Education Interventions to Improve Knowledge, Beliefs, Intentions and Practices with Respect to Dietary Supplements and Doping Substances: A Narrative Review
Bibliographic record
Abstract
The misuse of dietary supplements and doping substances is commonly associated with toxicity, nutritional imbalances, and health and psychological consequences. This is alarming especially in light of the increasing prevalence of the use of dietary supplements and doping, particularly among young adults including athletes. There is evidence that education interventions can lead to improved knowledge, intentions, and practices. However, no review has summarized and evaluated the effectiveness of such interventions. The aim of this article is to review the characteristics, contents and effects of education interventions that were designed and implemented to improve knowledge, attitudes, beliefs and intentions with respect to the use of dietary supplements and doping agents in different populations. PubMed, Scopus, CINAHL, PsycInfo and Google Scholar were searched for English-language education interventions targeting dietary supplements and doping substances. A total of 20 articles were identified and have generally provided consistent findings. Most interventions reported a significant improvement in knowledge on dietary supplements and doping agents. Unfortunately, the heavy reliance on self-reported assessment tools limits the validity of these interventions, with almost all articles targeting athletes and adolescents.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".