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Enregistrement W4284895075 · doi:10.7759/cureus.26637

Application of 3D Printing in Training Health Care Providers; the Development of Diverse Facial Overlays for Simulation-Based Medical Training

2022· article· en· W4284895075 sur OpenAlexaffabout
Julia Micallef, Anusha Broekhuyse, Sanjana Vuyyuru, Randy S. Wax, Subbaramiah Sridhar, Jane Heath, Suhair Clarke, Adam Dubrowski

Notice bibliographique

RevueCureus · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensLakeridge HealthWestern UniversityMcMaster UniversityOntario Tech University
Organismes subventionnairesnon disponible
Mots-clésMedicineGeneralizability theoryEthnic groupPopulationHealth careOverlayCultural diversityMedical educationFamily medicinePsychologyComputer science

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,305
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,099
Tête enseignante GPT0,402
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2022
Routes d'admission2
Résumé présentoui

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