What happened out there!? And other (potentially uncomfortable) topics of discussion. female volleyball players' experience of postgame debriefs
Bibliographic record
Abstract
Debriefing has become an increasingly common practice in sport (Hogg, 2002; Macquet, et al., 2015; McArdle et al., 2010). For the purpose of this study, debriefing is defined as discussion between the coach and athlete that is conducted after competition, with the aim of achieving positive changes and improvements in the following competition (Macquet et al., 2015). While postgame debriefing in sport has been purported to have numerous positive outcomes (e.g., aid learning, increase psychological recovery; Hogg, 1998; 2002), it is crucial to examine the athletes' perceptions of this process in order to examine its efficacy. To this end, nine female varsity level (USport) volleyball players participated in semi-structured interviews aimed at uncovering their thoughts and feelings related to postgame debriefs. Athletes identified two dominant themes and objectives that they want from the debriefing process: (a) communication, and (b) the desire for personal growth and learning. Athletes seek the opportunity to share thoughts/feelings about the game and engage in two-way communication with their coach. Eight of the nine participants reported wanting feedback about how they can improve as an athlete and be more effective on the court. These findings suggest that athletes do see value in post-game debriefing but the effectiveness of this practice is dependent on a number of factors. For example, the coach's ability to facilitate and teach the athletes was one of the biggest factors in whether or not athletes found a debrief to be effective.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".