Using SOAP notes to clean up small‐group discussion in a Medical Physiology course
Bibliographic record
Abstract
Two pedagogical problems were addressed in the Medical Physiology course at St. George’s University: provision of small group case‐based discussion by a small faculty for classes larger than 350 students, and poor preparation by students for the discussions. Both problems were successfully solved by incorporation of a modified SOAP notes in the discussion protocol, and orientation of students to student‐led discussion techniques. SOAP (Subjective & Objective Assessment of Physiology) notes, adapted from their use as a patient care tool, is a structured form on which students identify the subjective and objective findings in a paper clinical case and explain the physiological basis of each finding. Students submit their SOAP notes using a course management system before the case discussions begin. Submissions are automatically recorded and a random selection from those of each student is made for grading. Students receive a one‐hour orientation to the SOAP notes, and to student‐led discussion techniques and performance expectations. These strategies have improved student preparation so much that discussions can be facilitated by students rather than faculty tutors. Student satisfaction with the protocol is high, with >95% agreeing that discussants were well prepared and that the overall performance of their discussion group was good.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".