The Relationship between High School Students' Internet Addiction, Social Media Disorder, and Smartphone Addiction
Bibliographic record
Abstract
The purpose of this study is to examine the relationships between high school students' internet addiction, social media usage disorder, and smartphone addiction. The descriptive relational scanning model, one of the quantitative research methods, was used to determine this relationship. The research was carried out with 215 students who continue their education in the field of information technologies of 4 state vocational high schools in the province of Siirt in the 2018-2019 academic year. In this research, internet addiction, social media usage disorder, and smartphone addiction scales were used as data collection tools. The research data were analysed with the SPSS statistical calculation program. As a result of the research, it was observed that high school students' internet addiction, social media usage disorder, and smartphone addiction are moderate and similar in terms of gender and class levels. In the study, a positive relationship was found between high school students' internet addiction, social media usage disorder, and smartphone addiction. It was also found that all three problems were the predictors of each other. The findings were discussed with the literature along with some recommendations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".