The Description of Alexithymia in Nursing Students at Padjadjaran University with Social Media Addiction
Bibliographic record
Abstract
Aim: People’s inability to recognize and express their emotions (alexithymia) seems to be a risk factor in causing social media addiction, where the higher level of social media addiction, the higher level of alexithymia. This study aims to determine the prevalence of alexithymia among nursing students at Padjadjaran University who experience social media addiction. Method: This study used a quantitative descriptive design. The research samples were 216 nursing students at Padjadjaran University who experienced social media addiction after being screened using the IAT instrument, with a total sampling technique. The instrument used to see alexithymia was TAS-20 instrument. In this study, the data are analyzed by univariate analysis and presented in the form of frequency distribution tables. Result: The result of this study showed that less than half of the respondents, which were 94 (43.5%) experienced a high level of alexithymia, with alexithymia subscales average scores were 20,68 + 7.90 for DIF, DDF 16 + 5.54, and EOT 22,52 + 8.32. Conclusion: The conclusion of this study is that respondents who experienced moderate and severe social media addiction have higher alexithymia scores. Therefore, it is necessary to have preventive and promotive solutions for nursing students who don’t or have alexithymia.
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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 | 0.002 |
| 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) | 0.002 | 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".