Decoding unintentional doping: A complex systems analysis of supplement use in sport
Bibliographic record
Abstract
• This study has demonstrated the complex interactions between tasks, and the stakeholders associated with supplement use in sport in an Australian context. • This study has demonstrated that regulatory oversight, manufacturing, sale, and distribution processes are critical areas where substandard performance can potentially create conditions for unintentional doping downstream for athletes. • The findings indicate that for the prevention of unintentional doping through supplement use, policy interventions will need to shift away from the typical focus on athletes and their support personnel, to encompass a broader systemic focus. Unintentional doping though supplement use is an ongoing issue that has severe professional and personal impacts on athletes. Though the issue is well known, there are key knowledge gaps regarding the role of different stakeholders both in creating and managing unintentional doping. The current study aimed to identify the influential tasks and stakeholders within the Australian sport system that are associated with supplements. A Hierarchical Task Analysis (HTA) was developed during a subject matter expert workshop (n = 12) to decompose the supplement use in sport ‘system’ into a hierarchical structure of goals, sub-goals, operations, and plans. A task network was developed during the SME workshop and based on the first level sub-goals of the HTA. Network analysis was then applied to determine the interdependency and influence of system tasks and stakeholders. Network metrics included Density, Out-degree centrality, In-degree centrality, Betweenness centrality, Closeness centrality, and Eigenvector centrality. In total, 15 first level sub-goals were identified which were further decomposed into 71 sub-goals and operations. The overall identified goal of athletes taking supplements was to optimise health, performance, recovery, image, and achieve optimal weight. Within this overall goal, numerous tasks are required to be performed including research, manufacturing and regulation of supplements, maintaining clean sport, to the administration of supplements by athletes, to subsequent assessments of their efficacy. The most influential tasks within the system include ‘maintaining clean sport’ by anti-doping authorities, and ‘marketing/advertising’ of supplements by supplement companies. Influential stakeholders within the system included ‘anti-doping agencies’, ‘athlete support personnel’, and ‘sponsors’. The analysis has demonstrated that multiple and varied stakeholders have specific roles to play in preventing unintentional doping. The findings suggest that for the prevention of unintentional doping through supplement use, interventions will need to shift away from the typical focus on athletes and athlete support personnel, to encompass a broader systemic focus.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".