Towards an understanding of how stress and resources affect the nonmedical use of prescription drugs for performance enhancement among employees
Bibliographic record
Abstract
Abstract Based on assumptions of the Job Demands-Resources model, we investigated employees’ willingness to use prescription drugs such as methylphenidate and modafinil for nonmedical purposes to enhance their cognitive functioning as a response to strain (i.e., perceived stress) that is induced by job demands (e.g., overtime, emotional demands, shift work, leadership responsibility). We also examined the direct and moderating effects of resources (e.g., emotional stability, social and instrumental social support) in this process. We utilized data from a representative survey of employees in Germany (N = 6454) encompassing various job demands and resources, levels of perceived stress, and willingness to use nonmedical drugs for performance enhancement purposes. By using Structural Equation Models, we found that job demands (such as overtime and emotional demands) and a scarcity of resources (such as emotional stability) increased strain, consequently directly and indirectly increasing the willingness to use prescription drugs for cognitive enhancement. Moreover, emotional stability reduced the effect of certain demands on strain. These results delivered new insights into mechanisms behind nonmedical prescription drug use that can be used to prevent such behaviour and potential negative 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".