Internet Addiction Through the Phase of Adolescence: A Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Adolescents increasingly use the Internet for communication, education, entertainment, and other purposes in varying degrees. Given their vulnerable age, they may be prone to Internet addiction. OBJECTIVE: Our aim was to identify possible differences in the purpose of Internet use among adolescents with respect to age subgroup, country of residence, and gender and the distribution of Internet addiction across age subgroups. Another aim was to determine if there is a correlation between the purpose of Internet use and age and if this interaction influences the level of addiction to the Internet. METHODS: The study included a simple random sample of 1078 adolescents-534 boys and 525 girls-aged 11-18 years attending elementary and grammar schools in Croatia, Finland, and Poland. Adolescents were asked to complete an anonymous questionnaire and provide data on age, gender, country of residence, and purpose of Internet use (ie, school/work or entertainment). Collected data were analyzed with the chi-square test for correlations. RESULTS: Adolescents mostly used the Internet for entertainment (905/1078, 84.00%). More female than male adolescents used it for school/work (105/525, 20.0% vs 64/534, 12.0%, respectively). Internet for the purpose of school/work was mostly used by Polish adolescents (71/296, 24.0%), followed by Croatian (78/486, 16.0%) and Finnish (24/296, 8.0%) adolescents. The level of Internet addiction was the highest among the 15-16-year-old age subgroup and was lowest in the 11-12-year-old age subgroup. There was a weak but positive correlation between Internet addiction and age subgroup (P=.004). Male adolescents mostly contributed to the correlation between the age subgroup and level of addiction to the Internet (P=.001). CONCLUSIONS: Adolescents aged 15-16 years, especially male adolescents, are the most prone to the development of Internet addiction, whereas adolescents aged 11-12 years show the lowest level of Internet addiction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".