Understanding change and differential updating of risk perceptions associated with illicit substance use: a panel study
Bibliographic record
Abstract
While risk perceptions affect various health behaviors, there is insufficient knowledge about how they are formed and change over time surrounding illicit substance use. This study investigates the role of prior use, social influences, and media information on changes in the risk perceptions of expected susceptibility and severity of side effects in the context of the nonmedical use of prescription drugs for cognitive enhancement. It also examines differential updating by testing for the potential conditioning effects of prior use and self-control. We use a three-wave panel design (N = 8,377) with a nationwide random sample of adults in Germany. Fixed-effect regression models show that prior use and positive media information lower both risk perceptions, while negative information from others and the media produce increases. Rare users compared to non- and frequent users were more sensitive to new information obtained through others, thus showing stronger changes in risk perceptions. Moreover, self-control partially moderated the magnitude of changes in both risk perceptions, for example, regarding side effects reported in the media, which affected individuals with low self-control more strongly. In sum, the results indicate that personal and vicarious information affect the updating of risk perceptions, while partial evidence exists for differential updating.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".