Bibliographic record
Abstract
Role plays are almost universally loved by educators as a great teaching tool in the medical context, and almost universally hated by students and doctors on training programs, who cite embarrassment and humiliation as key barriers to embracing this teaching technique. “I would rather walk barefoot over hot coals than participate in a role play in front of my colleagues” is not an uncommon response to the suggestion of using role plays in teaching for doctors and medical students. But the good news is that it does not have to be this way! During this fun workshop, Dr. Hilton Koppe will present a number of role play techniques borrowed from psychodrama and adapted for use within the medical context. His use of role plays in teaching has achieved virtual legendary status as a result of the high evaluations they receive from medical students and doctors in training. Workshop attendees will be invited to participate in a number of scenarios which will be used to demonstrate a range of techniques which make the use of role plays both fun, and a highly effective teaching tool. The rationale underpinning these techniques will be outlined.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".