Attrition in Randomized and Preference Trials of Behavioural Treatments for Insomnia
Bibliographic record
Abstract
Preferences for treatment contribute to attrition. Providing participants with their preferred treatment, as done in a partially randomized clinical or preference trial (PRCT), is a means to mitigate the influence of treatment preferences on attrition. This study examined attrition in an RCT and a PRCT. Persons with insomnia were randomly assigned (n = 150) or allocated (n = 198) to the preferred treatment. The number of dropouts at different time points in the study arms was documented and the influence of participant characteristics and treatment-related factors on attrition was examined. The overall attrition rate was higher in the RCT arm (46%) than in the PRCT arm (33%). In both arms, differences in sociodemographic and clinical characteristics were found between dropouts and completers. The type of treatment significantly predicted attrition (all p ≤ .05). The results provide some evidence of a lower attrition rate in the PRCT arm, supporting the benefit of accounting for preferences as a method of treatment allocation.
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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 | 0.331 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".