Attending Emergency Physicians’ Perceptions of a Programmatic Workplace-Based Assessment System: The McMaster Modular Assessment Program (McMAP)
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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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".