Are Educators Actually Coaches? The Implication of Teaching and Learning via Simulation in Education in Healthcare Professions
Bibliographic record
Abstract
Simulation is a unique pedagogical tool designed specifically to develop skills, attitudes, behaviors, and knowledge using experiential learning. Though the teachers in the field of simulation are known as educators, they are generally categorized as educators or coaches and must employ unique pedagogic approaches. Though the aspects of educating and coaching are similar, there are numerous differences that set the two roles apart. Thus, the purpose of this editorial is to highlight the differences between the two roles and also to contextualize their differences, as they relate to simulation in healthcare professions, teaching, and learning. The fundamental proposition of this editorial is to highlight that the teachers who use simulation as their teaching and learning technology function as coaches and not educators as they are currently labeled. Like Haji et al. propose in their article titled "What we call what we do affects how we do it: a new nomenclature for simulation research in medical education," we propose that there needs to be a slight shift in the nomenclature of simulation.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".