Investigating the combination of a self-modeling intervention with psychological skills training on gymnasts' competitive performance
Bibliographic record
Abstract
Self-modeling (SM) involves an observer viewing oneself on an edited video showing desired behaviors (Dowrick, & Dove, 1990). Researchers have explored the impact of SM in the motor learning context (Clark, & Ste-Marie, 2006) however few researchers have investigated SM in competition (Ste-Marie, Rymal, Vertes, & Martini, 2009). Also, the combination of SM and psychological skills training (PST) on competitive performance has yet to be explored. The purpose of this research was to investigate whether a SM video combined with PST could enhance competitive performance. Eighteen gymnasts were divided into two groups; SM+PST (n=10) and SM (n =8). The SM+PST took part in workshops one month prior to the competitions wherein links between SM and psychological skills were made. The SM group did not do the workshops. Gymnasts competed at four competitions; two received the SM video and two did not. For the video competitions, participants viewed their video three times prior to warm-up and once before competing. A significant main effect for time was obtained, F(1,16)=11.57, p < .05, indicating that gymnasts' performance increased later in the season. Although the scores later in the season were higher when they received a SM video (M = 12.60, SD = 0.89) than when they did not (M = 12.32, SD = 1.10), this was not significant. Also, no group differences were found (SM+PST, M = 12.45, SD = 1.21; SM, M = 12.13, SD = 0.90). The strengths, limitations and implications will be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".