Exploring Covert Team Dynamics in High Performance Sport, A Systems Psychodynamic Perspective
Bibliographic record
Abstract
Maltreatment in sport, encompassing physical, emotional, and sexual harm, has garnered significant attention due to research advancements and high-profile abuse cases (Parent & Fortier, 2018). Athlete safety is often seen as an individual issue, blaming harmful behaviors on a few problematic coaches while overlooking broader systemic factors sustaining harmful cultures in elite sport (Mountjoy et al., 2016). Research by Monton et al. (2024) used the Masculinity Contest Culture (MCC) framework to explore the high-performance sport environments, which were marked by intense competition and traditionally masculine traits, prioritizing performance above all else, including, well-being, health and safety (Monton et al., 2024). This study extends Monton et al.'s (2024) findings by investigating covert team-level processes that sustain toxic organizational dynamics using the X-Ray Vision model (Noumair et al., 2017). Through a secondary analysis of qualitative interview data (n=30), consisting of retired Canadian national team athletes, three key dynamics were identified. Covert team-level dynamics, included fear-based authority, dysfunctional interpersonal behaviors, and tacit motivational contracts, which fostered control, dependency, and tension, leading to isolation, exhaustion, and fragmented team cohesion. The results indicated that these patterns undermined athletes’ well-being and performance, highlighting the need for systemic intervention to address toxic team cultures in elite 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".