Teammate efficacy and teammate trust: An examination of teammate dynamics in volleyball defense
Bibliographic record
Abstract
Very little research in sport has examined the constructs of teammate efficacy and teammate trust. Past research in the counseling and organization psychology areas suggest that efficacy in other group members (Lent & Lopez, 2002) and trust in teammates (Dirks, 1999) could have a significant impact on team performance and other group dynamic constructs. The purpose of this study was to examine the constructs of teammate efficacy, its relationship to self-efficacy, collective efficacy and team performance, and to examine teammate trust as a potential moderator between teammate efficacy and team performance. Eighteen girl's club volleyball teams were studied across an entire competitive season answering questionnaires of efficacy, trust and backing-up behaviours at three time points during their season. The relationship between collective efficacy and teammate efficacy became stronger over the competitive season. Teammate efficacy was not a significant predictor of team performance. Teammate efficacy and teammate trust were highly correlated and therefore a moderating relationship between these variables to predict team performance was not tested. Decreased teammate trust significantly predicted increased feelings of having to back-up or cover for teammates while accounting for collective efficacy. Findings from this study contribute to a better understanding of these constructs in the sporting arena and offer a starting point for future research in this area.
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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 | 0.001 |
| 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".