Association between Digital Burnout and Sleep Quality among King Faisal University Students
Bibliographic record
Abstract
As a public health issue, both burnout syndrome and sleep problems affect students' academic performance and their well-being. Overuse of digital devices can cause digital burnout (DBO) in university students. This study aimed to explore digital device usage, sleep quality, DBO levels, and their association with sleep quality (SQ) among King Faisal University (KFU) students for the academic year 2022-2023. This cross-sectional study targeted undergraduate students of both genders at KFU across all academic levels and specialties. An online self-administered questionnaire was created using Google Forms and distributed via e-mail to KFU students between May and July 2023. Data were analyzed using SPSS software with statistical tests applied, and a p-value of 0.05 was considered the significance threshold. In total, 744 KFU students participated in the study. Data analysis revealed moderate DBO levels and average SQ, 427(57.4%) and 526 (70.8%), respectively, with no significant differences based on age, gender, college, and marital status. Additionally, a statistically significant association was found between higher DBO levels and poorer SQ among the participants (Pearson’s correlation test, r = 0.548, p = 0.000). Our study confirmed the association between DBO and SQ among KFU students. These findings underline the importance of university initiatives that promote stress-reducing activities and adaptive behaviors as resilience measures to support students' mental health, academic performance, and well-being.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".