Mapping Cognitive Overlaps Between Practice-Based Learning and Improvement and Evidence-Based Medicine: An Operational Definition for Assessing Resident Physician Competence
Bibliographic record
Abstract
PURPOSE: The complex competency labeled practice-based learning and improvement (PBLI) by the Accreditation Council for Graduate Medical Education (ACGME) incorporates core knowledge in evidence-based medicine (EBM). The purpose of this study was to operationally define a "PBLI-EBM" domain for assessing resident physician competence. METHOD: The authors used an iterative design process to first content analyze and map correspondences between ACGME and EBM literature sources. The project team, including content and measurement experts and residents/fellows, parsed, classified, and hierarchically organized embedded learning outcomes using a literature-supported cognitive taxonomy. A pool of 141 items was produced from the domain and assessment specifications. The PBLI-EBM domain and resulting items were content validated through formal reviews by a national panel of experts. RESULTS: The final domain represents overlapping PBLI and EBM cognitive dimensions measurable through written, multiple-choice assessments. It is organized as 4 subdomains of clinical action: Therapy, Prognosis, Diagnosis, and Harm. Four broad cognitive skill branches (Ask, Acquire, Appraise, and Apply) are subsumed under each subdomain. Each skill branch is defined by enabling skills that specify the cognitive processes, content, and conditions pertinent to demonstrable competence. Most items passed content validity screening criteria and were prepared for test form assembly and administration. CONCLUSIONS: The operational definition of PBLI-EBM competence is based on a rigorously developed and validated domain and item pool, and substantially expands conventional understandings of EBM. The domain, assessment specifications, and procedures outlined may be used to design written assessments to tap important cognitive dimensions of the overall PBLI competency, as given by ACGME. For more comprehensive coverage of the PBLI competency, such instruments need to be complemented with performance assessments.
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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 | 0.022 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".