Developing a Simulated-Person Methodology Workshop: An Experiential Education Initiative for Educators and Simulators
Bibliographic record
Abstract
Numerous forecasts suggest that professional-competence development depends on human encounters. Interaction between organizations, tasks, and individual providers influence human behaviour, affect organizations’ or systems’ performance, and are a key component of professional-competence development. Further, insufficient or ineffective communication between professionals is deemed a contributing factor to adverse events worldwide. This underscores the need to provide educators with the tools and education to embed methods in their teaching that will enable learners to effectively intervene in highly charged interpersonal situations and high-risk scenarios; these concerns highlight the value of realistic simulated-experiential approaches, such as the one proposed in this project. The first phase of this project involved conducting a three-day experiential workshop developed at a Canadian university to provide educators with knowledge and skills to work and effectively utilize simulators, enhancing pedagogical classroom practices for teaching undergraduate learners. This workshop’s development resulted in numerous benefits. Participation in the workshop provided educators with opportunities for meaningful reflection on their teaching practice and the ability to apply this insight to optimize student learning. It provided theatre students, recruited as simulators as part of this interdisciplinary initiative, to expand their experiences and this will lead to an expanded practicum course for their program. There is now a group of simulators available to educators across the university to include in classroom activities, and lastly there are further iterations of this workshop available for faculty development. This paper reflects on the workshop experiences and the feedback obtained from the participants. Formal and informal feedback obtained provides an understanding of the participants’ experiences.
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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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".