Assessing sleep quality of Lebanese high school students in relation to lifestyle: pilot study in Beirut
Bibliographic record
Abstract
BACKGROUND: Sleep problems in teenagers seriously disturb the active process of learning. Given the absence of sleep data from Lebanon, a study to determine sleep quality among adolescents is vital. AIMS: To understand sleep habits and patterns that affect sleep quality, and assess the amplitude of possible sleep problems in Lebanese adolescents, raising awareness of the effects of good sleep hygiene on general health in adolescents. METHODS: A cross-sectional survey of 500 high-school students in Beirut was conducted using a self-filled questionnaire inquiring about sociodemographics, health-risk behaviour and sleep quality. The effect of several factors related to sleep habits of the students was investigated using bivariate analysis and logistic regression. RESULTS: We found that 76.5% of teenagers were not satisfied with their sleep quality; 56% did not have the appropriate amount of sleep (< 8 hours); and 82.4% used mobile phones and electronic devices in bed before falling asleep. Moreover, 3.2% faced a real problem with sleep initiation, 11.3% with sleep maintenance and 8.7% with early awakening. CONCLUSIONS: A large proportion of high-school students in Beirut have poor sleep patterns. It is therefore necessary to increase awareness of the problem in education in order to prevent its escalation.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".