patient as teacher-learnings about becoming a good physician from senior medical students
Bibliographic record
Abstract
For 15 years, in fourth-year clerkships in Family Medicine / Underserved Health Care, the author, a core clerkship faculty member, meets with 8-12 students at the beginning and end of their monthly rotation. Students reflect on and write goals in four areas, including Primary Care, Teaching, and Working with Underserved Communities. A key goal is the fourth. Students reflect on their training, especially third year, as a ‘socialization’ process where they may have learned some good habits, but also some behaviors that may have felt like survival, that are not congruent with the physician they aspired to become. In a safe and supportive learning environment, at the Student-Run Free Clinic, where time and reimbursement are not the drivers, the students grow in self-awareness as physicians, healers, and teachers. In the final session, each student also shares a meaningful story about a patient who will sit on their shoulder throughout their career and gently remind them about the physician they are becoming. Each student then identifies the essence of the patient’s teaching. Another student takes notes. The students co-create, in a facilitated and supportive environment, a set of teachings. One student reads them aloud and sends them to the group. The specific teachings, in the students’ own words, are about listening, to being present, to thoroughness, to asking open-ended questions, to exploring the social determinants of health, to learning from our errors, to taking the extra minute, and many others. More than 1200 students have participated in this activity, with consistent positive feedback.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".