Innovating Point-of-Care Ultrasound (POCUS) Education: A New Era of Undergraduate Medical Education at the University of Ottawa
Bibliographic record
Abstract
The University of Ottawa's Point-of-Care Ultrasound (POCUS) curriculum in Undergraduate Medical Education (UGME) serves as a strong foundation for the increasing expectations of POCUS proficiency in Postgraduate Medical Education (PGME) and clinical practice. Our program has seen notable successes, particularly in the realms of medical student engagement, educational innovation, and interdisciplinary support. These successes are evidenced through robust student participation in curriculum development and implementation, research with national and international presentations, and Interest Groups (IGs). The success of this curriculum continues to have the support from UGME and many Departments and Divisions with the University of Ottawa. As we transition to Competency-Based Medical Education (CBME) and incorporate Entrustable Professional Activities (EPAs), our curriculum is well-positioned but requires ongoing refinement. Key areas for development include formalizing clerkship objectives, diversifying assessment methods, and leveraging technology for future clinical practice. In summary, while the POCUS curriculum at the University of Ottawa marks significant strides in medical education, a focused approach toward addressing these evolving demands will further solidify its role as a cornerstone in preparing future clinicians. ---------- Le curriculum sur l’échographie au point d’intervention (POCUS) de l’Université d’Ottawa dans le cadre des études médicales de premier cycle (EMPC) sert de base solide pour répondre aux attentes croissantes en matière de maîtrise de POCUS dans la formation médicale postdoctorale et la pratique clinique. Notre programme a connu des succès notables, en particulier dans les domaines de l’engagement des étudiants en médecine, de l’innovation pédagogique et du soutien interdisciplinaire. Ces succès se traduisent par une forte participation des étudiants à l’élaboration et à la mise en œuvre du programme, par des recherches présentées à l’échelle nationale et internationale, et par la création de groupes d’intérêt. Le succès de ce programme continue de bénéficier du soutien de l’EMPC et de nombreux départements au sein de l’établissement. Ce soutien est illustré par la participation active des membres du corps professoral à l’enseignement, aux initiatives de recherche et aux investissements financiers dans l’équipement, le tout dans le cadre de POCUS. Alors que nous passons à une formation médicale basée sur les compétences et intégrons des activités professionnelles confiables, notre curriculum nécessite des améliorations continues. Les domaines clés à développer comprennent la formalisation des objectifs du stage, la diversification des méthodes d’évaluation et l’exploitation de la technologie pour la pratique clinique future. Afin d’évaluer l’efficacité de ce programme, un plan d’évaluation sera mis en œuvre. Il comprendra des commentaires réguliers des étudiants et du corps enseignant afin d’évaluer la pertinence des objectifs du POCUS, ainsi que des évaluations formatives/sommatives pour contrôler les compétences et l’acquisition des aptitudes des apprenants. Si le programme POCUS de l’Université d’Ottawa marque une avancée significative dans l’enseignement médical, une approche ciblée visant à répondre à ces exigences en constante évolution renforcera encore son rôle de pierre angulaire dans la formation des futurs cliniciens.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".