Operationalizing Programmatic Assessment: The CBME Programmatic Assessment Practice Guidelines
Bibliographic record
Abstract
PROBLEM: Assessing the development and achievement of competence requires multiple formative and summative assessment strategies and the coordinated efforts of trainees and faculty (who often serve in multiple roles, such as academic advisors, program directors, and competency committee members). Operationalizing programmatic assessment (PA) in competency-based medical education (CBME) requires comprehensive practice guidelines, written in accessible language with descriptions of stakeholder activities, to move assessment theory into practice and to help guide the trainees and faculty who enact PA. APPROACH: Informed by the Appraisal of Guidelines for Research and Evaluation II (AGREE II) framework, the authors used a multiphase, multimethod approach to develop the CBME Programmatic Assessment Practice Guidelines (PA Guidelines). The 9 guidelines are organized by phases of assessment and include descriptions of stakeholder activities. A user guide provides a glossary of key terms and summarizes how the guidelines can be used by different stakeholder groups across postgraduate medical education (PGME) contexts. The 4 phases of guideline development, including internal stakeholder consultations and external expert review, occurred between August 2016 and March 2020. OUTCOMES: Local stakeholders and external experts agreed that the PA Guidelines hold potential for guiding initial operationalization and ongoing refinement of PA in CBME by individual stakeholders, residency programs, and PGME institutions. Since July 2020, the PA Guidelines have been used at Queen's University to inform faculty and resident development initiatives, including online CBME modules for faculty, workshops for academic advisors/competence committee members, and a guide that supports incoming residents' transition to CBME. NEXT STEPS: Research exploring the use of the PA Guidelines and user guide in multiple programs and institutions will gather further evidence of their acceptability and utility for guiding operationalization of PA in different contexts.
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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 | 0.205 | 0.387 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".