The Influence of Internet Addiction and Time Spent on the Internet Towards Social Isolation Among University Students in Malaysia
Bibliographic record
Abstract
Recent research has shown that there is a connection between Internet addiction and time spent on the Internet with social isolation of the users. Therefore, the aim of this study is to examine the influence of Internet addiction and time spent on the Internet towards social isolation among university students in Malaysia. A total of 110 respondents who were undergraduate and postgraduate students from four universities participated in the survey. The collected sample through purposive sampling technique was 110 responses which exceeded the minimum sample size. The research instruments for Internet addiction were adopted from Young Internet Addiction Scale Test which consist of 20 items whereas social isolation was measured using the UCLA Loneliness Scale consisting also of 20 items. Time spent on the Internet was measured based on the number of hours the students spent on social media platforms such as Facebook, WhatsApp, Twitter, and Instagram. The questionnaire was distributed via online channels due to the physical constraints of the Covid-19 pandemic. Data were analysed using IBM SPSS Statistics version 22 and Smart PLS-SEM Version 3.2.8. The measurement assessment showed that the data had fulfilled the composite reliability and convergent and discriminant validities requirements. The findings revealed that the level of Internet addiction, time spent on the Internet and social isolation were moderate. The findings also demonstrated that Internet addiction and time spent on the Internet influenced social isolation 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 | 0.003 |
| 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 | 0.001 |
| 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".