INACSL Standards of Best Practice for Simulation: Past, Present, and Future
Bibliographic record
Abstract
AIM: To describe the historical evolution of the International Nursing Association for Clinical Simulation and Learning's (INACSL) Standards of Best Practice: Simulation. BACKGROUND: The establishment of simulation standards began as a concerted effort by the INACSL Board of Directors in 2010 to provide best practices to design, conduct, and evaluate simulation activities in order to advance the science of simulation as a teaching methodology. METHOD: A comprehensive review of the evolution of INACSL Standards of Best Practice: Simulation was conducted using journal publications, the INACSL website, INACSL member survey, and reports from members of the INACSL Standards Committee. RESULTS: The initial seven standards, published in 2011, were reviewed and revised in 2013. Two new standards were published in 2015. The standards will continue to evolve as the science of simulation advances. CONCLUSION: As the use of simulation-based experiences increases, the INACSL Standards of Best Practice: Simulation are foundational to standardizing language, behaviors, and curricular design for facilitators and learners.
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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 | 0.174 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".