Correlation between the Family Function Based on Circumplex Model and Students’ Internet Addiction in Shahid Beheshti University of Medical Sciences in 2015
Bibliographic record
Abstract
BACKGROUND & PURPOSE: University students deal with Internet with a variety of reasons. Internet great applications and attractions may cause increasing addiction to it; on the other hand the family function may affect the tendency to addiction. So, this study was conducted aimed to investigate the correlation between the family function based on Circumplex Model and students' Internet addiction in ShahidBeheshti University of Medical Sciences in 2015. METHODS: In this correlational study, 664 students were selected by stratified random sampling method. The study tools included: Demographic Information Questionnaire, Young Internet Addiction Test (α=0.90) and Olson Family Adaptability and Cohesion Evaluation Scale(FACE III) (α=0.91). Data were analyzed by SPSS software Version 22.The results were analyzed using descriptive statistics (mean, standard deviation, percentage and frequency) and analytical statistics (t-test, Mann-Whitney U, Spearman correlation coefficient) methods. FINDINGS: Findings showed, 79.2 percent of students did not have Internet addiction, 20.2 percent were at risk of addiction and 0.6 percent was addicted to the Internet. Female students were the most frequent users of the Internet among students (41.47% and p < 0.01) with the purpose of recreation and entertainment (79.5 percent). A significant negative correlation was seen between Internet addiction and cohesion (a family function aspect) (p<0.01), also a positive and significant relationship was seen between average time of using Internet every time, average weekly hours of Internet use and Internet addiction (p>0.01). CONCLUSION: With regard to the degree of students' dependence to internet and the correlation between the family cohesion and Internet addiction, there is a need to make policy in the field of cohesion balance in the family and preventive and educational measures.
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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 | 0.004 |
| 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) | 0.002 | 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".