Applying simulation learning theory to identify instructional strategies for <scp>Generation Z</scp> emergency medicine residency education
Bibliographic record
Abstract
Introduction: Generation Z learners are entering emergency medicine (EM) residency training, bringing unique learning preferences that influence their engagement with residency education. To optimally teach and motivate this incoming generation of learners, EM educators must understand and adapt to the changing instructional landscape. Methodology: The Simulation Leaders Advancing the Next Generation in Emergency Medicine (SLANG-EM) Workgroup was created to identify effective educational strategies for Generation Z learners entering EM. Members were faculty in the Society for Academic Emergency Medicine (SAEM) Simulation Academy, well versed in learning theory supporting simulation-based education (SBE) and actively involved in EM residency education. Unique treatment/analysis: Through primary and secondary literature searches, the SLANG-EM Workgroup identified four distinctive learning preferences of Generation Z learners: (1) individualized and self-paced learning, (2) engaging and visual learning environments, (3) immediate and actionable feedback, and (4) combined personal and academic support. Workgroup members evaluated these learning preferences using a novel conceptual framework informed by the theoretical principles underpinning SBE, recommending instructional strategies for Generation Z EM residency learners across multiple educational environments. Implications for educators: Instructional strategies were described for the didactic, simulation, and clinical learning environments. In the didactic environment, identified instructional strategies included meaningful asynchronous education, interactive small-group learning, and improved multimedia design. In the simulation environment, educational innovations particularly suitable for Generation Z learners included learner-centered debriefing, rapid-cycle deliberate practice, and virtual simulation. In the clinical environment, described instructional strategies involved setting learner-centered goals and delivering facilitative feedback in the context of an educational alliance. Overall, these instructional strategies were clustered around themes of student-centered education and the educator as facilitator, which align well with Generation Z learning preferences. These findings were synthesized and presented as an advanced workshop, "Delivering Effective Education to the Next Generation," at the 2023 SAEM Annual Meeting.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".