Thinking About the Why: A Qualitative Study on Students' Perspectives of Paediatric Team‐Based Learning Discussions
Bibliographic record
Abstract
INTRODUCTION: Clinical reasoning skills are essential to medical practice. Team-based learning (TBL) using key-feature questions provides students an opportunity to explicitly practice clinical reasoning skills with peers. Understanding student experiences with this learning strategy may provide insights into optimizing learning experiences that foster clinical reasoning in preparation for the care of future patients. METHODS: A descriptive qualitative design explored third-year medical students' experiences of a clinical reasoning-focused virtual TBL session. Six focus groups involving 26 third-year medical students were conducted; audio recordings were transcribed verbatim. Themes were generated from analysed codes, drawing connections between common thoughts, processes and conditions experienced by participants. Data were verified, upholding principles of qualitative rigour to assure methodological credibility, transferability, confirmability and reflexivity. RESULTS: Data analysis revealed five themes: (a) self-confidence, including enabling and deterring factors and peer calibration; (b) co-learning with peers, with students identifying knowledge gaps and gaining experience establishing consensus; (c) trust, as related to sense and position of authority; (d) clinical reasoning strategies articulated by students and integrating peer feedback; and (e) clinical application, as a parallel to real-life patients involving choosing wisely and commitment to decisions. CONCLUSIONS: Students' perspectives on the clinical reasoning process were greatly impacted by clinical experience, including lack thereof. Their peer-to-peer experience highlights the power of social learning and trusting relationships on students' professional identity formation. How students translate learning from the classroom to authentic clinical encounters requires further study.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".