2664 – Internet Addiction: Actual Status of Assessment Tools
Bibliographic record
Abstract
Introduction: Internet over-consumption can cause dysfunctional usage resulting in several deleterious outcomes. The “Internet Use Disorder” is gaining importance in diagnostic manuals as its prevalence increases. Objectives: The objective of the study was to examine how this upcoming disorder is assessed at present. Aims: We present the actual status of Internet addiction assessment tools and develop a theoretical framework for optimizing “Internet Use Disorder” assessment tools design. Methods: A factorial structure analysis of tools assessing Internet addiction for adolescents and adults published between January 1993 and October 2011 was performed and a theoretical framework for optimizing tools design was developed. The descriptive properties of 14 questionnaires were measured and the position of the instruments’ methodology, validity, reliability and model fit was presented, along with the preferred factorial analysis method and validation technique. Results: Results indicate some heterogeneity in study methodology and differences in descriptive and dimensional aspects of assessment tools. The three factor categories compulsive Internet use, negative outcomes and salience were central to Internet addiction questionnaires. Furthermore, the social dimension was often under-represented. No significant difference was observed in the distribution of factor categories across Internet addiction questionnaires and DSM-IV-TR and ICD-10 diagnostic criteria for substance dependence. Conclusions: The validity and reliability of the evaluated questionnaires reflect the newness of this field. The underrepresentation of the social use of Internet is a problematic situation as it is causing considerable damages. Future research should consider that Internet addiction evaluation questionnaires need refinement. Appropriate strategies are proposed.
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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 | 0.048 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".