Longitudinal examination of interpersonal emotion regulation, social support, and team performance among varsity athletes
Bibliographic record
Abstract
Interpersonal emotion regulation (IER) refers to social interactions that are intended to improve or worsen the emotions of others (Niven et al., 2011), and IER may function as a mechanism of social support (Marroquin, 2011). Athletes' IER has been associated with emotional and motivational outcomes in sport (Tamminen et al., 2016); however, there is no research to date examining how IER and perceptions of social support or cohesion are associated with performance. The purpose of this research was to examine these associations among a sample of 110 varsity team sport athletes. Participants completed measures of perceived social support (Freeman et al., 2009), social cohesion (Eys et al., 2009), and athletes rated the extent to which they engaged in affect-improving or affect-worsening IER with teammates in the days prior to and following a competition. There were significant decreases in athletes' affect-worsening IER in the days leading up to competition, while affect-improving IER decreased significantly in the days following competition. Social support moderated pre-competition trajectories of receiving affect-worsening IER in predicting the outcome of the competition: for athletes who perceived more social support from teammates, receiving less affect-worsening IER before competition was predictive of the team winning their competition. Social cohesion did not moderate any of the associations between IER, time, and performance outcome. These results indicate that athletes' perceptions of social support as well as daily interpersonal emotion regulation interactions among teammates have implications for team performance.Acknowledgments: This research was supported by an Insight Development Grant from the Social Sciences and Humanities Research Council of Canada
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".