Prevalence of internet addiction and anxiety, and factors associated with the high level of anxiety among adolescents in Hanoi, Vietnam during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic and the resulting isolation measures created an increase in the usage of smart devices and internet among adolescents. This study aims to estimate the prevalence of internet addiction, the prevalence of high level of anxiety as well as to examine factors associated with the high level of anxiety among adolescents in Hanoi, Vietnam during the COVID-19 pandemic.Data was collected using respondent-driven sampling and Google online survey forms from a sample of 5,325 school students aged 11-17 in Hanoi between October and December 2021. A short scale consisting of 5 items was used to measure internet addiction and the GAD-7 was used to measure adolescent anxiety level.The findings revealed that 22.8% and 7.32% of adolescents experienced moderate and severe anxiety. About 32.7% of the study sample exhibited at least three internet addiction indicators. Logistic regression analysis identified significant predictors for high levels of adolescent anxiety. Being female, family experiencing economic difficulties, and exposure to domestic violence were associated with higher risk of anxiety disorder (OR 1.78, 1.45, and 2.89, respectively). Both average daily online time and internet addiction demonstrated gradient association with high level of anxiety.The prevalence of internet addiction and high level of anxiety were high among adolescents in Hanoi, Vietnam during the COVID-19 pandemic. The study highlights the importance of implementing measures at the family and school levels to promote a balanced and healthy approach to smart device use among adolescents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".