Application of 3D Printing in Training Health Care Providers; the Development of Diverse Facial Overlays for Simulation-Based Medical Training
Bibliographic record
Abstract
The medical simulation manikins used by healthcare learners provide the training of numerous clinical skills but often lack diversity with respect to race, ethnicity, age, and sex. Having a diverse medical education environment is imperative for exposing learners to the diverse population of patients they may encounter when in practice. In this technical report, the development of diverse and cost-effective facial overlays produced using 3D scanning, 3D printing, and silicone to be used on top of the current medical manikins at Lakeridge Health Hospital (Oshawa, Ontario, Canada) is described. To obtain consistent feedback throughout the development process, an advisory committee was consulted monthly at Lakeridge Health Hospital. The process began by determining that two facial overlays would be developed based on the two groups that represent the highest percentage of visible minorities in the Durham Region (Ontario, Canada). Facial overlays representing the South Asian (31.8%) and Black (29.6%) races were chosen. To prevent the generalizability of the facial features of these two races, volunteers who identified as specific ethnicities (East Indian and Jamaican) within each race were selected. To add variation in age for the facial overlays, the East Indian facial overlay was edited to represent an adolescent teenager (15 to 17 years old) and the Jamaican overlay was edited to represent an elderly citizen (over 60 years old). The facial overlays were developed from the 3D scans of the two volunteers and were used to create the design of 3D printed molds, in which silicone was poured in. Pigments were added to the silicone to match the skin tones of the two volunteers, and these specific tones were used as the base color for each facial overlay. Details, such as wrinkles, eyebrows, and lip color, were painted on top of the base using additional pigmented silicone. Additionally, neck overlays were created to provide continuity of the skin tone of the facial overlay. To retain the functionality of the medical manikins, the eyes of the facial overlays were cut out, and the mouth was cut open to allow for intubation training. For stability purposes, Velcro attachments were added to the facial and neck overlays so that they could be secured onto the medical manikins. Overall, the costs to manufacture both facial overlays resulted in CAD 235.65, including local taxes. Once manufactured, both facial overlays were tested by medical students (n=18) during two separate advanced cardiovascular life support (ACLS) training sessions in the local, hospital-based simulation laboratory at Lakeridge Health Hospital. The feedback obtained suggested a need to improve the functionality of the facial overlays by making the mouths bigger and less stiff for easier intubation. However, the overlays were accepted overall as a means to add diversity to the current medical manikins. In the end, cost-effective and diverse facial overlays were created to be used on top of the medical manikins that are currently being used by healthcare learners at Lakeridge Health Hospital.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".