Between smart drugs and antidepressants: A cultural analysis of pharmaceutical drug use among university students
Bibliographic record
Abstract
In recent years, there has been a growing concern for the non-medical use of prescription drugs by young adults, and particularly college and university students. These expanding practices of pharmaceutical drug use have been associated with different phenomena such as physical and cognitive enhancement, non-medical prescription drug use and recreational use. All of these various consuming practices involve a blurring of the boundaries between curing, preventing and enhancing and between recreational and productive activities. Some studies show the importance of linking pharmaceutical drug use with broader issues among students in academic settings, such as competitiveness, pressure, stress, depression and mental health issues. The main objective of this article is to explore some of the perceptions and rationales that underlie the use of non-medical and medical prescription drugs, natural products, energy drinks and other pharmaceutical or natural substances among university students in Montreal (Canada). Focus-groups and indepth, semi-structured interviews were realized with a sample of 42 students in order to link the consumption cultures of the participants with other variables, such as their lifestyle, their experience of psychological distress and anxiety and their need of achievement. Focusing on the young population fulfills the function of sociological forecast about the future trends in the expansion of pharmaceutical drug use for the management of everyday life in contemporary Western societies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".