Students’ evaluation of a computerized patient simulator in nursing education and its effect on the results of preclinical tests
Bibliographic record
Abstract
The aim of the study was two-fold: to evaluate nursing students’ experiences of active participation in the use of a-computerized simulation manikin during preclinical first-year Bachelor’s studies, and to evaluate the effect of active participation in simulation by comparing active students’ result with observers’ result on preclinical test. An evaluative case study design was used to evaluate simulation with a computerized manikin as a pedagogical learning method. A questionnaire was used to evaluate the active students’ experiences. The second part was a comparison between the active students’ and the observers’ preclinical test results. Findings indicated that the students thought simulation was beneficial, feedback from peers and lecturer was helpful and reflection during debriefing was beneficial. A significant difference was seen between those students who actively participated and those who observed in relation to the pass/fail preclinical test. Nursing students experienced simulation with a computerized manikin as being a beneficial pedagogical learning method, and active participation in a simulation situation can help students pass their preclinical test.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".