Research on Relationship Among Internet-Addiction, Personality Traits and Mental Health of Urban Left-Behind Children
Why this work is in the frame
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Bibliographic record
Abstract
AIM: In this research, we attempted at exploring the relationships among urban left-behind children's internet-addiction, personality traits and mental health. METHODS: In the form of three relevant questionnaires (Adolescent Pathological Internet Use Scale, Eysenck Personality Questionnaire, Children's Edition in Chinese and Mental Health Test), 796 urban left-behind children in China were investigated, concerning internet-addiction, personality traits and mental health. RESULTS: (1) The internet-addiction rate of urban left-behind children in China reached 10.8%-a relatively high figure, with the rate among males higher than that among females. In terms of internet-addition salience, the figure of urban left-behind children was obviously higher than that of non-left-behind children. (2) In China, the personality deviation rate of the overall left-behind children was 15.36%; while the personality deviation rate of the internet-addicted urban left-behind children was 38.88%, a figure prominently higher than that of the non-addicted urban left-behind children group, with the rate among females higher than that among males. (3) The mental health problem rate of the overall urban left-behind children in China was 8.43%; while the rate of the internet-addicted urban left-behind children was 27.77%, a figure significantly higher than that of the non-addicted urban left-behind children. (4) There were significant relationships among internet-addiction, personality traits and mental health. The total score of internet-addiction and its related dimensions can serve as indicators of personality neuroticism, psychoticism and the total scores of mental health.
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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 | 0.017 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it