McMaster Modular Assessment Program (McMAP) Through the Years: Residents' Experience With an Evolving Feedback Culture Over a 3‐year Period
Bibliographic record
Abstract
BACKGROUND: Assessing resident competency in emergency department settings requires observing a substantial number of work-based skills and tasks. The McMaster Modular Assessment Program (McMAP) is a novel, workplace-based assessment (WBA) system that uses task-specific and global low-stakes assessments of resident performance. We describe the evaluation of a WBA program 3 years after implementation. METHODS: = 26) who used McMAP as part of McMaster University's emergency medicine residency program. Responses were triangulated using a follow-up written survey. Data were analyzed using theory-based thematic analysis. An audit trail was reviewed to ensure that all themes were captured. RESULTS: Findings were organized at the level of the learner (residents), faculty, and system. Residents identified elements of McMAP that were perceived as supporting or inhibiting learning. Residents shared their opinions on the feasibility of completing daily WBAs, perceptions and utilization of rating scales, and the value of structured feedback (written and verbal) from faculty. Residents also commented extensively on the evolving and improving feedback culture that has been created within our system. CONCLUSION: The study describes an evolving culture of feedback that promotes the process of informed self-assessment. A programmatic approach to WBAs can foster opportunities for feedback although barriers must still be overcome to fully realize the potential of a continuous WBA system. A professional culture change is required to implement and encourage the routine use of WBAs. Barriers, such as familiarity with assessment system logistics, faculty member discomfort with providing feedback, and empowering residents to ask faculty for direct observations and assessments must be addressed to realize the potential of a programmatic WBA system. Findings may inform future research in identifying key components of successful implementation of a programmatic workplace-based assessment system.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".