Practical trials in medical education: linking theory, practice and decision making
Bibliographic record
Abstract
CONTEXT: Concerns have been raised over the gap between education theory and practice and how research can contribute to inform decision makers on their choices and priorities. Little is known about how educational theories and research outcomes produced under optimal conditions in highly controlled settings generalise to the real-life education context. One way of bridging this gap is applying the concept of practical trials in medical education. In this paper we elaborate on characteristics of practical trials and based on examples from medical education we discuss the challenges, limitations and future directions for this kind of research. CURRENT STATE: Practical trials have the overall aim of informing decision makers. They are carried out in real-life settings and are characterised by (i) comparison of viable alternative education strategies, (ii) broad inclusion criteria regarding participants across several settings and (iii) multiple outcome measures with long-term follow-up to evaluate both benefits and risks. Questions posed by practical trials may be proactive in applying theory in the development of educational innovations or reactive to educational reforms and innovations. Non-inferiority or equivalence designs are recommended when comparing viable alternatives and the use of crossover designs, cluster randomisation or stepped wedge trial designs are feasible when studying implementations across several settings. Outcome measures may include variables related to learners, teachers, educational administration, quality of care, patient outcomes and cost. CONCLUSIONS: Practical trials in medical education may contribute to bridge the gap between education theory and practice and aid decision makers in making evidence-based choices and priorities. Conducting practical trials is not without challenges and rigorous design and methods must be applied. Of concern is that the practical focus may lead to failure to include a sound theoretical basis in the research questions and the interventions studied, and that authors fail to obtain informed consent from their participants.
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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 | 0.731 | 0.827 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.068 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.025 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".