Effectiveness of Group Cognitive Behavioral Therapy for Insomnia (CBT-I) in a Primary Care Setting
Bibliographic record
Abstract
Objective/Background: Primary care is where many patients with insomnia first ask for professional help. Cognitive-behavioral therapy for insomnia (CBT-I) is the recommended treatment for chronic insomnia. Although CBT-I’s efficacy is well established, its effectiveness in real-life primary care has seldom been investigated. We examined the effectiveness of CBT-I as routinely delivered in a Canadian primary care setting. Participants: The patients were 70 women and 11 men (mean age = 57.0 years, SD = 12.3); 83% had medical comorbidity. Methods: For the first 81 patients who took the six-session group program we compared initial and postprogram sleep diaries, sleep medication use, Insomnia Severity Index (ISI), the Hospital Anxiety and Depression Scale (HADS), and visits to the family physician. Results: Sleep onset latency, wake after sleep onset, total sleep time, sleep efficiency, and ISI scores improved significantly (p < .001). Mood ratings also improved (p < .001). Use of sleep medication decreased (p < .001). Effect sizes were medium to large. Eighty-eight percent of patients no longer had clinically significant insomnia (ISI score ≤ 14) by the last session; 61% showed at least “moderate” improvement (ISI score reduction > 7). Wait-list data from 42 patients showed minimal sleep and mood improvements with the passage of time. Number of visits to the family physician six months postprogram decreased, although not significantly (p = .108). Conclusions: The CBT-I program was associated with improvement on all sleep and mood measures. Effect sizes were similar to, or larger than, those found in randomized controlled trials, demonstrating the real-world effectiveness of CBT-I in an interdisciplinary primary care setting.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".