At‐risk internet addiction and related factors among senior high school teachers in Japan based on a Nationwide survey
Bibliographic record
Abstract
BACKGROUND: Internet addiction (IA) has been drawing attention to mental health. However, few reports have been found on the related factors of at-risk IA among regular workers by a nationwide survey. The study aimed to evaluate the characteristics of at-risk IA and identify related factors among senior high school teachers in Japan. METHODS: This survey was a cross-sectional survey of high schools across Japan in 2017. There were 3189 teachers (2088 males and 1098 female) who participated in this survey. The questionnaire asked about their devices, both the time and the activities of using their internet, and sociodemographic factors. IA was measured by the internet addiction test (IAT) by which 40-79 points were classified as at-risk IA, and more as IA. We compared the related factors of at-risk IA and non-IA using descriptive analysis and multivariable regression analysis. RESULTS: The rates of IA and at-risk IA were 0.09% (n = 3) and 6.91% (n = 220), respectively. At-risk IA was positively associated with activities on the internet for gaming, entertainment, net-surfing, and younger ages. In addition, the at-risk IA group had a longer time spent on the internet than the non-IA group. CONCLUSIONS: Around 7% of high school teachers are at-risk IA in this survey, though they have regular work. Our results suggest that at-risk IA may be reinforced not only by the active internet use such as gaming, but also by purposeless behaviors, such as net-surfing. Managing time on the internet may support preventing at-risk IA among senior high school teachers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".