The role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran
Bibliographic record
Abstract
BACKGROUND: The World Health Organization defines mental health as a combination of two dimensions: the negative dimension, or negative mental health, which indicates the presence of mental disorders, symptoms, and problems, and the positive dimension, or positive mental health, which includes emotions and positive personal characteristics such as self-esteem, resilience against environmental challenges, a sense of integrity, and self-efficacy. The aim of the present study was to investigate the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran, Iran. METHOD: The research method employed was a survey. 758 people participated in the study, and the samples consisted of high school students in Tehran during the academic year 2022-2023. The process of collecting information was carried out by distributing the questionnaire link through virtual networks and schools. The research utilized Young's Internet Addiction Test, Samuels' Academic Resilience Inventory, and Goldberg's Mental Health Questionnaire as the research tools. Statistical tests, including Pearson's correlation and multiple regression analysis, were employed to investigate the relationships between variables. RESULT: The effect of internet addiction on mental health (ß=0.39) is negative and significant at the 0.001 level, while the effect of academic resilience on mental health (ß=0.66) is positive and significant at the 0.001 level. These two variables collectively predict 53% of the variance in students' mental health. This indicates that as internet addiction increases among students, their mental health significantly decreases, whereas higher levels of academic resilience correspond to higher mental health. CONCLUSIONS: This study has elucidated the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran. Given the significance of adolescent mental health, it is imperative for healthcare professionals and other stakeholders to develop intervention and prevention models to address mental health crises and plan for the enhancement of adolescent mental health.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".