Nomophobia Levels and Personalities of University Students
Bibliographic record
Abstract
The development of technology has made our lives easier and has caused some dependencies to enter our lives. The aim of this study is to measure the nomophobia levels of the students at the faculty of sports at Uşak University and the faculty of technology at Isparta Applied Sciences University and to examine the relationship of personality with the level of nomophobia. The sample of the study consists of a total of 408 students at the faculty of sports at Uşak University and the faculty of technology at Isparta Applied Sciences University. Volunteering was taken into account in participation. Students’ nomophobia level and personality properties were analyzed according to the gender of the participants, age groups, educational status, department, phone usage by years, daily smartphone usage time, mobile internet usage time, daily mobile internet usage time, smartphone night off status, the time spent with friends during the day. In the study, the Nomophobia Scale, developed by Yıldırım and Correia (2015) and whose validity and reliability were made, was developed by Somer, Tatar and Korkmaz (2001) and the 5-Factor Personality Inventory, whose validity and reliability of short form were, made was used by Tatar (2005). In this study, SPSS 22.00 Program used in quantitative research methods was used. The data were summarized by giving percentage and frequency tables. This study was tested with a significance level of 0.05. There are statistically both significant relationship (p < .05) and non-sense relationship (p > .05) between nomophobia and subscale scores according to age, gender, department, phone usage by years, daily smartphone usage time, mobile internet usage time, daily mobile internet usage time, smartphone night off status and the time spent with friends during the day. A statistically significant relationship was found between personality and nomophobia (p < .05). The results of the study showed us how nomophobia is effective on students. As a result of the study, it is an indicator that personality affects nomophobia. It seems that personality is effective on nomophobia. As the personality gets stronger, the level of nomophobia will decrease. Therefore, necessary studies can be done about the personality. In addition, a new research can be proposed in which students in the other department will be assessed to be affected by the level of nomophobia.
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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 | 0.000 |
| 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) | 0.003 | 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".