History and physical exam: a retrospective analysis of a clinical opportunity
Bibliographic record
Abstract
BACKGROUND: All learners at the Northern Ontario School of Medicine University complete a longitudinal integrated clerkship experience in their third year, which serves to improve learner experience with community and clinical acute and chronic health needs. Early in the program, Muskoka faculty (two of the 15 LIC sites of NOSM U) became aware that learners never had the occasion to complete a full history and physical exam on a real patient with complex needs. Recognizing this as a critical experience, a program was initiated to provide learners with this opportunity. This manuscript reports on the effectiveness and impact of this novel program and outlines the procedure developed to incorporate a similar program should communities see the relevance. METHODS: Using a mixed method design, feedback was collected from learners and preceptors following the implementation of a novel learning opportunity for clinical clerks. Learners completed a full history and physical exam on volunteer complex patients, with supervision and immediate feedback. Using semi structured surveys, data was collected from each learner and preceptor to determine the program impact and optimize the program. Laurentian University research ethics board, certificate number 6021120. RESULTS: Both learners and preceptors agreed this was a valuable experience for learners, a good use of their time and contributed to essential skills including, communication, time management and appropriate data collection. The use of real patients was reported to be very appropriate by learners and faculty and often highlighted gaps in the learner's knowledge that they were then able to address. CONCLUSION: Feedback collected in this study confirms that providing medical learners the opportunity to complete a full history and physical exam with supervision and feedback was significantly beneficial from both a clinical and a skills-based aspect. Requiring learners to complete this task within the established period forced them to manage their time, focus on clinical consideration and remain on task. Enhancing learning opportunities is associated with improved outcomes and understanding in medical learners. Positive community experience is also related to learner retention, which is paramount for attracting new physicians in a time with significantly limited human health resources.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".