The adverse impact of excessive internet use during the COVID-19 pandemic on adolescents' coping skills: A case study in Hanoi, Vietnam 2021
Bibliographic record
Abstract
The COVID-19 pandemic has created significant stressors in Vietnamese adolescents' lives. Coping skills play important roles in helping adolescents contend with stress. This study aimed to evaluate adolescents' coping skills during the COVID-19 pandemic and examine how those skills are impacted by excessive internet use during this pandemic.The study used respondent-driven sampling and Google online survey forms to collect data. The study sample included 5,315 high school students aged 11- 17 years in Hanoi's rural and urban areas. The Kid Coping Scale was applied to examine adolescents' coping, and the coping score was compared among adolescents with different levels of internet use.The average coping score measured by Kid Coping Scale was 20.40 (std = 2.13). About half of adolescents often "avoid the problem or the area where it happened" when experiencing a hard time. One-third of adolescents often stopped thinking about the problem they faced. More than one-fourth of respondents stayed online for at least 8 h per day. The online time for learning/other activities showed a reverse dose-response relationship with the coping score; the longer the internet use duration, the lower the coping score.The mean score of coping of Hanoi adolescents was moderate. Internet use has an adverse impact on their coping skills.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".