Evolving With the Science of Simulation: Updated Guidelines for Authors
Bibliographic record
Abstract
Simulation in Healthcare has always been a place where our simulation community shares new ideas, discoveries, and innovations. As the official journal of the Society for Simulation in Healthcare, our mission reflects the society's broader purpose: serving our members through education, professional development, and the advancement of research and innovation; promoting the profession of healthcare simulation through standards and ethics; and championing healthcare simulation through advocacy, sharing, facilitation, and collaboration. Our vision is to serve a global community of practice that enhances the quality of health care. We are proud to be multidisciplinary and to welcome scholarship that spans safety and quality training, competency assessment, educational standards, simulation pedagogy, translational simulation, simulation technologies, and beyond. Our pages reflect the creativity and dedication of a field that is growing rapidly and shaping healthcare worldwide. Simulation is an evolving science. As the science of simulation continues to grow, our journal must evolve with it. To keep pace with the excitement and energy in our field, we are launching updated author guidelines and a refreshed set of article types. These changes provide greater clarity and more detailed instruction, helping authors structure their work in ways that align with how we aim to advance the science of simulation. WHAT'S NEW AND EXCITING Article types that fit the field. We have streamlined our categories into Original Research, Reviews, Innovations and Emerging Practices, Reflections, and Concepts and Commentaries. Each now comes with clear expectations for structure, word length, and abstracts or summary statements. Together, these categories highlight where simulation scholarship is progressing. Stronger expectations for validation. Innovations and evaluative work must include evidence of feasibility, usability, acceptability, or impact, guided by resources such as Barnes'1 paper addressing evaluative validity, or Calhoun and Scerbo's2 validation guide. These expectations reflect our commitment to moving beyond description toward evidence-informed innovation, ensuring that new tools and practices can be trusted, adapted, and built upon by the simulation community. AI transparency. As AI tools become part of academic work, we require authors to disclose their use in preparing manuscripts. This ensures accountability and trust in what is published, and it reinforces the principle that authors remain responsible for the accuracy, originality, and integrity of their scholarship. Shared language. By encouraging use of the Healthcare Simulation Dictionary3 and requiring references with available DOIs, we help create consistency across the field. Clarity for all. Revised expectations for abstracts, summary statements, and manuscript structure are designed to make contributions more transparent and accessible. Clearer structures help readers quickly grasp the significance of a paper and assist reviewers in providing more focused and fair evaluations. For readers, this also means that important insights can be more easily identified and applied to research, education, and practice. TRANSITION PLAN If you already have a manuscript in our system, it will continue under the previous categories and requirements. These updates apply only to new submissions going forward. LOOKING AHEAD This update is about more than new instructions. It is about ensuring that Simulation in Healthcare continues to grow with the science of simulation itself. By strengthening expectations for validation, refining how work is structured, standardizing language, and embracing transparency around new tools, we are positioning the journal to support the next stage of simulation scholarship. Our hope is that these changes will provide authors with clarity in how they shape their work, support reviewers with consistency in their assessments, and give readers confidence that every article they encounter is rigorous, trustworthy, and relevant. As a global and multidisciplinary journal, we are committed to publishing work that resonates across specialties, professions, and regions, ensuring that simulation scholarship has reach and impact worldwide. Together, these updates strengthen the role of Simulation in Healthcare as the place where simulation scholarship grows, connects, and advances. Just as simulation equips healthcare professionals to take on new and complex challenges, these updated guidelines position the journal to share scholarship that pushes our field forward and ensures its lasting impact.
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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 | 0.173 | 0.538 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.024 | 0.081 |
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".