Development of a short form of the compulsive internet use scale in <scp>Switzerland</scp>
Bibliographic record
Abstract
OBJECTIVES: The study aims to develop a short form of the compulsive internet use scale (CIUS), which can be used in multitopic and general population health surveys and is invariant across different sexes, linguistic regions, and ages. METHODS: Two general population surveys from 2013 and 2015 were used as learning (n = 1,371) and validation samples (n = 1,550), respectively. Reducing items from the original CIUS was based on the following: (a) correlated errors between items, (b) differential item functioning, and (c) measurement invariance. Methods used item response theory and latent confirmatory factor analysis for ordinal variables. RESULTS: The eight-item short form maintained the five dimensions of the original scale and was metric and mostly scale invariant for sex, region, and age. It fell marginally short of scale invariance (ΔCFI < 0.01) for regions in the learning sample and for sexes in the validation sample (both ΔCFI = 0.013, p < 0.01). Root mean square error of approximation was 0.045 and 0.036, and comparative fit index was 0.989 and 0.995, in the learning and validation samples, respectively, showing excellent fit of the model to data. Correlations with the full scale were r = 0.966 (learning) and r = 0.969 (validation). CONCLUSION: If the full 14-item CIUS is a valid, reliable screening instrument, then the short eight-item form is too, and can be used in multitopic, general population health surveys.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".