Shifting to Delivering Simulation Virtually Within a Healthcare Education Setting
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has changed how healthcare education is being delivered, creating a global shift towards virtual modalities. Various approaches, each with their own benefits and limitations, have been developed to bridge this gap and continue to provide comprehensive education to healthcare students. To understand which approach to implement, we must consider what each can offer and what is best suited for the situation. Much of this will focus on the learning goals and outcomes with research strongly favouring modalities focused on the areas of cognitive, affective, and behavioural skill acquisition as opposed to technical skills. The use of simulated persons offers the most diverse application for these areas of focus. This approach can provide opportunities for both synchronous and asynchronous learning. While novel in its approach, virtual simulations can leverage existing success and performance indicators used for in-person approaches to best understand the experiences of the learners and the facilitators. Evidence can be compared with outcomes of previous in-person groups to understand how this approach can be best implemented into curricula/programs. Future applications are numerous for this modality and the development of pilot studies focused on smaller groups of learners will provide opportunities for educators and program developers to review and understand the challenges that may arise. Simulation is a widely drawn upon teaching-learning approach deeply rooted in experiential learning. With the purpose of replicating real-world scenarios to increase knowledge transfer and reduce the shock of encountering challenging real-world situations, simulated environments are proven to be an effective tool in building learners' self-confidence and bridging the gap between theory and practice within the healthcare realm. Simulation can be, and is, applied within healthcare-education contexts from undergraduate to graduate levels and into ongoing professional development.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".