Dijital bağımlılık ve FOMO, kişilik faktörleri ve mutluluk ile ilişkisi: üniversite öğrencileri ile bir uygulama
Bibliographic record
Abstract
Objective: This research explores the relationships between social media addiction, smartphone addiction, gaming disorder and personality, fear of missing out on developments (FoMo) and happiness.Method: This study was conducted with 497 volunteer participants (61,2% female, 38,8% male) who were Erciyes University students. The questionnaire consists of Five- Factor Personality Scale, Uskudar Fear of Missing Out Scale, Smartphone Addiction Scale (SAS), Internet Gaming Disorder Scale (IGDS9-SF), Social Media Addiction Scale (SMAS-SF), Toronto Alexithymia Scale and Oxford Happiness Scale.Results: There is a significant correlation between mobile addiction, gaming disorder, FoMo and social media addiction Neuroticism, agreeableness and conscientiousness with mobile addiction were correlated with gaming disorder. Significant relationships were found between social media addiction and neuroticism; virtual communication and virtual problem with conscientiousness and also between virtual knowledge and extraversion. Conclusion: It was determined there are prominent relationships between factors of social media addiction, smartphone addiction, game-playing disorder, personality traits, fear of missing out (FoMo) and happiness.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".