Emotional self regulation, emotion regulation of others, and peer motivational climate: A multilevel analysis predicting adolescent athletes' sport enjoyment and commitment
Bibliographic record
Abstract
Athletes’ emotional self-regulation is valuable for performance, and athletes may also regulate teammates’ emotions for social and performance purposes (Jones, 2012). This study examined athletes’ emotional self-regulation (ERS-Improve and ERS-Worsen) and regulation of others’ emotions (ERO-Improve and ERO-Worsen) and associations with peer climate, sport enjoyment, and commitment. Athletes (N = 451, M age = 16.3, SD = 1.0) from 38 teams completed Emotional Regulation of Others and Self scale (Niven et al., 2011), a measure of sport enjoyment and commitment (Scanlan et al., 1993), and the Peer Motivational Climate in Youth Sport Questionnaire (Ntoumanis & Vazou, 2005). Multilevel analyses were conducted to predict athletes’ sport enjoyment and commitment as a function of their emotional self-regulation (level 1) and the team’s emotion regulation of others and peer climate (level 2). At level 1, athletes’ higher ERS-Improve was associated with higher commitment and enjoyment, and lower ERS-Worsen was also associated with higher enjoyment. At level 2, higher team perceptions of task climate predicted athletes’ higher enjoyment scores, while team-level task climate, ego climate, ERO-Improve and ERO-Worsen all predicted athletes’ commitment. There was a significant negative cross-level interaction for the negative relationship between athletes’ ERS-worsen with enjoyment, such that the relationship was stronger in teams with a lower average ego climate score. There was a significant negative cross-level interaction for the positive relationship between athletes’ ERS-Improve with commitment such that the relationship was stronger in teams with a lower average Task Climate score. Findings suggest that individual self-regulation of emotion and the team-level task climate influenced enjoyment. Athletes’ self-regulation as well as the team’s interpersonal emotion regulation and peer climate all influenced athletes’ commitment. The strength of the associations between athletes’ self-regulation with sport enjoyment and commitment depended on the task and ego motivational climates of the team.Acknowledgments: This research was supported by a SSHRC Postdoctoral Fellowship awarded to the first author.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".