Attending Emergency Physicians’ Perceptions of a Programmatic Workplace-Based Assessment System: The McMaster Modular Assessment Program (McMAP)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Construct: The McMaster Modular Assessment Program (McMAP) is a programmatic workplace-based assessment (WBA) system that provides emergency medicine trainees with competency judgments through frequent task-specific and global daily assessments. Background: The longevity of McMAP relative to other programmatic WBA systems affords a unique view that precedes large-scale transitions to competency-based medical education (CBME), particularly in North America. Although prior work has described the perspective of residents using this system, the in-depth experiences of assessors using the system have yet to be explored. This perspective is important for understanding the validity of the competency judgments the system produces. Approach: We conducted a qualitative study that used semi-structured interviews analyzed using interpretive description (Thorne) to explore 16 attending physicians’ experiences using McMAP. Data analysis was completed independently by 2 researchers, who met regularly to discuss codes and resolve any disagreements. Results: Having a structured assessment framework for a range of clinical tasks has helped encourage what attendings perceived to be more frequent and better-quality assessments, with the added advantages of being holistic, flexible, and learner-driven. However, attendings also perceived a number of challenges of McMAP and programmatic WBA more broadly. These included a reluctance to give and to document negative feedback, “gaming” of the system by both attendings and residents, and a variety of logistic and technology-related concerns. Conclusions: Based on our findings, we offer several key recommendations that can help programs maximize the benefits of programmatic WBA as they transition to CBME.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it