Bibliographic record
Abstract
Introduction: Accounting for treatment preferences is beneficial in practice, it increases adherence to treatment and improves health outcomes. The randomized controlled trial (RCT) is considered the most robust in generating valid evidence on effectiveness, yet it ignores participants’ preferences for treatment. This scoping review addressed three questions: 1) How are treatment preferences conceptualized in intervention research? 2) To what extent do treatment preferences affect participants’ enrollment in trials, withdrawal from the study, adherence to treatment, and outcomes? And 3) What designs are used to account for treatment preferences in intervention evaluation research? Methods: The first five steps of the scoping review methodology framework were applied: 1) identifying the research questions; 2) searching the literature; 3) selecting articles; 4) charting data; and 5) summarizing findings. Results: Treatment preferences refer to choice treatment; they are shaped by participants’ beliefs and appraisal of the interventions. Evidence from reviews and primary studies indicated that offering participants the opportunity to choose and receive the preferred treatment enhances enrollment and reduces withdrawal in trials; however, the evidence regarding the influence of treatment preferences on adherence to treatment and improvement in outcomes is inconclusive. Designs that account for treatment preferences include: RCT, RCT with a comprehensive cohort, partially randomized preference trial, and two-stage partially randomized trial. Conclusion: The pattern of results may be attributed to the methods for assessing treatment preferences. A systematic method for assessing preferences is recommended.
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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 | 0.162 | 0.422 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.038 | 0.040 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".