Immersion Clinical Simulation (ICS): what is a real Impact on knowledge acquisition?
Bibliographic record
Abstract
Background: For several decades, ICS has been one of various teaching strategies aimed at increasing students’ knowledge contain. To this day, the impact of ICS on knowledge acquisition cannot be fully ascertained because there are a lot methodological limitations: students’ self-reporting, identical pretest and post-test examinations, use of a single post-test, or absence of a control group. However, all of these situations affect the methodological quality of the studies and theirs conclusions. Indeed, no studies have compared the impact of ICS on knowledge acquisition from two group when perform the equivalent but not similar Multiple Choice Question (MCQ) exam. Methods: This prospective, multicentre study is based on a quasiexperimental research. The participants in the experimental group were taught using a series of four progressive ICS including debriefing session in addition to internship, while those in the control group were taught using internship alone. Before testing, we developed and validated two similar exams about cardiology knowledge with a RASH method. The knowledge of the participants was assessed twice based on MCQ about their cardiology knowledge: version A in pretest conditions (before internship and ICS) and version B in post-test conditions. Each MCQ, about cardiology knowledge, consists of 35 items, including seven common items. Results: A total of 177 nursing students (N=177) were involved in this research project, including 93 (n=93) in the experimental group and 84 (n=84) in the control group. Under pretest conditions, the results obtained by the two groups on version A of the exam questionnaire were found statistically equivalent (p=.63). Under post-test conditions, participants in the experimental group scored significantly higher (p=.002). Conclusion: The results of this research further more confirm the impact of simulation on knowledge acquisition.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".