The relationship between subjective sleep disturbances and illness insight in individuals with alcohol‐related cognitive disorders
Bibliographic record
Abstract
Abstract Background Sleep disturbances are often observed in people with alcohol‐related cognitive disorders. In addition to their cognitive disorders, illness insight may be compromised in these patients, complicating addiction treatment adherence. Aim of this study was to examine the relationship between illness insight and subjective sleep disturbances in individuals with alcohol use disorder (AUD) without cognitive impairment, individuals with alcohol‐related cognitive impairments (ARCI) and people with Korsakoff’s syndrome (KS) in a convenience sample. In addition, this study examines the role of illness insight in dysfunctional beliefs and attitudes about sleep. Methods Twenty‐four individuals with KS, 17 individuals with ARCI and 21 individuals with AUD participated in this study. Illness insight was measured using the Q8 questionnaire. Subjective sleep disturbances were measured using the Pittsburg Sleep Quality Index and the Dysfunctional Beliefs and Attitudes about Sleep questionnaire. General cognitive functioning was assessed with the Montreal Cognitive Assessment. Results Comparable levels of subjective sleep disturbances across the three diagnostic groups were found. Also, no significant relation was found between subjective sleep disturbances and illness insight in the three groups. Conclusion This study suggests that the level of illness insight does not appear to play a role in the self‐report of sleep disturbances.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".