Imagery and modeling influences on team sport athletes' collective efficacy
Bibliographic record
Abstract
Self-efficacy Theory identifies both imagery and modeling as important contributors to efficacy beliefs (Bandura, 1997). Research on use of these mental skills among athletes participating in team sport suggests mastery imagery (i.e., MG-M; Munroe-Chandler & Hall, 2004; Shearer et al., 2007) and use of observation-based interventions (Bruton et al., 2014) contribute to collective efficacy beliefs; however, the relative contribution of these mental skills is unknown. The purpose of this study was to explore whether use of the functions of imagery and modeling contribute to team sport athletes' collective efficacy beliefs. Athletes (n = 88; 60% female; M = 22.40 years, SD = 7.82) currently competing in team sports self-reported their use of the functions of imagery (SIQ-TS; Curtin et al., 2016) and modeling (FOLQ; Cumming et al., 2005) as well as their individual level perceptions of their team's collective efficacy (CEQS; Short et al., 2005) with respect to their primary sport. Regression analyses were conducted separately for all CEQS subscales (Ability, Effort, Preparation, Persistence, Unity) and total CEQS (R2adj = .13-.35, ps < .05). MS imagery alone significantly predicted persistence and unity beliefs, while MG-M imagery was also a significant contributor to ability, effort, preparation, and total collective efficacy beliefs. In addition, the strategy function of modeling significantly predicted both effort and preparation beliefs. The findings provide preliminary support for the relative contributions of imagery and modeling to collective efficacy beliefs. Discussion will focus on the theoretical and practical implications for developing mental skills interventions targeting collective efficacy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".