Understanding phubbing behavior: A scoping review of qualitative and mixed-methods studies
Bibliographic record
Abstract
The use of smartphones has significantly increased in recent years, leading to the emergence of a new concept known as phubbing , which refers to being absorbed in one's smartphone while in the presence of others and neglecting interpersonal communication. Quantitative studies have highlighted the negative impacts of phubbing on, for example, relationship quality and satisfaction, as well as its predisposing factors. However, there is limited information on the experiences of those who engage in phubbing (phubbers) and those who are affected by it (phubbees). This scoping review aims to provide a comprehensive overview of the current understanding of phubbing derived from qualitative and mixed-methods studies. It follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Seven databases were searched for relevant studies, from which 251 articles were found. The title and abstract screening led to the full-text review of thirty-one articles, of which thirteen were retained and assessed for quality. Data extraction and narrative synthesis were then performed on the thirteen articles included in this study. Among these, seven were qualitative and six employed mixed methods. The results were divided into seven categories: (1) study characteristics, (2) definitions, (3) negative consequences, (4) positive factors, (5) social norms and contextual factors, (6) motives, and (7) strategies. The findings of this review highlight the need for further research to clarify phubbing terminology, explore its social norms across cultures, understand its impacts, identify mitigation strategies, and investigate the factors associated with phubbing in children and adolescents.
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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 | 0.108 | 0.254 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.039 | 0.035 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".