A scoping review of literature about 3D printing: knowledge, skills and attitude for simulation educators in healthcare
Bibliographic record
Abstract
BACKGROUND: Three-Dimensional (3D) printing, also known as additive manufacturing (Linke, Additive manufacturing, explained, 2017), has rapidly emerged as a transformative tool in healthcare simulation. This scoping review investigates simulation educators' knowledge, skills, and attitudes (KSAs) about the impact of 3D printing and explores 3D printing's broader applications in healthcare simulation. By synthesizing existing literature, this study aims to identify trends, challenges, and opportunities for integrating 3D printing into simulation-based education. MAIN BODY: The review followed the PRISMA-ScR framework, employing a six-step approach. A comprehensive search was conducted across databases, including PubMed, Medline, ERIC, CINAHL, and Google Scholar, covering studies published between 2000 and 2023. Keywords related to 3D printing and simulation-based education were used. Inclusion criteria focused on peer-reviewed articles discussing 3D printing's role in KSAs for simulation educators and its applications in healthcare simulation. Articles were charted and analyzed thematically to identify trends, challenges, and outcomes. A total of 181 studies were included, spanning 36 countries and 113 journals. Most studies focused on medical education, with 73% utilizing 3D-printed models for direct teaching. Key themes identified included realism, skill development, cost-effectiveness, and teaching effectiveness. Challenges included model accuracy, training gaps for educators, and resource limitations. Study designs were predominantly descriptive, with a significant portion being single-site case reports. CONCLUSION: 3D printing has the potential to revolutionize simulation-based education by enhancing realism, accessibility, and skill development. However, gaps in educator training and methodological rigor must be addressed. Future research should focus on multi-institutional studies and long-term outcomes to maximize the impact of the technology.
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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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".