What predicts patients’ perceptions of improvement in insomnia?
Bibliographic record
Abstract
Although there has been considerable research into the effectiveness of individual cognitive behavioral treatment for chronic insomnia, less is known about patients' perceptions of what constitutes actual improvement. This study utilized 70 outpatients (mean age = 49.7 years, SD = 12.0) with insomnia who completed a 6-week cognitive behavioral group for sleep. Participants completed a number of primary (Pittsburgh Sleep Quality Index) and secondary measures (the Dysfunctional Beliefs about Sleep Scale, Insomnia Severity Index, Beck Depression Inventory, Penn State Worry Questionnaire) at pre- and post-treatment. Perceived improvement was measured using the Clinical Global Improvement Scale (CGI). Results were analyzed using a combination of Logistic Regression analysis and receiver operating curve characteristic analysis (ROC). Results demonstrated that sleep quality and sleep duration were the most sensitive primary measures, or best predicted perceived improvement, whereas sleep efficiency was the most specific primary measure, or best predicted perceived lack of improvement (defined as only mild improvement). Of the secondary measures, results showed that daytime impairment was the most sensitive predictor of perceived improvement and that mood was the most specific predictor of perceived lack of improvement. Implications of these findings are that sleep quality, sleep duration, and sleep efficiency may offer different types of information and the choice of measure for predicting global improvement in insomnia will depend on the needs of the researcher/clinician.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".