Reliability and validity of scenario-specific versus generic simulation assessment rubrics
Bibliographic record
Abstract
Background: This study assessed reliability and validity of scenario-specific and generic simulation assessment rubrics used in two different deteriorating patient simulations, and explored learner and instructor preferences.Methods: Learner performance was rated independently by three instructors using two rubrics.Results: A convenience sample of 29 nursing students was recruited. Inter-rater reliability was similar but slightly higher for the generic rubric than the scenario-specific learning outcomes assessment rubric (ICC = .759 vs .748 and IRR = .693 vs .641) for two different scenarios. Most students found the scenario-specific rubric more helpful to their learning (59%), and easier to use (52%). Instructors (3/3) found the scenario-specific rubric more helpful to guide debriefing.Conclusions: Scenario-specific rubrics may be more valuable for learners to help them identify their own knowledge and performance gaps and assist them in their preparation for simulation. Additionally, scenario-specific rubrics provide direction for both learners and instructors during debriefing sessions.
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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 | 0.044 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".