Use of modified SOAP notes and peer-led small-group discussion in a Medical Physiology course: addressing the hidden curriculum
Bibliographic record
Abstract
Peer leading of small-group discussion of cases; use of modified subjective, objective, assessment of physiology (SOAP) notes; and opportunities for self-assessment were introduced into a Medical Physiology course to increase students' awareness and practice of professional behaviors. These changes arose from faculty members' understanding of the hidden curriculum and their efforts to reveal it to take increased advantage of its educationally beneficial aspects. Faculty members and students observed that the requirement for students to submit SOAP notes before their discussions meant that they were well prepared to participate. Student satisfaction with the protocol was high, with >95% of the students agreeing that discussants were well prepared and that the overall performance of their discussion group was good. A comparison of students' performance on selected exam questions showed that peer leading was equally as effective as a previously used teacher-centered approach. Students agreed that their ability to analyze a clinical case had improved using this protocol, an effect that persisted at least one semester after the end of the course. These approaches were time and cost efficient from a faculty perspective while serving the needs of the students. The use of SOAP notes and peer-led discussion were effective forms of instruction, in which students succeeded in learning medical physiology and in practicing professional behaviors.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".