When is a randomised controlled trial required: the theoretical domains framework approach
Bibliographic record
Abstract
When evidence of a potentially promising intervention starts to accumulate it is often difficult to know whether the evidence is strong enough to move to promote widespread adoption or whether any, or further, randomised trials are required. We propose that the Theoretical Domains Framework, or TDF, is a useful tool to guide whether further randomised trials require to be undertaken. The TDF is a theory-informed framework developed in the field of health psychology, that allows the systematic assessment of constructs likely to affect health professionals adoption/use of the intervention under consideration. It assesses twelve separate domains that may affect health professionals readiness to adopt a treatment or change their behaviour. Depending on the profile of responses to the TDF, decision rules can be generated to determine whether further effectiveness research is still required. We recently adopted the TDF approach in a critical care setting exploring whether further randomised trials of a particular treatment (selective decontamination of the digestive tract) were deemed to be required (and, if so, additional questions identified what particular aspects should be addressed). This mixed-methods international study involving research groups in the UK, Canada, Australia and New Zealand highlighted the usefulness of the TDF approach in providing an evidence-based judgement on whether a randomised trial should be initiated. We will explain the TDF approach, how it can be adopted to identify whether further trials are required, and demonstrate its use in practice.
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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 | 0.600 | 0.770 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.033 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".