“They learn quickly there are consequences for their actions”: youth sport coaches’ perspectives on the effects of benching
Bibliographic record
Abstract
Coaches are critical figures who impact youth’s sport experiences, in part through their behavioural management methods. Benching, or the removal of playing time, is a method coaches use to manage athlete and/or team behavioural transgressions. Although athletes interpret benching as a form of punishment associated with detrimental effects, coaches’ perspectives on the effects of this practice remain unexplored. Therefore, this study explored the perceived effects of benching as a behavioural management strategy from the perspectives of youth sport coaches. Semi-structured interviews were conducted with 14 youth coaches (eight men and six women) and data were analysed using reflexive thematic analysis. The coaches cited both beneficial and detrimental effects of benching for athletes’ personal experiences and team dynamics but only negative effects for the coach–athlete dynamic. The coaches’ responses suggest that the potential detrimental outcomes associated with benching were short-term and resulted from athletes failing to understand the purpose of benching or from an overemphasis on winning. As coaches and athletes may share differing views on the effects of benching as a behavioural management strategy, future research is needed to explore ways in which benching may be used in a manner that best optimises youth sport experiences.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".