Developing and Successfully Implementing a Competency-Based Portfolio Assessment System in a Postgraduate Family Medicine Residency Program
Bibliographic record
Abstract
The use of portfolios in postgraduate medical residency education to support competency development is increasing; however, the processes by which these assessment systems are designed, implemented, and maintained are emergent. The authors describe the needs assessment, development, implementation, and continuing quality improvement processes that have shaped the Portfolio Assessment Support System (PASS) used by the postgraduate family medicine program at Queen's University since 2009. Their description includes the impetus for change and contextual realities that guided the effort, plus the processes used for selecting assessment components and developing strategic supports. The authors discuss the identification of impact measures at the individual, programmatic, and institutional levels and the ways the department uses these to monitor how PASS supports competency development, scaffolds residents' self-regulated learning skills, and promotes professional identity formation. They describe the "academic advisor" role and provide an appendix covering the portfolio elements. Reflection elements include learning plans, clinical question logs, confidence surveys, and reflections about continuity of care and significant incidents. Learning module elements cover the required, online bioethics, global health, and consult-request modules. Assessment elements cover each resident's research project, clinical audits, presentations, objective structured clinical exam and simulated office oral exam results, field notes, entrustable professional activities, multisource feedback, and in-training evaluation reports. Document elements are the resident's continuing medical education activities including procedures log, attendance log, and patient demographic summaries.The authors wish to support others who are engaged in the systematic portfolio-design process or who may adapt aspects of PASS for their local programs.
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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 | 0.035 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".