Mapping the Expert Mind: Integration Method for Revising the ACES Medical Simulation Curriculum
Bibliographic record
Abstract
PURPOSE: This article shares our experience developing an integrated curriculum for the ACES (Acute Critical Event Simulation) program. The purpose of the ACES program is to ensure that health care providers develop proficiency in the early management of critically ill patients. The program includes multiple different types of educational interventions (mostly simulation-based) and targets both specialty and family physicians practicing in tertiary and community hospitals. METHODS: To facilitate integration between different educational interventions, we developed a knowledge repository consisting of cognitive sequence maps that make explicit the flow of cognitive activities carried out by experts facing different situations - the sequence maps then serving as the foundation upon which multimodal simulation scenarios would be built. To encourage participation of experts, we produced this repository as a peer-reviewed ebook. Five national organizations collaborated with the Royal College of Physicians and Surgeons of Canada to identify and recruit expert authors and reviewers. Foundational chapters, centered on goals/interventions, were first developed to comprehensively address most tasks conducted in the early management of a critically ill patient. Tasks from the foundational chapters were then used to complete the curriculum with situations. The curriculum development consisted of two-phases each followed by a peer-review process. In the first phase, focus groups using web-conferencing were conducted to map clinical practice approaches and in the second, authors completed the body of the chapter (e.g., introduction, definition, concepts, etc.) then provided a more detailed description of each task linked to supporting evidence. RESULTS: Sixty-seven authors and thirty-five peer reviewers from various backgrounds (physicians, pharmacists, nurses, respiratory therapists) were recruited. On average, there were 32 tasks and 15 situations per chapter. The average number of focus group meetings needed to develop a map (one map per chapter) was 6.7 (SD ± 3.6). We found that the method greatly facilitated integration between different chapters especially for situations which are not limited to a single goal or intervention. For example, almost half of the tasks of the Hypercapnic Ventilatory Failure chapter map were borrowed from other maps with some modifications, which significantly reduced the authors' workload and enhanced content integration. This chapter was also linked to 6 other chapters. CONCLUSIONS: To facilitate curriculum integration, we have developed a knowledge repository consisting of cognitive maps which organize time-sensitive tasks in the proper sequence; the repository serving as the foundation upon which other educational interventions are then built. While this methodology is demanding, authors welcomed the challenge given the scholarly value of their work, thus creating an interprofessional network of educators across Canada.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".