Building consensus for the future of paediatric simulation: a novel ‘KJ Reverse-Merlin’ methodology
Bibliographic record
Abstract
Objectives: This project aims to identify guiding strategic principles to optimise simulation-based educational impact on learning, patient safety and child health. Methods: Study participants included 39 simulation experts who used a novel 'KJ Reverse-Merlin' consensus process in the systematic identification of barriers to success in simulation, grouped them in themes and subsequently identified solutions for each theme. Results: 193 unique factors were identified and clustered into 6 affinity groups. 6 key consensus strategies were identified: (1) allocate limited resources by engaging health systems partners to define education and research priorities; (2) conduct and publish rigorous translational and cost-effectiveness research; (3) foster collaborative multidisciplinary research and education networks; (4) design simulation solutions with systems integration and sustainability in mind; (5) leverage partnerships with industry for simulation, medical and educational technology; (6) advocate to engage the education community, research funding agencies and regulatory bodies. Conclusions: Simulation can be used as a research, quality improvement and or educational tool aimed at improving the quality of care provided to children. However, without organisation, strategy, prioritisation and collaboration, the simulation community runs the risk of wasting resources, duplicating and misdirecting the efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.196 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| 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".