Impact of teaching, learning, and assessment of medical law on cognitive, affective and psychomotor skills of medical students: a systematic review
Bibliographic record
Abstract
BACKGROUND: It is necessary to improve medical students' legal cognitive, affective, and psychomotor skills to prevent further legal issues in the medical profession. Choosing the proper teaching and assessment methods is crucial in this matter. This study aimed to investigate the impact of teaching, learning, and assessment of medical law on the cognitive, affective, and psychomotor skills of medical students. METHODS: A systematic review was conducted in PubMed, Embass, and Web of Science databases, and Google Scholar search engine using MECIR and PRISMA, AMEE Guide 94 for 1980 to 2022.12.30. Nineteen articles met the inclusion criteria. MERSQI checklist was used to assess the quality of the articles, and URSEC (theoretical underpinning of the development, resources required, setting, educational methods employed, and content) used to assess the risk of educational bias. RESULTS: Internship courses called Medical Education Humanities and Society (MESH), clinical scenario design, seminars and small group discussions, web-based interactive training, legal training courses, PBL, and mind maps have been used to improve the medico-legal knowledge of medical students. MESH clerkship, simulation of a legal event, medico-legal advocacy program based on interdisciplinary education, group discussion, integration, and court-based learning used to improve student attitudes. Multidisciplinary training, small group discussions after the seminar, mock trial competition, and interdisciplinary education are used to teach psychomotor skills. All studies, except one on knowledge, reported positive effects of legal education on students' knowledge, attitudes, and legal performance. Written assessments were used for cognitive and affective domains, while performance was assessed by OSCE, simulated court, and evaluation of patient referrals. CONCLUSION: There are few studies to examine the cognitive, affective, and legal psychomotor skills of medical students. The texts have not yet fully explored the high level of affective and psychomotor domains, which is evidence of a gap in this sector. Recognizing that medico-legal problems are prevented through proper education and assessment, it is recommended that this area be considered a research priority and that effective educational policies are adopted.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".