Assessing the Relationship Between Life Events and Internet Addiction Disorder Among Adolescents and College Students: A Protocol for Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background The increasing number of adolescents and college students who overuse the Internet is a global problem, brought a series of physical and mental harm to them. Systematic and standardized clinical treatment plan has not yet been formed, early intervention of its influencing factors, However, may help to reduce the symptoms of over-dependence to a certain extent. In this study, we will synthesize the present studies to evaluate the relationship and the mediating factors between life events and internet addiction disorder among adolescents and college students. Methods From inception to 25 March 2020, and contains the following databases: China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), Wan Fang Data, PubMed, Embase, The Cochrane Library, and Web of Science, MEDLINE. All observational studies will be included. No restriction on gender, race, or nation. Two reviewers (JW and YT) will independently conduct study selection, data extraction, and study quality assessment, any discrepancies will be settled by a third author (WP). Study quality will be assessed by the Newcastle-Ottawa Scale. The main outcome is several scales include YDQ, CIAS-R, IAT, ASLEC, LES and other high-quality scales on IAD and life events. We will use Review Manager 5.3 software to assess bias risk and data synthesis of each study.DiscussionThe findings of this study may provide a helpful reference for the intervention of Internet addiction disorder among adolescents and college students.Systematic review registrationCRD42020177316
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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 | 0.061 | 0.091 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.027 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 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".