Assessing the Transition From Pre-Clerkship to Clerkship in a Four-Year Medical Program: Recommendations for Future Success
Bibliographic record
Abstract
Abstract Background: The transition from pre-clerkship to clinical clerkship is a pivotal moment for medical students. At the University of Ottawa Faculty of Medicine, Unit IV and the Link Block are designed to facilitate this transition. Improvements to the current curriculum, specifically in Unit IV, may better prepare students for clerkship. We aimed to summarize existing literary evidence on the transition to clerkship and collect student feedback to generate recommendations for success with regard to clerkship preparedness at the University of Ottawa Faculty of Medicine. Methods: We conducted a literature search using PubMed, MEDLINE, ERIC, and CINAHL for studies evaluating the transition to clerkship in a four-year medical program. Using this data, we created two different versions of our survey (pre-transition and post-transition) for dissemination to second, third, and fourth-year medical students, plus MD/PhD students, in the Anglophone and Francophone stream. The survey was open for three weeks from October 10 to October 31, 2020, with weekly reminders to all eligible participants. Microsoft Excel 2016 was used for data analysis. Results: We obtained 176 respondents, of which 158 (70% Anglophone and 30% Francophone) were included in the analysis. The majority of students were in the MD2023 cohort (40%) and had completed a 4-year Bachelor’s degree (61%) prior to medical school. Students in the post-transition group were less anxious about the transition to clerkship than their junior colleagues, although differences between streams were marginal. The most notable difference concerning Entrustable Professional Activities was in terms of obtaining a complete history and performing a physical examination, with the post-transition cohort reporting increased competency compared to the pre-transition cohort in the Anglophone stream (2.9/5.0 pre-transition vs. 3.3/5.0 post-transition, +0.33 difference, p<0.05). Top two stressors for incoming clerks were a lack of clinical skills or experience, and lack of clarity around clerkship roles, responsibilities, and expectations.Conclusion: There is limited training to facilitate a seamless transition for incoming clerks at the University of Ottawa Faculty of Medicine. Changes can be made at the pre-clerkship level in the form of small-group orientation sessions, formative OSCEs, accelerated review of pre-clerkship material, and clerkship simulation sessions.
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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 | 0.143 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".