Fear of Missing out, Social Media Engagement, Smartphone Addiction and Distraction: Moderating Role of Self-Help Mobile Apps-based Interventions in the Youth
Bibliographic record
Abstract
Smartphones offer high mobility and internet connectivity at the same time which has led to a substantial increase in the number of active social media users on the move, especially the ‘Millennials’. The excessive use of smartphone has been linked with several issues including mental well-being. Recently, different mobile applications have emerged to help users track their excessive use of smartphones and protect them from potential risks to mental health. This paper uses self-determination theory to examine the moderating role of such mobile applications (or self-help interventions) on inter-relationships between social media engagement, smartphone addiction and smartphone distractions. Survey responses from 284 college students reveal that mobile applications could prove to be quite effective self-help interventions that can help the young people in self-regulating their smartphone use. These results have substantial implications for designing effective mobile app-based interventions to save young people from potential risks to their mental health, productivity, and safety in performing their daily tasks. Future research directions have also been pointed out.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".