The Relationship Among Sleep Quality, the Stages of Change Readiness and Treatment Eagerness Scale, Abstinence Self-efficacy, and Quality of Life with Alcohol Use Disorder in South Korea
Bibliographic record
Abstract
Many patients with alcohol use disorder experienced insomnia or sleep disturbances. However, their sleep problems rarely addressed in the treatment process. It may prove beneficial if treatment programs should intend to help prevent the recurrence of alcohol use disorder by solving patients’ sleep-induced problems and accordingly include appropriate sleep interventions. The present study employed a descriptive design and conducted a cross-sectional survey to assess the relationship among sleep quality, score on the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES), abstinence self-efficacy, and quality of life in inpatients with alcohol use disorders. Data were collected from June to August 2018, from 117 patients admitted to the psychiatric ward for alcohol-use patients in two mental hospitals in South Korea. Sleep quality was significantly correlated with the SOCRATES score (r = .247, p = .007) and quality of life (r = -.346, p = .001). However, it showed no relationship with abstinence self-efficacy (r = -.066, p = .477). These findings suggest that abstinence programs need to employ a comprehensive approach instead of primarily focusing on maintaining abstinence and cessation of alcohol use. However, both sleep disturbances and alcohol abstinence require patience and prolonged treatment. Thus, it is a challenge to design concrete interventions to address the sleep problems experienced by patients with alcohol use disorder.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".