The Prevalence of Nomophobia by Population and by Research Tool: A Systematic Review, Meta-Analysis, and Meta-Regression
Bibliographic record
Abstract
Background: No systematic review or meta-analysis has yet been performed to examine the global prevalence of nomophobia by population, by instrument. Thus, this review was performed to estimate the prevalence of nomophobia by severity. Methods: American Psychological Association PsycINFO, Cochrane, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EBSCOhost, EMBASE, MEDLINE, ProQuest Medical, ScienceDirect, Scopus, and Web of Science from inception of each respective database to second week of January 2021 were used. There was no language restriction. The random-effect meta-analysis model was used with the DerSimonian and Laird methodology was used for computation. Results: Twenty papers, involving 12,462 participants from ten countries, were evaluated for meta-analysis. The prevalence of moderate to severe nomophobia is 70.76% [95% CI 62.62%; 77.75%]. The prevalence of severe nomophobia is 20.81% [95% CI 15.45%; 27.43%]. University students appeared to be the highest group affected with a prevalence of severe nomophobia 25.46% [95% CI 18.49%; 33.98%]. Meta-regressions of severe nomophobia showed that age and sex were not a successful predictor of severe nomophobia β = −0.9732, p = 0.2672 and β = −0.9732, p = 0.4986. Conclusions: The prevalence of severe nomophobia is approximately 21% in the general adult population. University students appeared to be the most impacted by the disorder.
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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 | 0.034 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.061 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 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".