Nonmedical use of prescription drugs for cognitive enhancement as response to chronic stress especially when social support is lacking
Bibliographic record
Abstract
The nonmedical use of prescription drugs to improve cognitive performance has gained attention due to concerns over its social and political implications as well as side effects and long-term health consequences. Some researchers expect a future trend of an instrumental use of drugs for cognitive enhancement (CE). Thus, getting insights about causes of CE-drug consumption is warranted before the prevalence increases. Because perceived stress is ubiquitous in universities and may decrease cognitive performance, one reaction to cope with stress and its consequences might be the instrumental use of drugs for CE, especially if other resources, such as social support, are lacking. With a prospective design, randomly selected students from four German universities were invited to a web-based survey and reinterviewed after 6 months (N = 2,203). Results show a 6-month prevalence rate of self-reported CE-drug use of about 2%. Higher reported chronic stress is positively associated with CE-drug use. Although social support has no main effect, stress-buffering effects were found. In men with low stress, more support is associated with a higher chance of self-reported CE-drug use. These findings can inform intervention and prevention strategies such as changes in drug regulation or sensitizing (potential) users to unwanted health consequences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".