Dyadic trust in sport: a theoretical and integrative review
Bibliographic record
Abstract
There is a widely held assumption within sport that building trust – among athletes, coaches, or support staff – is critical for achieving individual and team success as well as promoting member well-being. Despite this notion, limited attention in the field of sport psychology has been devoted to the theoretical bases that underpin trust with regard to its conceptual features, antecedents, and consequences. The purpose of this paper is to conduct an integrative review culminating in a theoretical framework on dyadic trust in sport. We draw from the extant trust literatures across a range of disciplines (e.g. organizational, social psychology), embracing the strengths of existing approaches while addressing their conceptual and operational limitations, to advance a reconstituted multidimensional conceptualization of trust. Within our reformulated framework, we consider trust to be a psychological state that is performance and/or person-oriented. Further, trust is composed of expectations (i.e. instrumental and affective) and the intention to accept vulnerability (i.e. reliance and disclosure). In addition, we propose putative inputs to, and outputs of, trust. This framework can be used to guide the development of trust-related research questionsand provides a road map for high-quality science centered on the construct of trust within sport.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".