Teammate efficacy and teammate trust in adolescent sport: A qualitative analysis
Bibliographic record
Abstract
Little research in sport has examined teammate efficacy (TE) and teammate trust (TT), however, research in other contexts suggest that efficacy in other group members (Lent & Lopez, 2002) and trust in teammates (Dirks, 1999) could have a significant impact on the team. The purpose of this study was to have a better understanding of how one defines TE and TT, their causes and outcomes. Twelve interviews were conducted with adolescent female volleyball players—interviews were transcribed verbatim. Hierarchical content analysis was conducted by two independent coders (Patton, 2008). Results indicated that although participants sometimes had difficulty differentiating between TE and TT, TE was often defined based on the teammate's abilities whereas TT was often defined based on the necessity to back-up a teammate or on more emotional aspects. Participants also stated that TE stemmed mostly from the effort (or lack thereof) put forth by teammates and that TT stemmed from how teammates performed in critical situations, the effort put forth and her experience. Participants expressed that TE affected performance in terms of focus, decision making, motivation, and backing-up behaviours whereas TT affected performance in terms of backing-up behaviours, focus, motivation, pressure and frustration. With regards to the development of TE and TT, participants generally indicated that both TE and TT improved over the course of the season, mostly due to the increase in familiarity and skill improvement of the teammates. Results of this study will be compared with quantitative results from the same sample and provide a foundation for future research in this area and context.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".