Software-Based Simulation on a 3D Environment for Vaccination Teaching and Learning: Design Science Research
Bibliographic record
Abstract
BACKGROUND: Student training requires specific laboratories for vaccination practice, which are usually limited, and even professionals' continuing education regularly lacks proper care. Thus, new methodologies, concepts, and technologies, such as software-based simulations, are in highly demand. OBJECTIVE: This work aims to develop a 3D virtual environment to support teaching activities in the vaccination room. The software-based simulation must contribute positively to teaching considering a variable set of scenarios. METHODS: We applied the design science research method to guide the work. First, the concepts and opportunities were raised, which we used to build the simulation (ie, the proposed technological artifact). The development was assisted by a specialist, in which we sought to create a vaccination room according to Brazilian standards. The artifact evaluation was achieved in 2 stages: (1) an evaluation to validate the design with experts through the Delphi method; and (2) a field evaluation with nursing students to validate aspects of usability (System Usability Scale [SUS]) and technology acceptance and use (Unified Theory of Acceptance and Use of Technology version 2). RESULTS: We built the simulation software using the Unity game engine. An additional module was also developed to create simulation scenarios and view the students' performance reports. The design evaluation showed that the proposed solution is adequate. Students' evaluations confirm good usability (SUS score of 81.4), besides highlighting Performance Expectation as the most positively influential factor of Behavioral Intention. Effort Expectancy is positively affected by younger users. Both evaluation audiences cited the high relevance of the proposed artifact for teaching. Points for improvement are also reported. CONCLUSIONS: The research accomplished its goal of creating a software-based simulation to support teaching scenarios in the vaccination room. The evaluations still reveal desirable improvements and user behavior toward this kind of technological artifact.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".