Maximising sensory learning through immersive education
Bibliographic record
Abstract
The use of simulated learning in nurse education is not new and much has been written regarding various approaches to using low, medium and high-fidelity approaches. Reality or fidelity is important in terms of creating quality learning using simulation; however, within the literature there is a strong focus on the use of computerised mannequins, rather than on the environment in which the simulation occurs. It is accepted that scenarios on which simulation is based should represent the reality of the clinical world, where students are enabled to learn through active participation in situation which they will likely encounter in the real world. Nurses retrieve information from patients using all of their senses; indeed nursing text books advocate the use of a multi-sensory approach to assessment. Using the senses is often highlighted as part of active learning reinforcing the need for seeing, noticing and observing as a central principle; however other senses may be just as important in terms of active learning. Educators need to determine which aspects of clinical simulation are most important for learning. For example, are motor, cognitive and sensory aspects of equal importance? This paper describes the emerging technology enabling educators to introduce a range of sensory learning stimuli, for example, the use of smell as a clinical indicator and sophisticated suits which provide the wearer with tactile feedback. We go on to consider the potential value of such mechanisms to learning through simulation. Normal 0 false false false EN-GB X-NONE X-NONE
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".