SP4 Efficacy Across Three Simulation Models Used to Teach Nursing Students Complex Cardiac Pain Management: A RCT
Bibliographic record
Abstract
Purpose: Health care professionals have misbeliefs that block effective cardiac pain assessment and management. While standardized patients (SP) have been used effectively to improve nursing students' interview skills and knowledge, they can be expensive. This randomized controlled trial pilot tested two alternate simulation methods versus SPs for improving nursing students' knowledge of cardiac pain-related misbeliefs and assessment skills including classroom-based simulation training (CBS) and deteriorating patient-based simulation (DPS). Methods: Design. Students (N=149) were randomized to SP, CBS or DPS simulation, each lasting 3 hours. Measures. Pre and post-test pain-related misbeliefs were measured using the Pain Beliefs Scale (PBS); students' perceived satisfaction and quality of simulation were secondary outcomes measured by the Student Satisfaction with Learning Scale (SSLS) and the Simulation Design Scale (SDS) respectively. Analyses. ANCOVA tested for overall differences in pain-related misbeliefs between treatment arms. Oneway ANOVA tested for overall group differences in post-test SSLS and SDS scores. Results: At post-test, students who underwent DPS had significantly higher scores for a) knowledge of cardiac pain-related misbeliefs than those who worked with SPs [F=10.26(2,134), p<0.001], and b) significantly higher SSLS scores than both the SP and CBS groups [F=27.08(2,135), p<0.001]. With respect to perceived quality of simulation, DPS and SP group scores were similar and significantly higher than the CBS group scores [F=6.52(2,128), p=0.02].
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".