Recommendations to bolster adherence in cognitive behavioral therapy for insomnia: a self-efficacy approach
Bibliographic record
Abstract
Lay Summary The safest and most effective treatment for chronic insomnia is Cognitive Behavioural Therapy for Insomnia (CBT-I). People sometimes struggle to adhere to, or follow the steps, involved in CBT-I because they are challenging (i.e., restricting time in bed to induce sleepiness, getting out of bed when not sleeping). These steps are based on sleep science and research shows that more closely adhering to them relates to better sleep improvements. One way that clinicians can help patients completing CBT-I improve their adherence to the difficult treatment steps is to promote their self-efficacy, or belief that they can effectively complete the treatment steps. Inspired by tried-and-true health promotion techniques rooted in Social Cognitive Theory, this paper describes concrete recommendations that clinicians can use to improve their patients’ self-efficacy when completing CBT-I. These recommendations include suggestions such as setting positive treatment expectations, discussing with patients how to set up for success when completing the treatment steps at home, and how to work with potential barriers to treatment. If clinicians can help boost patients’ self-efficacy, they may be able to carry out the treatment steps of CBT-I more effectively, and ultimately experience more benefits.
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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 | 0.040 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".