Effectiveness of an online acceptance and commitment therapy programme for perfectionism in soccer players: A randomized control trial.
Bibliographic record
Abstract
There is currently limited understanding of how to reduce perfectionism in sport. With research outside of sport as impetus, in the current study we evaluated the effectiveness of an online ACT-based intervention for reducing perfectionism and improving pre-competition emotions in soccer players. Following a pre-registered protocol, eighty-one female soccer players (M age = 24.28 years, SD = 6.77) were randomly allocated to either an intervention group (n = 41) or a waitlist control group (n = 40). The intervention group had access to a set of online ACT-based modules for 8-weeks. Athletes completed measures of trait perfectionism, perfectionism cognitions, and pre-competition emotions pre-intervention and post-intervention. A 2 (group) x 2 (time) ANOVA revealed significant interaction effects for trait perfectionism, perfectionism cognitions, and pre-competition emotions. Following the intervention, the two groups displayed significant mean differences for trait perfectionism, perfectionism cognitions, and almost all pre-competition emotions. However, due to lower reliability of some instruments, findings regarding post-competition emotions were discounted. The findings suggest that online ACT-based interventions may be a viable and effective way to reduce perfectionism in soccer players (but not necessarily improving pre-competition emotions).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".