Next One Up! Exploring How Coaches Manage Team Dynamics Following Injury
Bibliographic record
Abstract
Injuries are commonplace in high-intensity sport, and research has explored how athletes are psychologically affected by such events. As injuries carry implications for the group environment in sport teams, the authors explored what occurs within a team during a time period of injury from a coach perspective and how high-performance coaches manage a group at this time. Semistructured interviews were conducted with 10 Canadian university basketball head coaches. Thematic analysis revealed four high-order themes in relation to how coaches managed group dynamics from the moment of the injury event to an athlete’s reintegration into the lineup. Strategies to mitigate the negative effects of injury on the group environment while prioritizing athlete well-being involved remaining stoic at the time of an injury event, maintaining the injured athlete’s sense of connection to the team, and coordinating with support staff throughout the recovery and reintegration process.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".