Physical examination in undergraduate medical education in the field of general practice – a scoping review
Bibliographic record
Abstract
BACKGROUND: Physical examination (PE) is an essential clinical skill and a central part of a physician's daily activity. Teaching of PE has been integrated into medical school by many clinical disciplines with respective specific examination procedures. For instance, PE teaching in general practice may include a full-body examination approach. Studies show that PE-skills of medical students often need enhancement. The aim of this article was to scope the literature regarding the teaching and research of PE within general practice during undergraduate medical education. We evaluated a wide breadth of literature relating to the content, study design, country of research institution and year of publication. METHODS: Literature search in Medline along the PRISMA-P protocol was performed by search syntax ("physical examination" AND "medical education" AND "undergraduate" AND general practice) considering Medline MeSH (Medical Subject Heading)-Terms and Medline search term tree structure. Independent title, abstract and full-text screening with defined inclusion and exclusion criteria was performed. Full texts were analyzed by publication year, country of origin, study design and content (by categorizing articles along their main topic according to qualitative content analysis of Mayring). RESULTS: One-hundred seven articles were included. The annual number of publications ranged from 4 to 14 and had a slightly rising trend since 2000. Nearly half of the publications originated from the United States (n = 54), 33 from Canada and the United Kingdom. Overall, intervention studies represented the largest group (n = 60, including uncontrolled and controlled studies, randomized and non-randomized), followed by cross-sectional studies (n = 29). The 117 studies could be assigned to five categories "teaching methods (n = 53)", "teaching quality (n = 33)", "performance evaluation and examination formats (n=19)", "students' views (n = 8)" and "patients' and standardized patients' views (n=4)". CONCLUSIONS: The present work shows a wide spectrum of teaching and research activities and a certain level of evidence for the effectiveness of individual teaching methods. It can be used as orientation and impulse generator for the further development of medical education in the field of PE.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".