2240 – Simple And Multiple Relationships Between Assertiveness, Sensation Seeking, Alexithymia And Addiction Potential In University Students
Bibliographic record
Abstract
Alexithymia components (difficulty identifying feelings, externality oriented thinking and difficulty describing) and addiction potential in university students. Two hundred and fifty students were selected by cluster sampling from Shahid Chamran University, Ahvaz, Iran. The scales used for this descriptive study were the 20-item Toronto Alexithymia Scale (TAS-20), the Assertiveness Self-Report Inventory (ASRI), the Arnett inventory of sensation seeking, and the Iranian Addiction Potential Scale (IAPS). Data were analyzed with statistical analyzer software SPSS-ver. 16. There were simple and multiple relationships between assertiveness, sensation seeking, Alexithymia, difficulty identifying feelings, externality oriented thinking and difficulty describing and addiction potential. Multiple regression analysis (stepwise method) showed that sensation seeking, difficulty identifying feelings and assertiveness had significant multiple correlations with addiction potential (F=24.25, p< 0.001). The variables of externality oriented thinking and difficulty describing feelings were eliminated by the regression analysis. Variables such as sensation seeking, difficulty identifying feelings and assertiveness predicted addiction potential among university students. The most important implication of this research was to pay attention scientifically to these variables as the fundamental factors of this difficulty, rather than just emphasizing the cessation of drug or alcohol using.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".