Adapting the Gamified Educational Networking Online Learning Management System to Test a Decentralized Simulation-Based Education Model to Instruct Paramedics-in-Training on the Emergency Intraosseous Access and Infusion Skill
Bibliographic record
Abstract
Intraosseous (IO) access and infusion is a safe and rapid alternative to intravenous access in obtaining vascular access for administering fluids and drugs. Healthcare professionals, such as primary and advanced care paramedics, use IO access and infusion in emergency circumstances where peripheral intravenous routes are inaccessible. IO access skills require hands-on training, which can be done remotely if the participants have access to simulation, instructions, guidance, and feedback. For the purpose of moving the training outside of the simulation laboratories, we have developed (1) an inexpensive and scalable three-dimensional (3D) printed and silicone-based advanced adult proximal tibial IO access and infusion simulator and (2) a unique learning management system (LMS) for remote simulation-based training. The LMS was built using the Django platform and supports experiential learning by providing access to educational and instructional content (including virtual simulation and serious games), allowing peers to communicate among themselves and with subject-matter experts, provide and receive feedback asynchronously, and engage in learning using gamification elements. The aim of this technical report is to describe the process of development and the final product of the LMS as a research and educational tool to scaffold remote learning of emergency IO skills by paramedics-in-training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".