Physical and biophysical markers of assessment in medical training: A scoping review of the literature
Bibliographic record
Abstract
Purpose Assessment in medical education has changed over time to measure the evolving skills required of current medical practice. Physical and biophysical markers of assessment attempt to use technology to gain insight into medical trainees’ knowledge, skills, and attitudes. The authors conducted a scoping review to map the literature on the use of physical and biophysical markers of assessment in medical training.Materials and methods The authors searched seven databases on 1 August 2022, for publications that utilized physical or biophysical markers in the assessment of medical trainees (medical students, residents, fellows, and synonymous terms used in other countries). Physical or biophysical markers included: heart rate and heart rate variability, visual tracking and attention, pupillometry, hand motion analysis, skin conductivity, salivary cortisol, functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The authors mapped the relevant literature using Bloom’s taxonomy of knowledge, skills, and attitudes and extracted additional data including study design, study environment, and novice vs. expert differentiation from February to June 2023.Results Of 6,069 unique articles, 443 met inclusion criteria. The majority of studies assessed trainees using heart rate variability (n = 160, 36%) followed by visual attention (n = 143, 32%), hand motion analysis (n = 67, 15%), salivary cortisol (n = 67, 15%), fMRI (n = 29, 7%), skin conductivity (n = 26, 6%), fNIRs (n = 19, 4%), and pupillometry (n = 16, 4%). The majority of studies (n = 167, 38%) analyzed non-technical skills, followed by studies that analyzed technical skills (n = 155, 35%), knowledge (n = 114, 26%), and attitudinal skills (n = 61, 14%). 169 studies (38%) attempted to use physical or biophysical markers to differentiate between novice and expert.Conclusion This review provides a comprehensive description of the current use of physical and biophysical markers in medical education training, including the current technology and skills assessed. Additionally, while physical and biophysical markers have the potential to augment current assessment in medical education, there remains significant gaps in research surrounding reliability, validity, cost, practicality, and educational impact of implementing these markers of assessment.
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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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.036 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".