor hockey associations in the province of Ontario, Canada. Data collection: Coaches were interviewed individually using a semi-structured interview guide. The questions asked to coaches were about their learning through formal large-scale coach education programs, their learning experiences outside of these programs starting when they were young athletes until their actual head coaching positions, and their use of the Internet. Data analysis: The first part of the interview consisted of specific questions regarding the number of years coaches had played and/or coached hockey and their level of coaching certification. The answers to these questions were entered directly on a form and entered later into Microsoft Excel to perform descriptive statistical tests. The second part of the interview involved more in-depth questions regarding what learning opportunities contributed to their development as a coach. Finally, questions were asked regarding how they use the Internet in their coaching. The content of this second part of the interview was transcribed verbatim into Microsoft Word rich text format for further data analysis using Nvivo software (Qualitative Solution Research, 2002). Findings: The results revealed seven different learning situations including (a) large-scale coach education programs, (b) coaching clinics/seminars, (c) formal mentoring, (d) books/videotapes, (e) personal experiences related to sport, family, and work, (f) face-to-face interactions with other coaches, and (g) the Internet. Conclusion: Considering that coaches learn to coach through many learning situations, it would be inappropriate to discriminate against any of these situations, since each situation seems to have a unique role in the development of a coach. Therefore, it may be concluded that coach education should include a combination of all seven learning situations, instead of focusing on one. Future research should concentrate on investigating the complementary potential of these situations and what can be done to make each of these situations more appealing to coaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it