Examining Self-Efficacy and Self-Esteem in Healthcare Students Participating in an Interprofessional Critical Care Simulation
Bibliographic record
Abstract
BACKGROUND: Interprofessional education (IPE) is becoming increasingly encouraged in healthcare. However, a lack of consensus exists in how IPE should be provided. The research at hand examines changes in self-efficacy and self-esteem in 132 nursing, dietetic, physician assistant, and social work students when participating in a critical care simulation. The simulation focused on a septic patient requiring cardiopulmonary resuscitation.METHOD: This quantitative, repeated measures and correlational study utilized the Generalized Self-Efficacy Scale and Rosenberg Self-Esteem Scale to examine a possible relationship between self-efficacy and self-esteem. In addition, exploration of changes in self-efficacy and self-esteem after participating in the cardiopulmonary simulation was conducted.RESULTS: There was a statistically significant medium, positive correlational relationship between self-efficacy and self-esteem in healthcare students participating in a cardiopulmonary resuscitation simulation (p<0.001). Healthcare students did not experience statistically significant gains in self-efficacy and self-esteem (p>0.05). During debriefing, students expressed experiencing role confusion when working with each other.CONCLUSION: More IPE experiences, including in mock code simulations, is necessary to enhance communication, collaboration, and prevent role confusion.HIGHLIGHTS:More interprofessional education is necessary to prevent role confusionSelf-efficacy and self-esteem have a positive correlation in IPE critical care simulationHealthcare students did not have significant gains in self-efficacy and self-esteem
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".