Treating Sleep Problems in Patients with Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Sleep disturbance is increasingly recognized as a major problem for patients with schizophrenia but it is rarely the direct focus of treatment. The main recommended treatment for insomnia is cognitive behavioural therapy, which we have been evaluating for patients with current delusions and hallucinations in the context of non-affective psychosis. AIMS: In this article we describe the lessons we have learned about clinical presentations of sleep problems in schizophrenia and the adaptations to intervention that we recommend for patients with current delusions and hallucinations. METHOD: Twelve factors that may particularly contribute to sleep problems in schizophrenia are identified. These include delusions and hallucinations interfering with sleep, attempts to use sleep as an escape from voices, circadian rhythm disruption, insufficient daytime activity, and fear of the bed, based upon past adverse experiences. Specific adaptations for psychological treatment related to each factor are described. CONCLUSIONS: Our experience is that patients want help to improve their sleep; sleep problems in schizophrenia should be treated with evidence-based interventions, and that the interventions may have the added benefit of lessening the psychotic experiences. A treatment technique hierarchy is proposed for ease of translation to clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".