Il fenomeno delle "addiction" tra gli studenti universitari: uno studio esplorativo della relazione tra variabili psicologiche e il rischio di dipendenze
Bibliographic record
Abstract
This exploratory study aims to investigate the relationship between personality traits, addictive behaviors, and psychological well-being in a sample of 97 Italian university students. The research, conducted within the framework of the PRevenire con Insight, Motivazione, Emozione (Pr.I.M.E) project promoted by the Department of Anti-Drug Policies, seeks to identify risk and protective factors for maladaptive behaviors, with particular attention to substance use and internet abuse. Participants completed a battery of standardized questionnaires, including the Addictive Behavior Questionnaire, the Difficulties in Emotion Regulation Scale, the Toronto Alexithymia Scale, the Kessler Psychological Distress Scale, and the Big Five Questionnaire. The results show significant correlations between internet and alcohol abuse and certain psychological dimensions, such as difficulties in emotion regulation, alexithymia, and perceived stress. No significant associations emerged with other forms of addiction or addictive behaviors. Findings from this preliminary study suggest that, in particular, internet and alcohol abuse may represent maladaptive coping strategies among university students experiencing emotional difficulties and stress, and displaying personality traits characterized by low emotional stability. Further investigation of these dynamics is crucial for the development of targeted preventive interventions, which should focus on enhancing emotion regulation skills and effective stress management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".