Profiling and assessing the risks of image- and performance-enhancing drugs use during the COVID-19 lockdown
Bibliographic record
Abstract
Background: Image and Performance-Enhancing Drugs (IPEDs) can enhance mental and physical capabilities and impact one's overall health. Initially confined in sport environments, IPEDs use has become increasingly widespread in a high-performing society. The present study was aimed at profiling IPEDs use during the COVID-19 lockdown among an international sample of young adults. Methods: A cross-sectional observational study was carried out in eight countries (United Kingdom, Italy, Lithuania, Hungary, Portugal, Spain, Brazil, and Japan) between April and May 2020. The survey questionnaire included validated measurements such as Exercise Addiction Inventory (EAI), Appearance Anxiety Inventory (AAI), and Self-Compassion Scale (SCS) as well as questions about the type of IPEDs, purchasing methods and socio-demographic information. Results: = 10.06), and 64.2% were female participants. Overall, 6.8% were found at risk of exercise addiction (EAI >24), 27.6% presented high levels of appearance anxiety, and 24.9% revealed low levels of emotional regulation's self-compassion. Most participants (55.6%) purchased IPEDs through pharmacies/specialized shops, while 41.3% purchased IPEDs on the Internet. Online IPEDs buyers were mainly men who had higher scores on the Exercise Addiction Inventory. One or more IPEDs classifiable as "potentially risky" were used by 66.3% of the sample. Users of "potentially risky IPEDs" were younger and primarily men. They showed higher scores both on the Exercise Addiction Inventory and Appearance Anxiety Inventory. Conclusion: This study profiled users of IPEDs when the most restrictive COVID-19 lockdown policies were implemented in all the participating countries. More targeted post-COVID 19 prevention strategies should be implemented according to the emerged socio-demographic and psychopathological traits and cross-cultural differences emerged. Longitudinal studies will also be needed to determine the long-term effect of the COVID-19 lockdown on IPEDs consumption.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".