Perceived Stress, Alexithymia, and Psychological Health as Predictors of Sedative Abuse
Bibliographic record
Abstract
BACKGROUND: The harmful effects of sedative medications and substances in conjunction with limited research regarding predictive psychological constructs of drug abuse necessitate further investigation of associated factors. Therefore, the present study aimed to elucidate the roles of perceived stress, alexithymia, and psychological health as predictors of sedative abuse in medical students. METHODS: In this cross-sectional study, 548 students at Kermanshah University of Medical Sciences, Iran, were selected using stratified random sampling. The data were obtained using the Perceived Stress Scale, an alexithymia scale (Farsi version of the Toronto Alexithymia Scale-20), and a General Health Questionnaire to assess psychological health. Data were analyzed using discriminant analyses. RESULTS: The results demonstrated that the user and non-user of sedative substances groups had significantly different predictive variables (except for social function disorder) (P>0.05). Physical complaints, alexithymia, and perceived stress, which had standard coefficients of 0.80, 0.60, and -0.27, respectively, predicted sedative drug use. CONCLUSION: The results of the present study indicate that perceived stress, alexithymia, physical complaints, anxiety, and depression are associated with sedative drug abuse.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".