Better together? Examining correlates of teamwork in sport
Bibliographic record
Abstract
Although teamwork has been shown to be an important group variable across a range of team contexts, corresponding research within the context of sport has not yet been conducted. As such, the purpose of this study was to examine the relationships between various aspects of teamwork and several individual- and group-level variables within sport. A sample of 195 athletes from 19 sports teams completed the Multidimensional Assessment of Teamwork in Sport (MATS), which measures five aspects of teamwork. One month thereafter, participants completed measures of team cohesion, collective efficacy, satisfaction with team and individual performance, enjoyment in one's sport, and commitment to one's team. The correlations between each of the five aspects of teamwork with the six external variables were significant (p < .001). Large effect sizes were found for the correlations between teamwork and satisfaction with team performance, task cohesion, and collective efficacy. Medium effect sizes were shown with social cohesion. Small-to-medium effect sizes were evident with satisfaction with individual performance, commitment to one's team, and enjoyment in one's sport. The relationships between each aspect of teamwork and satisfaction with team performance were mediated by task cohesion, social cohesion, and collective efficacy. The relationships between four of five aspects of teamwork and satisfaction with individual performance was mediated by enjoyment and commitment. This research is the first to provide evidence of external validity related to the MATS. These findings suggest that teamwork is an important variable to consider within the context of sport.Acknowledgments: Funding: Canadian Graduate Scholarship from the Social Sciences and Humanities Research Council awarded to the first author
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".