Successful High-Performance Ice Hockey Coaches’ Intermission Routines and Situational Factors That Guide Implementation
Bibliographic record
Abstract
Competitions in many team sports include short breaks (e.g., intermissions, halftime) where coaches have a unique opportunity to make tactical adjustments and communicate with athletes as a group. Although these breaks are significant coaching moments, very little is known about what successful coaches do during this time. The purpose of this study was to examine intermission routines and knowledge of highly experienced and successful National Collegiate Athletic Association (NCAA) ice hockey coaches. A thematic analysis was used to analyze semistructured and stimulated-recall interview data. Results revealed that coaching during intermissions was a continuous process influenced by the coaches’ history and personal characteristics. Drawing on these factors, the coaches created an intermission routine that guided them as they analyzed unpredictable situational factors such as their team’s performance and the athletes’ emotional state. Overall, the results offer a rare glimpse into the intermission strategies of successful coaches in a high-performance setting.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".