Alexithymia and its relation with Social Media among nursing students
Bibliographic record
Abstract
Back ground: Nursing students need to be aware of the optimal use of the social media because they spend a lot of time conducting scientific research that complements the academic curriculum, but when a nursing student becomes unable to control the use of the platform’s, this leads to internet addiction and this will affect their physical, psychological and social health . Aim: This study aimed to explore the relation between alexithymia and social media among nursing students. .Design: Correlational research design was used in the current study. Sample: quota sample Consists of 352 Nursing was chosen from the four- grades students of Faculty of Nursing, Benha University. Tools: Three tools were used (1st tool): Structured Interview Questionnaire sheet. (2nd tool): Toronto Alexithymia Scale. (3rd tool): Social Media Scale Student Form. Results: nearly half (48.6%) of the studied students have moderate level of social media addiction and more than half (51.1%) of the studied students have possible alexithymia level. Conclusion: there was a highly statistically positive correlation between total level of social media addiction and total level of alexithymia. Recommendations: The hazards and determinants of social media addiction should be added to the educational curricula and the methods that must be followed to avoid its adverse effects 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".