Athletes Perceived Level of Risk Associated with Botanical Food Supplement Use and Their Sources of Information
Bibliographic record
Abstract
Athletes should carefully consider the use of botanical food supplements (BFSs) given the current lack of substantiation for botanical nutrition and health claims under EU and UK food laws. In addition, athletes may be at an increased risk of doping violations and other adverse outcomes potentially associated with BFS use; however, little is known about athletes’ intake, knowledge, or perceptions in relation to BFS use. An online cross-sectional survey of n = 217 elite and amateur athletes living on the island of Ireland was conducted using Qualtrics XM to assess intake, knowledge, attitudes, and perceptions. General food supplements (FSs) were reported by approximately 60% of the study cohort, and 16% of the supplements reported were categorized as BFS. The most frequently consumed BFSs were turmeric/curcumin (14%), Ashwagandha (10%), and Beetroot extract (8%). A higher proportion of amateur athletes would source information about BFSs from less credible sources, such as fellow athletes, or from internet sources or their coach, compared to elite athletes. Those who sourced information about botanicals from fellow athletes (p = 0.03) or the internet (p = 0.02) reported a lower perceived level of risks associated with BFS use. This study therefore suggests that amateur athletes may be more likely to source information from less credible sources compared to elite athletes who may have more access to nutrition professionals and their knowledge/advice. This may have potential adverse implications for amateur athletes, e.g., Gaelic games players, who are included within the doping testing pool but who may not have access to evidence-based nutrition advice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".