Exploring How the COVID-19 Pandemic Impacted the Coach–Athlete Relationship for Travel Sport Coaches: A Qualitative Study
Bibliographic record
Abstract
During the COVID-19 pandemic, maintaining the quality of the coach–athlete relationship (CAR) became a significant challenge for travel sport coaches. The first aim of this study was to explore the coaches’ perceptions of how the CAR’s characteristics (i.e., closeness, commitment, and complementarity) were affected by the COVID-19 pandemic. The second aim was to explore the differences before and during the COVID-19 pandemic in CAR quality from the coaches’ perspective. Fourteen travel/club coaches from Ontario took part in 90-min semistructured interviews. Generally, coaches believed that their closeness was maintained, commitment levels improved, and complementarity decreased within their CAR. Past relationships between the coach and athletes helped to maintain their closeness. The resiliency of athletes was thought to aid in the increased commitment. Lack of face-to-face interactions hampered complementarity. Three themes—barriers, variability, and benefits—emerged as differences within the CAR during the pandemic. Recommendations from this study are that coaches focus on the characteristic of complementarity to enhance their CAR quality following the COVID-19 pandemic.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".