The prevalence and risk factors of sleep disturbances among mental health patients following hospital discharge
Bibliographic record
Abstract
Background Sleep disturbances significantly impact psychological wellbeing, particularly during the critical transition when patients are discharged from psychiatric units. Despite extensive research on sleep and mental health, limited attention has been given to this transitional period. Aim This study examined the prevalence and risk factors of sleep disturbances among patients preparing for discharge from psychiatric units in Alberta, Canada. Methods This cross-sectional epidemiological study involved face-to-face interviews with eligible patients, followed by an online survey assessing sleep issues using the Patient Health Questionnaire (PHQ-9). Additional data on demographics, clinical information, and responses to the Generalized Anxiety Disorder (GAD-7) and World Health Organization Well-Being Index (WHO-5) were also collected. Results Of the 1,437 patients approached, 1,106 participated. The prevalence of sleep disturbances was 79.6%. Key factors associated with sleep issues included relationship status (Chi2 = 13.39; p = 0.01), primary mental health diagnoses (Chi2 = 61.35; p < 0.001), anxiety (Chi2 = 80.28; p < 0.001), and poor wellbeing (Chi2 = 82.18; p < 0.001) at baseline. Conclusion The study reveals a high prevalence of sleep disturbances among patients preparing for discharge and identifies key risk factors. These findings underscore the need for targeted interventions to address sleep-related issues during the discharge transition, improving recovery outcomes and reintegration into the community.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".