Simulation curriculum evaluation and development in a postgraduate emergency medicine programme: a 2-year logic model follow-up
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Simulation is an educational tool most valuable when implemented by trained individuals.1 Having regular simulation-based educational (SBE) activities leads to skill acquisition transferable to real-life situations.2 Emergency medicine (EM) residents at the University of British Columbia (UBC) have a variety of SBE opportunities across the four training sites (Vancouver, Fraser Valley, Victoria and Kelowna). We previously completed step two of Kern’s six-step model for curriculum development; a formal learner-targeted needs assessment.3 The assessment found a desire for increased SBE and concerns around prebrief inconsistency that may have contributed to the 19% rate of reported lack of psychological safety. This project was the second stage in an iterative curricular improvement process using a logic model.4 We chose to use a logic model as it allowed us to analyse our current programme and how it relates to the outcomes we are trying to achieve. In doing so, we were able to construct a theory of change by mapping our logical assumptions about how resource inputs into our programme result in deliverable outputs.5 The main advantage to this approach was to gain a high-level understanding of our simulation programme so that we could target specific inputs as a way to modify outputs. Our inputs into the logic model (figure 1) included simulation facilitators, technologists and labs, a recently …
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it