Motivation to train during a pandemic: The role of fitness resources, mental health, and motivational profiles among student-athletes in team sports
Bibliographic record
Abstract
The sporting season across post-secondary institutions was canceled in March 2020 due to COVID-19, and student-athletes had to maintain their training at home. It is unclear what personal and contextual factors facilitated student-athletes' ability to maintain their training routines at home when social distancing and lockdown (SD/L) policies were put in place. Our cross-sectional study of 433 student-athletes examined (a) how athletes adapted their training, (b) what training barriers they experienced, (c) whether motivational profiles were associated with differences in training behaviors and mental health, and (d) what variables predicted athletes' motivation to train during this prolonged offseason. Student-athletes across Canada were recruited to complete an online survey between August and September 2020. Results showed that athletes significantly reduced their training load and intensity, with approximately 25% exercising two or fewer days a week. Barriers to training included limited access to fitness resources and equipment, having inconsistent training schedules, and experiencing emotional distractions, with some of these barriers more common among female athletes than male athletes. For motivation profiles, athletes with higher levels of intrinsic motivation tended to maintain the intensity of their workouts and experienced lower mood disturbance. A hierarchical multiple regression revealed that being male, being younger, having higher levels of intrinsic and introjected motivation, having access to fitness resources, maintaining a steady training schedule, having fewer emotional distractions, and lower mood disturbance were significant predictors to being motivated to train during the pandemic. We discuss strategies coaches and trainers can implement to best support their student-athletes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".