Evaluation of Internet Addiction and Relational Variables Among Nursing Students in Turkey During the COVID-19 Pandemic
Bibliographic record
Abstract
It is known that individuals use the internet more to escape from the psychological problems they encounter in daily life during the pandemic. Besides, it is also known that individuals with personality traits such as neuroticism and extraversion might be prone to internet addiction due to poor communication skills. It is important to determine the relationship between the internet usage characteristics and the mental state of nursing students so that students can provide better quality health services in their education and professional processes. The present study aimed to determine the relationship between internet addiction and personality traits, stress, and obsessive-compulsive symptoms among nursing students during the pandemic. This study includes 528 nursing students. The Young’s Internet Addiction Test (YIAT), the Vancouver Obsessive-Compulsive Inventory (VOCI), the Eysenck Personality Inventory (EPI), and the Perceived Stress Scale (PSS) were used for data collection between August and October 2021. It was found that there was a statistically significant and positive correlation between the students’ YIAT mean scores and the EPI neuroticism sub-dimension, VOCI all sub-dimensions, and PSS mean scores ( p < .05). In addition, the mean scores of the PSS and EPI were predictors of the YIAT total score ( R = .550, R 2 = .233, p < .05). Considering these results, it is necessary to prevent the negative effects of the COVID-19 pandemic on the psychosocial health of individuals. Psychological counseling can be offered to provide protective factors during the pandemic period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".