Development and validation of a trust in sport questionnaire
Bibliographic record
Abstract
Many professional athletes and coaches have cited great team chemistry as an essential factor in producing a winning team and research has shown there are several team variables that positively contribute to team performance (Beauchamp & Eys, 2014). One variable that has lacked considerable research within sport group dynamics literature is trust. Organizational trust research has provided the impetus for trust research to move into the world of sport. The purpose of this research is to develop a sport-specific measure of trust. Procedure followed a typical scale development parameter: 1) an understanding was developed through a comprehensive literature review of trust in sport and organizational science; 2) items were generated, a comprehensive list of items were developed and narrowed through an expert review process and think aloud protocol; and 3) a confirmatory factor analysis was conducted to identify the factor structure of the questionnaire based on participant's responses. Validity and reliability were also tested for. The initial pool items were refined through experts reviews and a Think Aloud Protocol. These steps refined the questionnaire and moved research into the next phase. The factor structure of the Trust in Sport Questionnaire was confirmed and validated. The development of a definition and measure of trust in sport that will allow future researchers to effectively assess trust within sport and examine how it relates to different sport variables.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".