Exploring Youth Sport Coaches’ Perspectives on the Use of Benching as a Behavioral Management Strategy
Bibliographic record
Abstract
The practice of benching players or removing playing time is commonly used in sport. Although benching is used to adhere to game rules related to the number of athletes permitted on the field of play at any given time or to provide athletes with rest breaks, athletes have reportedly experienced benching in response to behavioral infractions such as not paying attention, not devoting sufficient effort, or failing to adhere to team rules. The purpose of this study therefore was to explore the use of benching as a behavioral management strategy from the perspectives of youth coaches. Semistructured interviews were conducted with 10 youth coaches (six men and four women) regarding their views of benching, reasons for use, and alternatives to the practice of benching. Data were analyzed using inductive thematic analysis. All coaches reported using benching to manage athlete and team behavior, address conduct detrimental to the team, and reinforce the coach’s position of power. The coaches interpreted benching as punishment or a learning tool depending on the provision of communication and feedback. Future work is needed to address the use of communication and the nature of this communication to ensure that benching practices are associated with learning and not punishment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".