Evaluating the impact of high‐ and low‐fidelity instruction in the development of auscultation skills
Bibliographic record
Abstract
CONTEXT: A principal justification for the use of high-fidelity (HF) simulation is that, because it is closer to reality, students will be more motivated to learn and, consequently, will be better able to transfer their learning to real patients. However, the increased authenticity is accompanied by greater complexity, which may reduce learning, and variability in the presentation of a condition on an HF simulator is typically restricted. OBJECTIVES: This study was conducted to explore the effectiveness of HF and low-fidelity (LF) simulation for learning within the clinical education and practice domains of cardiac and respiratory auscultation and physical assessment skills. METHODS: Senior-level nursing students were randomised to HF and LF instruction groups or to a control group. Primary outcome measures included LF (digital sounds on a computer) and HF (human patient simulator) auscultation tests of cardiac and respiratory sounds, as well as observer-rated performances in simulated clinical scenarios. RESULTS: On the LF auscultation test, the LF group consistently demonstrated performance comparable or superior to that of the HF group, and both were superior to the performance of the control group. For both HF outcome measures, there was no significant difference in performance between the HF and LF instruction groups. CONCLUSIONS: The results from this study suggest that highly contextualised learning environments may not be uniformly advantageous for instruction and may lead to ineffective learning by increasing extraneous cognitive load in novice learners.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".