Measuring athletes' self-efficacy for deliberate practice: Initial development and analyses
Bibliographic record
Abstract
Research has substantiated the role of deliberate practice (DP) in the acquisition of expertise but none has considered psychological characteristics enabling athletes to navigate Ericsson et al.'s (1993) three constraints to DP (Baker & Young, 2014). This study examined the initial factorability and concurrent validity of items assessing self-efficacy to navigate resource (6 items), effort (3 items) and motivational (3 items) constraints. Sixty-six athletes (30 m, 36 f, ages 13-17), from six (regional to international) performance levels, also provided amounts of DP and pertinent concurrent measures. A 3-constraint factor model, CFI = .93, SRMR = .06, RMSEA = .08 (90% CI .03 - .12) showed superior fit to a 1-factor model, CFI = .68, SRMR = .12, RMSEA = .17 (90% CI .14 - .20). Pearson correlations (partialling out age) between each scale and performance level, DP and physical preparation were non-significant, ps > .17. However, resource efficacy correlated with future sport self (r = .25, p = .04) suggesting athletes who are more efficacious practicing without resources more strongly foresee themselves as reaching the upper echelon of sport in the long-term. Further, effort efficacy correlated with consideration of future consequences (r = .36, p = .003) suggesting athletes who are more efficacious practicing when they are fatigued/lack energy report a greater focus on the long-term consequences of their training. Partialling out performance level, older athletes reported higher motivation efficacy (r = .26, p = .04) meaning they have greater efficacy to practice even when unmotivated or not improving.Acknowledgments: This research was funded by a grant from the Research Development Program and the Faculty of Health Sciences at University of Ottawa.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".