Gymnasts' self-regulatory processes and beliefs in competition: Examining the impact of a feedforward self-modeling video
Bibliographic record
Abstract
Self-regulation is thought to be an important tool in athletic success (Zimmerman, 2008). Furthermore, researchers have acknowledged such processes and beliefs occur following the use of a feedforward self-modeling video (FF-SM) during competition (e.g., Rymal et al., 2010). The current study also examined self-regulation, but with a specific interest in how self-modeling alone, or paired with psychological skills training, was used as a strategy to influence self-regulation. Ten gymnasts were divided into two groups; Group 1 took part in a four week psychological skills training workshop involving a FF-SM video and received the video during competition; Group 2 only received the FF-SM video during competition. Immediately following a competition in which gymnasts viewed their FF-SM video, they were asked a set of interview questions developed based on Zimmerman's (2000) model of self-regulation specific to the use of the video. A content analysis of the transcripts suggested that the video was used as a task analysis strategy to influence processes (e.g., imagery) and beliefs (e.g., self-efficacy) that occur in all phases described by Zimmerman; i.e., before, during, and after the competitive event. Differences in gymnasts' self-regulation, however, were not evident between the groups. This suggests that the video, rather than the psychological skills training, was being used as a stagey to influence gymnasts' self-regulation. Supported by SSHRC.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".