Prevalence and Associated Factors of Insomnia in Adult Psychiatric Patients: A Hospital-Based Study
Bibliographic record
Abstract
Background and Objective Insomnia is highly prevalent in psychiatric disorders, yet its patterns and predictors vary across diagnoses and cultural contexts. This study examined the prevalence and associations of insomnia in adult psychiatric patients in an outpatient setting.Methods A cross-sectional study was conducted among 380 adult psychiatric patients at a tertiary hospital. Insomnia was assessed using the Structured Clinical Interview for DSM-5 Sleep Disorders–Revised. Sociodemographic, clinical, and lifestyle variables were assessed, and logistic regression analyses were used to identify predictors of insomnia.Results The prevalence of insomnia was 44.2%, with the highest rates observed in trauma-and stressor-related disorders, followed by depressive disorders. Schizophrenia spectrum disorders, obsessive compulsive disorder, and conversion disorder showed significantly lower odds of insomnia compared to depressive disorders. Key predictors of insomnia included lower educational level (0–5 years: adjusted odds ratio [aOR]=5.60, p=0.015), low socioeconomic status (aOR=6.57, p=0.044), comorbid physical illness (aOR=3.81, p=0.005), and prolonged screen time (aOR=8.17, p<0.001).Conclusions Insomnia affects nearly half of adult psychiatric patients, with considerable variation across diagnostic categories. These findings underscore the need for targeted sleep assessment and interventions within psychiatric services, especially in low resource settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".