Examining the influence of treatment preferences on attrition, adherence and outcomes: a protocol for a two-stage partially randomized trial
Bibliographic record
Abstract
BACKGROUND: Empirical evidence pertaining to the influence of treatment preferences on attrition, adherence and outcomes in intervention evaluation trials is inconsistent. The inconsistency can be explained by the method used for allocating treatment and measuring preferences. The current methodological study is designed to address these factors by implementing the two-stage partially randomized or preference trial design, and administering a validated measure to assess participants' preferences for the treatments under evaluation. It aims to compare attrition, adherence and outcomes for participants allocated randomly or by preference to treatment. The study is in its final stages of data collection; its protocol is presented in this paper. METHODS/DESIGN: A partially randomized clinical or preference trial is used. Eligible participants are randomized to two trial arms. First is the random arm involving random assignment to treatments, and second is the preference arm involving allocation to the chosen treatment. Participants with chronic insomnia are targeted. Two behavioral treatments are offered, stimulus control therapy and sleep restriction therapy, in the same format (small group) and dose (two sessions given over a 4-week period). A participant log is used to collect data on attrition. Adherence is evaluated in terms of exposure and enactment of treatment. Sleep-related outcomes (sleep parameters and perceived insomnia severity) are measured at pretest, posttest, 6 and 12 month follow-up. Treatment preferences, adherence and outcomes are assessed with reliable and valid measures. DISCUSSION: The advantages and limitations of the preference trial design are highlighted. The challenges in implementing the trial are discussed relative to the distribution of participants in the groups defined by treatment received and method of treatment allocation. TRIAL REGISTRATION: ClinicalTrials.gov Registry NCT02513017.
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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 | 0.242 | 0.240 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".