DISENTANGLING THE COMPLEXITIES OF TRUST: AN EMERGING LINE OF INQUIRY IN THE CONTEXT OF SPORT
Bibliographic record
Abstract
Consensus within the sport psychology literature is that the attainment of task-related objectives should never come at the expense of athlete welfare (Brown & Arnold, 2019). As such, researchers have begun to examine factors that best promote the achievement of high levels of performance and well-being simultaneously—an experience termed athlete thriving (Brown et al., 2021). One construct that has received minimal attention in sport but has been found to promote the aforementioned outcomes across team contexts is interpersonal trust (herein referred to as trust). Extensive research across the fields of organizational, developmental, and social psychology describes trust as a key ingredient for the achievement of performance (e.g., enhanced perceptions of cohesion; de Jong et al., 2016) and well-being outcomes (e.g., engagement in help-seeking behaviours; Rickwood et al., 2005). When considering these implications alongside thriving, trust appears to be a relevant yet overlooked construct within sport. This dissertation includes three studies that aim to address this gap by advancing a targeted line of inquiry on trust generally, and in sport specifically. Study 1 involved the consolidation and evaluation of existing trust research across performance-oriented team contexts. Using a two-phase citation network analysis (CNA) and critical review process, a strong understanding of the conceptual underpinnings of trust was achieved. Benefiting from Study 1 findings, Study 2 involved the direct exploration of Canadian interuniversity athletes’ (n = 13) experiences with trust in interdependent sport teams. As a result, a key feature as well as precursors, facilitators, and outcomes of trust were identified. Additionally, athletes emphasized the important role that coaches played in supporting trust development. Thus, Study 3 sought to broaden our understanding of trust through the inclusion of Canadian interuniversity coach perspectives (n = 18). Using a narrative inquiry approach, the analysis process identified three narratives that coaches drew from when sharing their stories about building trust. Altogether, by enhancing our understanding of what trust is and how it is developed in sport teams, athletes and coaches alike could be better equipped to foster sport environments most conducive to developing trusting relationships—thereby enabling athletes to thrive.
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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 | 0.079 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.028 | 0.045 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".