Contribution of treatment acceptability to acceptance of randomization: an exploration
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Randomization to treatment is viewed unfavourably by many trial participants. There is limited research that investigated factors contributing to acceptance of randomization. This study explored the influence of participants' socio-demographic and clinical characteristics, and their perceived acceptability of the treatments on their acceptance of randomization (i.e. willingness to be randomized) in a clinical trial. METHODS: Persons with insomnia (n = 383) were asked about their acceptance of randomization before and after they rated the acceptability of behavioural therapies for managing insomnia (sleep education and hygiene booklet, stimulus control therapy and sleep restriction therapy). Socio-demographic and clinical characteristics, and treatment acceptability, were measured with established instruments. Logistic regression was applied to explore the association between participants' characteristics and treatment acceptability, and reported acceptance of randomization. RESULTS: Prior to rating treatments' acceptability, 54.6% of participants were willing to be randomized; socio-demographic (age and ethnicity) and clinical (severity of insomnia's impact, state anxiety, depression, vitality and mental and social functions) contributed to acceptance of randomization. After rating the treatments' acceptability, 87.8% of participants were unwilling to be randomized; age, severity of insomnia's impact and acceptability of behavioural therapy were significantly associated with acceptance of randomization. CONCLUSIONS: The study findings indicated that participants are likely to express unwillingness to be randomized once they receive treatment information and rate the acceptability of treatments. The reported non-acceptance may influence participants' behaviour (e.g. withdrawal, non-adherence) during the trial, suggesting the need to explore alternative designs for intervention evaluation.
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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 | 0.332 | 0.598 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".