Gender Differences in Excessive Screen Time among Chinese High School Students in Henan Province
Bibliographic record
Abstract
In a technology-driven society, adolescents are particularly vulnerable to the effects of excessive screen time, and gender disparities are notable. However, evidence on the gender difference in excessive screen time among Chinese high school students is scarce. This study examined gender differences in excessive screen time and its impact on mental health and urinary incontinence among 15,055 high school-aged adolescents, including 7514 (49.9%) males and 7541 (50.1%) females with the mean age of 16.72 (SD 0.88) in Henan Province, China. A stratified two-stage cluster sampling design was applied. Mental Health Inventory of Middle School Students and International Consultation on Incontinence Questionnaire—Short Form was used to collect data about mental health and urinary incontinence status. The percentage of students reporting excessive screen time was 9.4%, predominantly male (15.3% versus 3.5%; p < 0.001). Physical disease, living on campus, and cigarette or alcohol consumption were common factors associated with excessive screen time in both genders. Students in their second or third year showed significantly lower rates of excessive screen time than their peers. In males, younger age, active sexual behavior, chronic constipation, and having a mother with a Ph.D. degree were factors associated with a higher probability of reporting excessive screen time, while females living in a rural–urban continuum or whose mothers have a junior college degree were associated with higher odds of reporting excessive screen times. Excessive screen time was significantly positively correlated with mental health problems and urinary incontinence (p < 0.05). Results suggests the need to address excessive screen time and to focus separately on the mechanisms influencing excessive screen time in males and females.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".