Exploring the utility of an online post event reflection tool in elite sport: A case study
Bibliographic record
Abstract
In elite sport environments, coaches and sport psychology practitioners work to optimize individual and team performance. One strategy is the use of athlete self-awareness, identified as a key to optimal sport performance (Ravizza & Fifer, 2014). The purpose of this study is to investigate the utility of a novel, online post-event reflection (PER) tool (Chow & Luzzero, 2019) used in the context of an elite sport team. Specifically, we sought to test the efficacy and effectiveness of the PER as a self-awareness tool to track individual and team outcomes over time (e.g., performance, mental skills). Participants included 21 members of a Canadian University Women's Ice Hockey Team who completed the PER following 24 games across one season. Athletes were provided a personalized summary of PER results at midseason and postseason during individual meetings with the head coach and mental performance coach, and could request a PER summary at any time during the season. In terms of efficacy, the PER provided tailored insight used as a basis for discussion with the athletes, complementing other objective, analytical performance-related feedback. As evidence for effectiveness of the PER, narrative visual examples will be presented to illustrate specific ways the PER was used, along with significant team-level associations between self-reported mental skills and overall performance (.49 ? r ? .91, ps < .001). We conclude the presentation with a series of recommendations for future applied practice in order to optimize the use of the novel online PER in the context of elite sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".