Are we optimizing medical students’ preparation for clerkship? A content analysis of narrative comments on clinical skills during preclinical training
Bibliographic record
Abstract
Introduction: The progression from preclinical medical training to clerkship is a pivotal yet steep transition for medical students. Effective feedback on clinical skills during preclinical training can better equip students for clerkship and allows time for them to address difficulties promptly. The goal of this study was to explore whether and how narrative comments at this stage were being leveraged to achieve this transition. Methods: We conducted a content analysis to categorize narrative comments on the clinical skills of two cohorts of third-year preclinical students at one academic institution. Results: Teachers made narrative comments for 272 students. Each comment was divided into analysis units (n = 1,314 units). Comments were either general (n = 187) or focused on attitude (n = 628), knowledge and cognitive processes (n = 357), or clinical reasoning (n = 142). They were abundantly positive (n = 1,190) and marginally negative (n = 39). Few (6%) contained suggestions for improvement. Discussion: In this study, narrative comments on clinical skills before clerkship seemed minimally helpful, as they were overwhelmingly positive and seldom offered suggestions. This could suggest missed opportunities for early interventions. Pre-clerkship narrative comments could potentially be optimized by increasing emphasis on clinical reasoning, addressing challenges early and providing actionable steps for improvement.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".