The Relationship Between Loneliness and Internet or Smartphone Addiction Among Adolescents: A Systematic Review and meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Loneliness is a common public health problem that influences people’s physical and mental health. There is a high incidence of loneliness in adolescents. Some research suggested that smartphone or Internet addiction (SA or IA) may be a factor. But the relationship between loneliness and SA or IA is not completely clear among adolescents. We aim to estimate the correlation coefficient r between them. Methods: Databases, consisting of PubMed and Web of Science, were retrieved systematically for studies of the association between adolescents’ loneliness and SA or IA. The Newcastle-Ottawa Scale was chosen as an assessment tool in this analysis. We estimated the correlation coefficient r between loneliness and SA or IA and drew a forest plot. Moreover, moderator analyses were also conducted to explore what leads to heterogeneity in our study. Results: 21 studies were finally included in our analysis with 27,843 samples. The pooled correlation coefficient r was 0.252 (95% confidence interval: [0.173, 0.329]; p < 0.001) with low heterogeneity (I 2 = 0.000%; Q = 23.616; p < 0.001), indicating a moderate positive association. The funnel plot indicated small publication bias. A one-study removal sensitivity analysis indicated there was no significant difference between these studies. Meta-regression indicated no significant difference between the results and age (Q = 11.94, df = 18, p = 0.8504). Conclusions: Our analysis indicated a moderate positive association between loneliness and SA or IA. The results may attract the attention of some experts who study adolescent psychological problems and behavioral problems and may provide ideas for their research in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it