Do Resident Archetypes Influence the Functioning of Programs of Assessment?
Bibliographic record
Abstract
While most case studies consider how programs of assessment may influence residents’ achievement, we engaged in a qualitative, multiple case study to model how resident engagement and performance can reciprocally influence the program of assessment. We conducted virtual focus groups with program leaders from four residency training programs from different disciplines (internal medicine, emergency medicine, neurology, and rheumatology) and institutions. We facilitated discussion with live screen-sharing to (1) improve upon a previously-derived model of programmatic assessment and (2) explore how different resident archetypes (sample profiles) may influence their program of assessment. Participants agreed that differences in resident engagement and performance can influence their programs of assessment in some (mal)adaptive ways. For residents who are disengaged and weakly performing (of which there are a few), significantly more time is spent to make sense of problematic evidence, arrive at a decision, and generate recommendations. Whereas for residents who are engaged and performing strongly (the vast majority), significantly less effort is thought to be spent on discussion and formalized recommendations. These findings motivate us to fulfill the potential of programmatic assessment by more intentionally and strategically challenging those who are engaged and strongly performing, and by anticipating ways that weakly performing residents may strain existing processes.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".