Screen-Based Virtual Simulation in Medical Laboratory Science Education: Findings from a National Program
Bibliographic record
Abstract
ABSTRACT Curricular limitations on clinical placements in medical laboratory technology (MLT) education have increased interest in health care simulation, particularly virtual simulation (VS). This study explored learner experience, perceived learning, and readiness for clinical application among MLT students through an end-of-course survey in a work-integrated learning program across 8 Canadian institutions (N = 145, 2023-2024). Using a mixed-methods design, measures included satisfaction, psychological safety, inclusivity, skill development, engagement, usability, and debriefing quality, supplemented by open-ended comments and interviews. Students reported high satisfaction (87.9%), strong psychological safety ( x¯ = 4.55; SD = 0.75), and inclusivity ( x¯ = 4.38; SD = 0.85). High engagement and usability scores indicated effective functionality and positive debriefing experiences. Perceived learning gains were greatest for critical thinking and problem solving, with smaller improvements in communication and teamwork; cross-program differences were minimal and nonsignificant. Qualitative data highlighted VS as a valuable, low-risk environment enabling repetition, feedback, and knowledge application. Students with repeated VS exposure reported deeper learning and increased confidence. Overall, VS was associated with favorable learner experiences, enhanced perceived learning, and greater self-reported readiness for clinical practice. Findings support its use as an effective adjunct and potential partial substitute for traditional clinical placements in MLT education.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".