Integrating a learning outcomes assessment rubric into a deteriorating patient simulation for undergraduate nursing students
Bibliographic record
Abstract
Background: Few studies have examined effective methods to prepare learners to participate in simulation-based learning experiences. Similarly, there is limited literature on valid, reliable assessment methods to determine whether clinical simulation learning outcomes have been met. We developed a learning outcomes assessment rubric to support self-regulated learning and assessment during presimulation preparation and debriefing.Methods: Fourth-year undergraduate nursing students enrolled in a critical care nursing course participated in two deteriorating patient simulations, one delivered in a traditional format, and the other using a new format incorporating a learning outcomes assessment rubric into presimulation preparation and debriefing. A descriptive survey evaluated learner perceived competence with deteriorating patients and satisfaction with the two simulations formats. Learner self-assessment data using the rubric was collected pre and post simulation.Results: Learner satisfaction with the deteriorating patient scenario and accompanying assessment rubric was very high. Learners were significantly more satisfied with the simulation scenario delivered using the new format which included the assessment rubric than with the standard format without the assessment rubric (p < .001). Learners valued the opportunity to identify their own learning needs, and reported increased competence in management of a deteriorating patient following the simulation (p < .001).Conclusions: Senior nursing students perceived that integration of learning outcomes assessment rubrics into simulation design enhanced their self-regulated learning and presimulation preparation. Further research is needed to explore presimulation preparation strategies and to validate rubrics used for summative assessment.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".