Social Media Addiction and its relation to Alexithymia and Feeling of Loneliness among Students of Faculty of Nursing
Bibliographic record
Abstract
Background: The proliferation of social media platforms such as Facebook, Twitter, andInstagram have revolutionized the way students communicate, consume, and share information.Nursing students also use social media in their daily routine. The use of social media leads toimpairment in both mental and emotional status of nursing students. Aim of this study: Was toexplore the relationship between social media addiction and alexithymia and feeling of lonelinessamong students of Faculty of Nursing. Design: Descriptive correlational design was utilized tofulfill the aim of this study. Setting: This study was conducted at the Faculty of Nursing at BenhaUniversity, Qalyubia governorate. Study subject: Purposive sample of 352 students was chosenfrom the four- grades students of Faculty of Nursing, Benha University. Tools of data collection:four tools were used. 1st tool: Structured Interview Questionnaire sheet. 2nd tool: Social MediaAddiction Scale Student Form. 3rd tool: Toronto Alexithymia Scale. 4th tool: UCLA LonelinessScale. Results: Nearly half of the studied students have moderate level of social media addictionand more than half of the studied students have possible alexithymia level and less than one fifthof them have mild level of loneliness. Conclusion: There was a highly statistically positivecorrelation between total level of social media addiction and total level of alexithymia. Also, therewas a highly statistically positive correlation between total level of loneliness and total level ofalexithymia. Recommendations: The hazards and determinants of social media addiction should beadded to the educational curricula and the methods that must be followed to avoid its adverseeffects on physical, psychological, and social well
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".