Short version of the Smartphone Addiction Scale in Chinese adults: Psychometric properties, sociodemographic, and health behavioral correlates
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND AIMS: Problematic smartphone use (PSU) is an emerging but understudied public health issue. Little is known about the epidemiology of PSU at the population level. We evaluated the psychometric properties of the Smartphone Addiction Scale - Short Version (SAS-SV) and examined its associated sociodemographic factors and health behaviors in Chinese adults in Hong Kong. METHODS: A random sample of 3,211 adults aged ≥18 years (mean ± SD: 43.3 ± 15.7, 45.3% men) participated in a population-based telephone survey in Hong Kong and completed the Chinese SAS-SV. Multivariable linear regressions examined the associations of sociodemographic factors, health behaviors, and chronic disease status with SAS-SV score. Data were weighted by age, sex, and education attainment distributions of the Hong Kong general population. RESULTS: The Chinese SAS-SV is internally consistent (Cronbach's α = .844) and stable over 1 week (intraclass correlation coefficient = .76, p < .001). Confirmatory factor analysis supported a unidimensional structure established by previous studies. The weighted prevalence of PSU was 38.5% (95% confidence interval: 36.9%, 40.2%). Female sex, younger age, being married/cohabitated or divorced/separated (vs. unmarried), and lower education level were associated with a higher SAS-SV score (all ps <.05). Current smoking, weekly to daily alcohol drinking, and physical inactivity predict greater PSU after controlling for sociodemographic factors and mutual adjustment. DISCUSSION AND CONCLUSIONS: The Chinese SAS-SV was found valid and reliable for assessing PSU in Hong Kong adults. Several sociodemographic and health behavioral factors were associated with PSU at the population level, which may have implication for prevention of PSU and future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it