Assessing the prevalence of insomnia and its socio-behavioral determinants among school going adolescents in Bagamati Province, Nepal
Bibliographic record
Abstract
Insomnia among adolescents is a prevalent public health concern and is closely linked to suicidal tendencies, health risk behaviors, and other adverse health outcomes. This study builds on existing literature by exploring the multifaceted associations between insomnia and socio-behavioral factors, which are currently underexplored in the Nepalese context. It assesses the prevalence of insomnia and its association with socio-behavioral factors and internet addiction among adolescents in Bagmati Province, Nepal, to inform targeted public health interventions. From July to September 2022, a school-based descriptive cross-sectional study was conducted among grade 9 and 10 students (aged 13-19) using a self-administered semi-structured questionnaire. A questionnaire included the 7-item Insomnia Severity Index (ISI) for insomnia assessment and the 20-item Young's Internet Addiction Test for evaluating internet addiction. Binary logistic regression analysis was utilized to identify factors associated with insomnia. The study identified a significant prevalence of insomnia at 24.2%. Key socio-behavioral determinants included religion [AOR 3.58; 95% CI 1.56-8.23, AOR 3.36; 95% CI 1.27-8.89], experience of a break up [AOR 1.67; 95% CI 1.10-2.55] absence of close friendships [AOR 2.62; 95% CI 1.32-5.19], exposure to bullying [AOR 1.74, 95% CI 1.12-2.70], and internet addiction [AOR 2.74; CI 1.83-4.11]. These findings highlight the complex interplay of individual and environmental factors influencing insomnia. The significant prevalence of insomnia among school-going adolescents in Bagmati Province underscores the necessity for enhancing the role of schools in health counselling that considers behavioural, social, and demographic factors. Addressing internet addiction, fostering healthy social connections, and acknowledging the impact of demographic factors like religion could enhance intervention strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".