Implementing a cost effective and configurable hybrid simulation platform in healthcare education, using wearable and web-based technologies
Bibliographic record
Abstract
There are many examples of hybrid simulation models in healthcare education which are designed to simulate specific scenarios. However, there appears to be a need for a cost effective and configurable hybrid simulation platform which can be used by educators of various healthcare disciplines to simulate different scenarios. The purpose of this paper is to develop a proof-of-concept platform that can be easily implemented at little cost and provide flexibility to healthcare instructors to develop a variety of simulation scenarios, and to determine the effectiveness of this platform. Using a standardized patient, a person acting as a patient in a scripted manner, along with wearable and web-based technologies, a congestive heart failure simulation was used as an evaluative exercise for a group of personal support worker students at a Canadian Community College. Personal support workers typically provide care to any person who may require personal assistance with activities of daily living such as feeding, lifting, bathing, skin care and oral hygiene to name a few. Standardized patients are typically used in healthcare education to educate and evaluate soft skills, such as caregiver to patient communication, professionalism, as well as hard skills, such as history taking, examination and diagnostic skills (Rosen in J Crit Care 23:157-166, 2008). Instructor feedback indicated that the platform was easy to use and capable of simulating a large variety of scenarios. Pre and post test results are evidence of initial findings of promise indicating that the platform seemed to be effective in enabling students to meet learning outcomes. Focus group results seem to indicate an increase in student confidence as it relates to their ability to handle a similar scenario in the workplace.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".