The relationship between perfectionism and athlete engagement: The moderating role of coach autonomy support
Bibliographic record
Abstract
Athlete engagement is a positive state of mind capturing athletes' feelings of enthusiasm, confidence, vigour, and dedication toward their sport (Lonsdale, Hodge, & Jackson, 2007). This study examined whether athletes with different perfectionism profiles differed across these engagement characteristics and tested whether those differences were moderated by coach autonomy support. A sample of 191 male youth club basketball and football players (Mage = 16.59, SD = 0.67) completed measures of athlete engagement, sport perfectionism, and coach autonomy support. Latent profile analysis was used to categorize participants according to their standings across perfectionistic strivings and perfectionistic concerns. A 3-class model was adopted with groups representing non-perfectionistic athletes, moderately perfectionistic athletes, and highly perfectionistic athletes. Multiple regression was then used to test for class differences and moderation effects (see Hayes & Montoya, 2017). Across each engagement characteristic, highly perfectionistic athletes reported higher levels in comparison to moderately perfectionistic athletes regardless of levels of coach autonomy support. On vigour and dedication, though, class differences involving non-perfectionistic athletes were moderated by coach autonomy support. For both characteristics, non-perfectionistic athletes reported lower levels than highly perfectionistic athletes when coach autonomy support was low. However, non-perfectionistic athletes and highly perfectionistic athletes did not differ across vigour and dedication when coach autonomy support was moderate-to-high. In discussion, we compare the adopted 3-class model with those produced in past research, address issues surrounding perfectionism functionality, and speculate as to why fostering autonomy support may have the greatest impact on engagement among athletes low, but not high, in perfectionism.
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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 | 0.007 |
| 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) | 0.002 | 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".