The Correlation Between Internet Addiction and Interpersonal Relationship Among Teenagers and College Students Based on Pearson's Correlation Coefficient: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Internet addiction (IA) has become a serious social issue, inducing troubles in interpersonal relationships, which may negatively impact the healthy development of teenagers and college students. Thus, the current research aimed to synthesize the available evidence to clarify the correlation between IA and troubles in interpersonal relationships. Method We searched eight electronic databases from inception to December 2020. Study quality was assessed by the Newcastle-Ottawa Scale (NOS), and Agency for Healthcare Research and Quality (AHRQ). We analyzed the data by extracting the Pearson correlation coefficients of each study and converted it into Fisher's Z. Pooled r was conducted by Fisher's Z and standard error (SE). STATA (Version 15.0) software was used for data synthesis. Results A total of 10,173 studies were initially identified, and 26 studies (n = 14,638 participants) were retrieved for further analysis. The results indicated that there was a significant positive correlation between IA and interpersonal relationship troubles [0.36 (95% CI 0.35–0.38)]. In addition, there was a positive correlation between IA and different dimensions of interpersonal relationship reflected by troubles with interpersonal conversation, making friends, dealing with people, and heterosexual communication, with the result of [0.26 (95% CI 0.18–0.33)], [0.29 (95% CI 0.20–0.37)], [0.27 (95% CI 0.19–0.34)], [0.22 (0.15–0.30)], respectively. The Egger test suggested that there was no publication bias (P > 0.05). Conclusion IA is positively correlated with troubles in interpersonal relationships. This research will provide new ideas and direction for further intervention, clinical therapy, and policy-making regarding IA to some extent. Systematic Review Registration https://www.crd.york.ac.uk/prospero/ , identifier: CRD42020177294.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".