Patterns of Enrollment in Randomized and Preference Trials of Behavioral Treatments for Insomnia
Bibliographic record
Abstract
Participants’ preferences for treatment may deter enrollment in a randomized clinical trial (RCT). The Partially randomized clinical trial (PRCT) is proposed as an alternative design to increase enrollment rate and enhance representativeness of the sample. There is limited evidence supporting the advantages of the PRCT. This study aimed to examine enrollment and refusal rates, reasons for refusal, and clinical profile of persons who declined participation and those who enrolled, in the context of a RCT and a PRCT. Persons with chronic insomnia completed a questionnaire to determine if they met the eligibility criteria regarding type, frequency, and duration of insomnia. Those who declined participation indicated reasons for refusal. Enrollment rate was computed as the percentage of individuals who took part in the study out of those found eligible. Independent sample t-test was used to compare enrollees and non-enrollees on characteristics of insomnia. The results showed a higher enrollment rate in the RCT than PRCT. Reasons for refusal were similar under the RCT and PRCT. Significant differences between enrollees and non-enrollees were found on fewer characteristics in the RCT than PRCT. The results do not support the advantages of the PRCT in enhancing enrollment of participants in studies evaluating the effectiveness of behavioral treatments of chronic insomnia.
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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 | 0.471 | 0.563 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".