The Correlation of Internet Addiction and Interpersonal Relationship: A Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: Internet addiction has become a very serious social phenomenon. With the popularity of the internet, more and more people are addicted to the virtual network and ignore the real interpersonal communication. This has a lot of adverse effects on the healthy growth of teenagers. Thus, this systematic review and meta-analysis will access the problems existing in the interpersonal relationship of internet addiction and clarify the correlation between internet addiction and interpersonal disturbance.Method: We will search the following databases: PubMed, Embase, Medline, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), Wan Fang and China Science and Technology Journal Database (VIP). All observational studies will be included. Study quality will be assessed by the Newcastle-Ottawa Scale. We will use Review Manager 5.3 software for bias risk assessment and data synthesis. We will assess the between-study heterogeneity using I2 statistics. Discussion: We will synthesize the existing research to evaluate the correlation between internet addiction and interpersonal relationship of adolescents. This review will provide new ideas for the further intervention of Internet addiction to some extent.Systematic review registration: PROSPERO International prospective register of systematic reviews: 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 | 0.092 | 0.125 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.023 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.005 |
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".