Stepped‐Care Management of Insomnia: Treatment Choices Guided by a Patient Decision Aid in a Pragmatic Nonrandomized Clinical Trial Setting
Bibliographic record
Abstract
Patients with insomnia face difficult decisions when choosing between treatment options with competing risk-benefit profiles. Patient treatment choices were evaluated as part of a pragmatic nonrandomized clinical trial for a two-step cognitive behavioural therapy for insomnia (CBT-I) intervention. Upon enrollment, participants were guided by a patient decision aid (PtDA), outlining the risk-benefit profiles of medication, face-to-face CBT-I (FtFCBT-I), and digital CBT-I (dCBT-I). In Step 1, participants chose between dCBT-I alone, combined dCBT-I plus medication or medication alone. Non-remitters who enrolled into Step 2 chose between FtFCBT-I, medication, or no additional treatment. A secondary analysis was conducted evaluating patient treatment choices, the presence of decisional conflict, and the acceptability of the PtDA. In Step 1, 47.4% (n = 73) of participants chose dCBT-I, followed by combined dCBT-I plus medication (42.3% n = 66) and medication alone (9.74%; n = 15). The dCBT-I group was less likely to use medications or used them less frequently compared to the other treatment groups. Men and individuals less motivated to change sleep habits were more likely to choose medication in Step 1. In Step 2, 60.9% (n = 42) of non-remitters chose FtFCBT-I, followed by no additional treatment (23.6%; n = 16) and medication (15.9%; n = 11). Non-remitters from the medication group in Step 1 were more likely to choose medication again in Step 2. Over 90% of participants across both treatment steps endorsed the PtDA as acceptable and facilitated their decision-making. This corroborated with the minimal decisional conflict observed, highlighting a potential role in further developing and integrating patient decision aids into practice. Trial Registration: NCT03633305.
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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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".