Tridimensional Personality of Adolescents with Internet Addiction and Substance Use Experience
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the differences in personality characteristics between adolescents with and without Internet addiction and substance use experience as defined by the Tridimensional Personality Questionnaire (TPQ), and to compare personality characteristics among groups of adolescents with both Internet addiction and substance use experience (comorbid group), those with only Internet addition (Internet addiction group), those with only substance use experience (substance experience group), and those without Internet addiction or substance use experience (control group). METHOD: In the cross-sectional investigation, we recruited 3662 students (2328 boys and 1334 girls) from high schools in southern Taiwan. Our investigation was conducted using the TPQ, the Chen Internet Addiction Scale, and Questionnaires for Experience in Substance Use. RESULTS: Adolescents with Internet addiction were more likely to have substance use experience. High novelty seeking (NS), high harm avoidance (HA), and low reward dependence (RD) predicted a higher proportion of adolescents with Internet addiction. High NS, low HA, and low RD predicted a higher proportion of adolescents with substance use experience. Of the 4 groups, the Internet addiction group had the highest HA scores and the comorbid group had the lowest HA scores. CONCLUSION: Adolescents with high NS and low RD should be provided with effective strategies for preventing Internet addiction and substance use. In addition, the Internet addiction group and the comorbid group should be provided with different preventative strategies focused on HA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".