Piloting Simulations: A Systematic Refinement Strategy
Bibliographic record
Abstract
Introduction Few approaches articulate a systematic way to address confusing, missing, or underdeveloped simulation design features prior to implementing into coursework. To address this gap, we tested a novel, systematic refinement strategy to improve the design elements of two simulations. Methods Forty eligible participants (Year 3 undergraduate nursing students) evaluated two simulation scenarios (each followed by a debriefing session) through a novel and systematic refinement strategy across five iterations. Each simulation was evaluated using the validated Simulation Design Survey (SDS). Ratings were analyzed using descriptive data. Students also responded to an open-ended question in order to provide qualitative feedback regarding how to improve its features, i.e., scenario design and debriefing components. Written comments by students were analyzed using the principles of qualitative content analysis. Results Descriptive statistics revealed a gradual increase in the mean scores of the SDS over each of the simulation refinement periods. For the first simulation, the SDS mean score reached a high on Day 5 of 4.86 (standard deviation (SD) = 0.14) in contrast to a score of 3.45 (SD = 0.17) on Day 1. For the second simulation, the SDS mean score was 4.75 (SD = 0.16) on Day 5, which represented a mean score increase of 1.01 from the score on Day 1. Conclusions This novel refinement strategy improved the overall design elements of each of the simulations. The potential use of the SDS and open-ended feedback, guided by a refinement approach, merits further investigation.
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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 | 0.212 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".