Examining the impact of COVID-19 on sport coaches
Bibliographic record
Abstract
To-date, the impact of the COVID-19 pandemic on athletes' experiences has been examined, however, there remains a lack of attention examining the impact of the pandemic on coaches. The purpose of this study therefore was to examine Ontario sport coaches' perspectives of the implications of the COVID-19 pandemic on their experiences. As part of a large-scale survey of Ontario coaches' experiences in sport, an open-ended question was asked regarding the implications of COVID-19 on the coaching population. In total, 591 participant responses were analyzed using thematic analysis. According to participants, most of the cited outcomes of COVID-19 were negative, although some positive aspects were cited. Negative outcomes of the pandemic included adapting coaching methods and practices, insufficient coach supports, declines in coaching confidence and skills, lack of meaningful interpersonal connections, mental health concerns, job and financial instability, unclear guidelines on safe returns to sport, and loss of athletes and athletic programs. Conversely, positive impacts included providing time to reflect on their coaching pursuits and alternative interests and to engage in professional development. These findings highlight the importance of understanding coaches' experiences during the COVID-19 pandemic and may be used to inform recommendations for supporting coaches post-COVID.Acknowledgments: The authors would like to thank the coaches who participated in this study along with Coaches Association of Ontario who contributed to the design and recruitment of this study. The authors would also like to acknowledge Alexia Tam for her assistance with survey development and data collection.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".