Gender-Based Differences and Associated Factors Surrounding Excessive Smartphone Use Among Adolescents: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Excessive smartphone use is a new and debated phenomenon frequently mentioned in the context of behavioral addiction, showing both shared and distinct traits when compared to pathological gaming and gambling. OBJECTIVE: The aim of this study is to describe excessive smartphone use and associated factors among adolescents, focusing on comparisons between boys and girls. METHODS: This study was based on data collected through a large-scale public health survey distributed in 2016 to pupils in the 9th grade of primary school and those in the 2nd grade of secondary school. Bayesian binomial regression models, with weakly informative priors, were used to examine whether the frequency of associated factors differed between those who reported excessive smartphone use and those who did not. RESULTS: The overall response rate was 77% (9143/11,868) among 9th grade pupils and 73.4% (7949/10,832) among 2nd grade pupils, resulting in a total of 17,092 responses. Based on the estimated median absolute percentage differences, along with associated odds ratios, we found that excessive smartphone use was associated with the use of cigarettes, alcohol, and other substances. The reporting of anxiety and worry along with feeling low more than once a week consistently increased the odds of excessive smartphone use among girls, whereas anxiety and worry elevated the odds of excessive smartphone use among boys. The reporting of less than 7 hours of sleep per night was associated with excessive smartphone use in all 4 study groups. CONCLUSIONS: The results varied across gender and grade in terms of robustness and the size of estimated difference. However, excessive smartphone use was associated with a higher frequency of multiple suspected associated factors, including ever having tried smoking, alcohol, or other substances; poor sleep; and often feeling low and feeling anxious. This study sheds light on some features and distinctions of a potentially problematic behavior among adolescents.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".