Using simulation stethoscopes to support physical exam skill development in health professionals education: A scoping review of educational applications and outcomes
Bibliographic record
Abstract
BACKGROUND: Developing physical assessment skills relies on practicing with healthy peer learners and standardized patients or simulating pathologic sounds with mannequins. Simulation stethoscopes bridges both by offering learners an opportunity to communicate with a live patient and hear pathologic sounds. OBJECTIVES: This scoping review aims to map the use of simulation stethoscopes in health professions, describe how they are integrated into training, and identify further research opportunities. METHODS: We conducted a scoping review following the Arksey and O'Malley framework with advancements by Levac and colleagues. We searched EMBASE, MEDLINE, Google Scholar, and bibliographies of included studies. Eligible studies involved simulation stethoscopes used in health professions education. Two reviewers independently screened articles, extracted data, and mapped outcomes. Study characteristics, populations, learning objectives, disease states, and simulation design were analyzed descriptively. RESULTS: Six studies were included. These studies were published between 2011 and 2023 and conducted in the USA (n = 5) and UK (n = 1). Study populations included pharmacy students, paramedics, medical students, physician residents, and physiotherapists. Simulation stethoscopes were used during respiratory, cardiovascular, and acute paramedic scenarios on standardized patients, peer learners, and mannequins. Mapped outcomes included physical exam knowledge, technique, interpretation of findings, confidence, fidelity, and simulation evaluation. IMPLICATIONS: Studies demonstrated the flexible use of simulation stethoscopes among healthcare professions by providing high realism with a focus on skills development and learner confidence. Further research on the role of simulation stethoscopes in teaching physical assessment, including their benefits and implications for student learning and confidence, especially among pharmacy professionals, would be beneficial.
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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 | 0.029 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".