SAS-1: A single-item version of the Smartphone Addiction Scale
Bibliographic record
Abstract
The digital age has brought increasing concern about the negative effects of smartphones, which has led to dozens of measures of problematic smartphone use. Almost all of these measures, however, may be too long for contexts such as large-scale surveys. Here, we introduce a single-item measure that probes agreement with the statement: ‘I am addicted to my smartphone’. Across 10,786 participants aged 5 to 89 from 149 countries, 37% agreed or strongly agreed with this statement. Their agreement strongly correlated (r = .65 to .70) with scores on the short version of the Smartphone Addiction Scale (SAS-SV), the most widely used scale in the field. Younger women had the highest scores on both measures and tended to somewhat underestimate their addiction on the single-item scale. Overall, our results suggest that most people have an accurate self-assessment of their problematic smartphone use. This new 1-item scale (SAS-1) can therefore serve as a brief measure of problematic smartphone use when space is limited.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".