Traits associated with internet\n\t\t\t\t addiction in young adults: Potential risk factors
Bibliographic record
Abstract
The present study sought to determine whether certain personality traits associated with problematic substance use may also characterize young adults who report problematic internet use. An index of internet addiction as well as measures of traits previously linked to problematic substance use were administered to a sample of 86 young adults aged 18–30 years. Measures included the Internet Addiction Test (IAT), Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), Depression Anxiety and Stress Scales (DASS-21), Toronto Alexithymia Scale (TAS-20), and the Fear of Intimacy Scale (FIS). Results indicated that IAT scores were significantly positively correlated with TAS-20, DASS-21, SPSRQ and FIS scores, as predicted. When age, gender and negative mood were controlled in a hierarchical regression, sensitivity to punishment (SP), sensitivity to reward (SR) and FIS significantly contributed to variance in IAT in the final model. SP partially mediated the relationship between TAS-20 and IAT, whereas no such mediation was indicated for SR or FIS. Present findings suggest that alexithymia and reward sensitivity may be important risk factors for internet addiction as for problematic substance use, whereas sensitivity to punishment may account for at least part of the association between alexithymia and problematic use of the internet.
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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 | 0.001 |
| 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) | 0.002 | 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".