Motivational characteristics of coach behaviors and their effect on stress: A self-determination approach
Bibliographic record
Abstract
Research in sport highlights the importance of understanding the motivation of coaches. However, little is known about the effect different coaching tasks have on coaches' motivation and the subsequent influence on coaches' perceptions of stress. As such, the purpose of this study was twofold: (a) To examine the effect of two distinct types of coaching tasks, performance enhancement (PET) and administrative tasks (AT), on the motivation of coaches, and; (b) To assess how the tasks and the motivational characteristics of the tasks contributed to perceived stress. PET and AT were examined through the lens of Self-determination theory (SDT) and in particular Basic Psychological Needs Theory. Data were collected through an online survey, completed by 819 coaches working with college, university, Canada games, and nationally identified athletes. The findings indicate that coaches feel in control of PET but their autonomy and satisfaction with regards to AT was somewhat lower. The data further suggested that time conflict between AT and PET contributed to stress. The results suggest that stronger job structure supporting both AT and PET would reduce coach stress due to time conflict. Sport organizations may wish to use the information to create structures that better meet the psychological needs of coaches.Acknowledgments: Coaching Association 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".