The association between problematic Internet use, eating disorder behaviors, and well-being among Palestinian university students
Bibliographic record
Abstract
OBJECTIVES: The current study aimed to test the correlation between problematic Internet use, eating disorder behaviors, and well-being among Palestinian university students. METHODS: To examine the relationship between the study variables, a correlational study was conducted. The geographical representation of the study sample showed that 48.1% of participants were from urban populations, 48.1% were from rural villages, and 3.8% were from internally displaced people's camps. RESULTS: Pearson's correlation coefficient was used to test the relationship between problematic Internet use, eating disorder behaviors, and well-being. Results showed that problematic Internet use was negatively correlated to well-being (r = - .32, p < .01), and positively correlated to eating disorder behaviors (r = .39, p < .01). The regression analysis found that problematic Internet use contributes statistically and significantly towards explaining variance in eating disorder behaviors (B = .46, SE = .08, β = .32). Moreover, well-being contributed in a way that was statistically significant towards explaining variance in eating disorders behaviors (B = - .39, SE = .09, β = - .25). CONCLUSION: The results of our study support previous studies that indicated that problematic Internet use was significantly and positively correlated with eating disorder behaviors, while it was significantly and negatively correlated to well-being among Palestinian university students. Further studies testing this relationship will be crucial in developing interventions to both reduce problematic Internet use and eating disorder behaviors and increase well-being among university students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".