Assessing Self-Reported Readiness of Medical Students transitioning to Clinical Clerkship at the University of Ottawa
Bibliographic record
Abstract
Introduction: The transition from pre-clerkship to clinical clerkship is a pivotal moment for medical students. Curricular improvements can be made to better prepare students for clerkship. We collected student feedback to generate recommendations for improvement with regard to clerkship preparedness at the University of Ottawa Faculty of Medicine. Methods: We created a pre- and post-clerkship transition survey for medical students at the University of Ottawa Faculty of Medicine. The groups assessed were from different cohorts. Likert-type and open ended questions were used. The survey was open from October 10 to October 31, 2020. Microsoft Excel 2016 was used for data analysis. Results: We obtained 176 respondents (37% response rate), of which 158 provided consent and completed the survey. Students in the post-transition group were less anxious about the transition to clerkship, compared to their pre-transition colleagues, with the most significant difference being completing a thorough history and physical examination (2.9/5.0 vs. 3.3/5.0, p<0.05). The two main stressors for incoming clerks were inadequate clinical skills training in pre-clerkship and lack of clarity around clerkship roles, responsibilities, and expectations. Conclusion: Improvements can be made in pre-clerkship through the integration of small-group orientation sessions, formative OSCEs, accelerated review of pre-clerkship material, and clerkship simulation sessions to facilitate a seamless transition to clerkship at the University of Ottawa.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".