Association of Phubbing Behavior and Fear of Missing Out: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Phubbing, a pervasive social behavior linked to smartphone usage, involves users neglecting their conversation partners to engage with their phones. Despite consistent exploration of its association with the concept of fear of missing out (FOMO), findings in the existing literature exhibit notable inconsistency. To address this gap, this study employs a systematic review and meta-analysis to scrutinize the intricate relationship between phubbing behavior and FOMO. A comprehensive systematic review, spanning up to December 10, 2023, encompassed databases such as PubMed, Scopus, Web of Science, ProQuest, and Google Scholar. The resulting dataset comprised 27 eligible studies, incorporating insights from 20,415 participants across 15 countries. Rigorous evaluation of study quality was executed using the Newcastle Ottawa Scale, while statistical analyses were meticulously conducted using R Studio. Revealing a robust positive association, phubbing behavior was significantly linked to FOMO (effect size[ES] = 0.43, 95% CI: 0.36, 0.49, I 2 : 97.5%, τ 2 : 0.05). Correcting for detected publication bias using the Trim and Fill method, an additional 16 studies were included, fortifying the robustness of the findings. Moderation analysis uncovered significant influences of location ( p < 0.01), income level ( p < 0.01), sampling method ( p < 0.01), phubbing scale ( p < 0.01), and FOMO scale and type ( p < 0.01) on the estimated relationship. Univariate meta-regression highlighted the substantial impact of sample size ( R 2 = 11.81%, p < 0.01), while multivariate meta-regression illuminated the combined effects of publication year, study quality score, sample size, mean age, and female proportion on the estimated relationship ( k = 19, R 2 = 52.85%, I 2 = 93.78%, p < 0.05). Furthermore, post hoc influential analysis, conducted through the leave-one-out method, offered additional depth to the examination.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 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".