Poor sleep quality may contribute to dysfunctional illness perception, physical and emotional distress in hospitalised patients: results of a national survey of the Italian Society of Consultation‐Liaison Psychiatry
Bibliographic record
Abstract
Distress associated with physical illness is a well-known risk factor for adverse illness course in general hospitals. Understanding the factors contributing to it should be a priority and among them dysfunctional illness perception and poor sleep quality may contribute to it. As poor sleep quality is recognised as a major risk factor for health problems, we aimed to study its association with illness perception and levels of distress during hospitalisation. This cross-sectional study included a consecutive series of 409 individuals who were hospitalised in medical and surgical units of different hospitals located throughout the Italian national territory and required an assessment for psychopathological conditions. Sleep quality was assessed with the Pittsburgh (Sleep Quality Index), emotional and physical distress with the Edmonton Symptom Assessment System (ESAS), and illness perception with the Brief Illness Perception Questionnaire (BIPQ). Differences between groups, correlations and mediations analyses were computed. Patients with poor sleep quality were more frequently females, with psychiatric comorbidity, with higher scores in the ESAS and BIPQ. Poor sleep quality was related to dysfunctional illness perception, and to both emotional and physical distress. In particular, by affecting cognitive components of illness perception, poor sleep quality may, directly and indirectly, predict high levels of distress during hospitalisation. Poor sleep quality may affect >70% of hospitalised patients and may favour dysfunctional illness perception and emotional/physical distress.Assessing and treating sleep problems in hospitalised patients should be included in the routine of hospitalised patients.
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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 | 0.003 |
| 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 | 0.001 | 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".