Defining Principles of Expert Performance During Medical Procedures: Optimizing Assessment Criteria of Procedural Skills
Bibliographic record
Abstract
Introduction: For pediatric residency programs, the Royal College of Physicians and Surgeons of Canada has outlined certain Entrustable Professional Activities that center around procedural skills that residents are assessed on for competency. Currently, procedural skill training and assessment focuses on the technical aspects of procedural performance such as psychomotor skills and knowledge, hindering our understanding of procedural expertise. Madani et al., (2017), have developed a universal framework that describes the core skill domains important for expert performance in the operating room; however, the framework may not be transferable to pediatrics and procedures performed outside of the operating room. This thesis aims to understand the core principles that guide expert performance during medical procedures. Methods: In this study, we took a qualitative description approach and used Braun and Clarke’s reflexive thematic analysis. We conducted semi-structured interviews with faculty from procedure-heavy specialties across Canada, and inquired about general steps in procedures, skills needed for procedural expertise, and procedural training programs. Results: There were 18 participants in this study from six institutions across Canada. We identified five themes from our data: (1) Procedural Expertise Requires Skills that Go Beyond Psychomotor Skills, and Relies Heavily on Non-Technical Skills, (2) The Generalization of Procedural Expertise is Dependent on the Skills, Specialty, and Contextual and Patient Factors, (3) Approaching Expected/Unexpected Events in Procedures and Deciding on Adaptations is a Crucial Part of Expertise, (4) Pediatric Training Programs Use a Variety of Approaches in Procedural Training, and (5) Decrease in Procedural Opportunities for Experts Lead to Skill Decay. Conclusions: In conclusion, the findings of this thesis suggest that the core principles of procedural expertise are similar to the core principles of expert surgeons apart from communication with caregivers. The findings of this thesis can be used in developing holistic assessment plans for procedural performance and modifying Madani et al.’s framework for expertise in pediatric procedures.
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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 | 0.065 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".