The self-efficacy-performance relationship in a continuous sport task
Bibliographic record
Abstract
Research has suggested that the efficacy-performance relationship is reciprocal over seasons (e.g., Myers, Payment, & Feltz, 2004) and across trials (e.g., Feltz, Chow, & Hepler, 2008). However, research on this relationship within continuous sport tasks has not supported a reciprocal relationship (LaForge & Sullivan, 2010). The purpose of the present study was to examine the efficacy-performance relationship within an uninterrupted basketball task. Sixty-three participants (38 male, 25 female) were timed while dribbling a basketball around a series of pylons and simultaneously responding to a self-efficacy measure. Path analyses using residualized scores revealed a reciprocal relationship between efficacy and timed performance, with self-efficacy (p < .01; ?s ranging -.18 to -.47) and previous performance in the task (p < .01; ?s ranging -.85 to -.95) shown to be consistent predictors of present performance. Past performance (p < .05; ?s ranging -.24 to -.39) and past efficacy (p < .01; ?s ranging -.43 to -.62) were also predictors of self-efficacy beliefs. Consistent with previous research (i.e., Feltz et al., 2008), these findings suggest that the efficacy-performance relationship is reciprocal within a continuous sport task. The present results extend prior knowledge that, even within short temporal periods (i.e., uninterrupted trials less than one minute in length), previous performance and self-efficacy are strong and consistent predictors of sport performance.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".