INACSL Standards of Best Practice for Simulation: Past, Present, and Future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it