Effort–Reward Imbalance at Work and Prescription Drug Misuse—Prospective Evidence from Germany
Bibliographic record
Abstract
This study examines how work stress affects the misuse of prescription drugs to augment mental performance without medical necessity (i.e., cognitive enhancement). Based on the effort–reward imbalance model, it can be assumed that a misalignment of effort exerted and rewards received increases prescription drug misuse, especially if employees overcommit. To test these assumptions, we conducted a prospective study using a nationwide web-based sample of the working population in Germany (N = 11,197). Effort, reward, and overcommitment were measured at t1 and the 12 month frequency of prescription drug misuse for enhancing cognitive performance was measured at a one-year follow-up (t2). The results show that 2.6% of the respondents engaged in such drug misuse, of which 22.7% reported frequent misuse. While we found no overall association between misuse frequency and effort, reward, or their imbalance, overcommitment was significantly associated with a higher misuse frequency. Moreover, at low levels of overcommitment, more effort and an effort–reward imbalance discouraged future prescription drug misuse, while higher overcommitment, more effort, and an imbalance increased it. These findings suggest that a stressful work environment is a risk factor for health-endangering behavior, and thereby underlines the importance of identifying groups at risk of misusing drugs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".