The Relationship between Problematic Use of Smartphones and Social Anxiety
Bibliographic record
Abstract
This study investigated the smartphone use as the indicators of smartphone addiction and their associations with social anxiety as related variables. Problematic use of smartphones which is well known to be associated with anxiety might act as a common underlying factor explaining social anxiety disorder. This study aims to analyze the associations between mobile phone dependence and social anxiety disorder and to find possible predictors of social anxiety. Methods: Smartphone addiction assessed using the 20-item Nomophobia Questionnaire (NMP-Q) and Smartphone addiction scale (SAS). Liebowitz Social Anxiety Scale (LSAS) was used to determine social anxiety. The correlational analysis used to investigate the relationship between smartphone addiction and social anxiety. Linear regression conducted to calculate the predictors of social anxiety based on smartphone addiction parameters. Results: It is revealed that the level of social anxiety and smartphone addiction scales are positively correlated. Linear regression models for male and female participants showed different predictors of social anxiety. Conclusions: The study provides deeper insights into smartphone use and smartphone addiction as predictors of social anxiety in young people and concluded lesser dependence of males’ social anxiety on smartphone addiction level than the females’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".