Evaluating self-efficacy and personality differences of nursing students in clinical simulation
Bibliographic record
Abstract
Background: This present research was conducted to evaluate the efficacy of a clinical simulation where senior nursing students cared for a standardized patient with Autism Spectrum Disorder (ASD). The goal of the simulation was to teach the nursing students how to work with children with autism. In addition, the study aimed to determine if individual differences in personality affect students’ abilities to complete the simulation and how a student’s personality may affect their perceptions of the simulation. Projected outcomes included learning the use of appropriate communication strategies, improved assessment skills, prioritization of care, development of problem-solving skills, and decision-making abilities when dealing with children with ASD.Methods: Simulations are verified as effective training mechanisms to increase students’ self-efficacy in multiple nursing settings. Therefore, seventy-five senior baccalaureate nursing students completed the standardized patient simulation for care of an individual with ASD. The effect on the students’ self-efficacy was measured using the Occupational and Academic Self-Efficacy for Nursing Measure, the IPEP-NEO short form, and an ASD simulation study questionnaire.Results and conclusions: Logistic regression was used to investigate the relationship between personality measures and experience with ASD. The higher the openness and extraversion scores the more likely respondents were to disclose positive benefits in relation to expectations, communication strategies, teamwork, and reflection.
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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".