Designing the simulation learning environment: An active engagement model
Bibliographic record
Abstract
Simulation is rapidly becoming a significantly learning methodology in healthcare education. The unique characteristics of simulation learning create a bridging experience between the classroom and actual patient care and, more importantly, helps learners develop advanced clinical reasoning skills. Learner active engagement is a critical requirement for effective learning during simulation and debriefing, which tasks educators to design simulation learning environments that foster learner active engagement. To foster learner active engagement, the educator and the learner must develop a dyadic relationship of trust, openness, sharing, and safety. The formation of this dyadic relationship implies that the learner has engaged in the learning environment. The simulation literature lacks significant discussion of how the elements of the simulation learning environment can be used to create a learning environment that encourages active engagement in the learning process. From the information gathered through a literature search in CINHAHL, PubMed, and Psychology and Behavioral Sciences databases, this article describes the critical elements of effective simulation learning. The purposes of this article are to elucidate how the interaction of important elements of the simulation experience can foster active engagement and to introduce an Active Engagement Model as a framework for designing the simulation learning environment that encourages and supports learner engagement. The components of the model are the educator, the learner, the environment, which must interact effectively to form the functional entity of the model–-the educator/learner dyad. Once the educator/learner dyad is formed, all the elements of the model function in concert to form an effective simulation learning environment.
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.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".