Relationship between Psychological Well-Being and Smartphone Addiction of University Students
Bibliographic record
Abstract
This study was carried out to examine the relationship between university students’ levels of psychological well-being and smartphone addiction. The study group consists of a total of 408 students (303 female and 105 male) selected by random sampling method and studying at the departments of Primary Education, Science Teaching, Art and Crafts Teaching, French Teaching, and Guidance and Psychological Counseling at the Faculty of Education, Ondokuz Mayis University in the 2015-2016 academic year. In this research, the Psychological Well-Being Scale, Smartphone Addiction Scale-Short Version, and Personal Information Form were used to collect data.The independent-samples t-test, arithmetic mean, one-way analysis of variance (ANOVA) and Scheffé's post hoc test were employed for the analysis and interpretation of data. The Pearson Product-Moment Correlation Coefficient was used for the relationship between the level of psychological well-being and the use of smartphones, and accordingly the results were evaluated.The relationship between university students’ levels of psychological well-being and smartphone addiction seems to be significant based on this research. Factors affecting university students’ levels of psychological well-being and smartphone addiction include gender, grade, parental attitudes, economic status of the family, and level of perception.
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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 | 0.002 |
| 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".