Development and validation of the QASSH scale: a tool for assessing the quality of simulation scenarios in healthcare education
Bibliographic record
Abstract
Simulation-based education has become essential for pre- and post-graduate training of healthcare professionals. However, there is no tool to help simulation educators or program managers in assessing the educational quality of simulation scenario scripts for team-based immersive simulation (IS), simulated participants (SP) and procedural simulation (PS). To that end, we developed the Quality Assessment of Simulation Scenario in Healthcare (QASSH) tool. This study aims at providing validity evidence for QASSH. We set up a francophone group of experts within the French-speaking Society for Simulation in Healthcare (SoFraSimS) network and designed this scale based on recently published best practices and our long experience in conceiving simulation scenarios. We tested it by submitting three scenarios of high, borderline and low quality for assessment to a group of experts, a third of which were involved in its development. Analysis of reliability and validity of the QASSH was done using the Standards for educational and psychological testing. Generalizability theory (GT) was used to assess the internal structure and reliability of the tool. The absolute reliability coefficients (G coefficients) calculated through GT were: 0.97 (IS), 0.96 (SP), and 0.98 (PS). G-facet analyses showed that no removal of a single item of QASSH significantly increased the G coefficient above 0.01 for any of the three variants. Cronbach's alpha coefficients were 0.94 (IS), 0.94 (SP) and 0.97 (PS). Estimating the impact of the number of raters on reliability (i.e. D-studies) showed that two raters were enough to achieve a G coefficient above 0.85. The G study shows a high generalizability coefficient (≥0.90), which demonstrates high reliability. The response process evidence for validity provides evidence that no error was associated with using the instrument and its reliability was high with two raters. The QASSH is a tool to assess the quality of healthcare simulation scenarios and will be helpful to instructors wishing to build effective IS, PS and SPs scenarios.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".