Transformation and articulation of clinical data to understand students’ clinical reasoning: a scoping review
Bibliographic record
Abstract
Abstract Background Despite the importance of effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning, unanswered questions remain. Understanding how these cognitive operations can be observed and assessed is crucial, particularly considering the rapid growth of artificial intelligence and its integration into health education. A scoping review was conducted to map the literature regarding educational strategies to support transformation and articulation of clinical data, the learning tasks expected of students when exposed to these strategies and methods used to assess individuals’ proficiency Methods Based on the Joanna Briggs Institute methodology, the authors searched 5 databases (CINAHL, MEDLINE, EMBASE, PsycINFO and Web of Science), ProQuest Dissertations & Theses electronic database and Google Scholar. The data were synthesized narratively using descriptive statistics. Results A total of 38 articles were included in the final synthesis. Most studies were conducted in North America and Europe (n = 30, 79%) focused primarily on medical students (n = 35, 92%) and mainly used observational (n = 17, 45%) or methodological (n = 8, 21%) designs. Various educational strategies were identified, the most common were resolution of written or computerized case-based scenarios (n = 13; 52%) and simulated or real patient encounters (n = 6; 24%). The learning tasks comprised, among others, identifying key findings, translating clinical information, synthesizing cases aloud, and writing a summary statement. Furthermore, the review included assessment methods and rubrics with assessment criteria for clinical data transformation and articulation. The narrative synthesis shows positive results when integrating various educational strategies within clinical reasoning curricula compared to a single strategy used episodically. Limitations and conclusions The varying objectives, diversity of educational strategies documented, and heterogeneity of the evaluation tools or rubrics limit our conclusions. However, insights gained will help educators develop effective approaches for teaching clinical reasoning. Additional research is needed to evaluate the impacts of educational strategies aimed at developing skills for the transformation and articulation of clinical data. Clinical trial number Not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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 teacher head, 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".