Electronic portfolios for assessment in your postgraduate medical education program: essential questions to ask when selecting a platform for competency-based medical education (CBME)
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Portfolios have re-emerged as a means of addressing the complex assessment challenges in postgraduate programs as we transition to competency-based medical education (CBME). However, implementing electronic portfolios (e-portfolios) can be a daunting task. In this paper, we aim to provide guidance to program directors and administrators who are tasked with the implementation of an e-portfolio by reporting on the successful development of a low-cost e-portfolio system for our Developmental Pediatrics residency program. Twelve helpful tips have been developed: 1. Can the e-portfolio be customized to meet the needs of my program? What resources are required to customize the system? (tailor to fit); 2. Is it easy to learn, efficient to use, and easy to remember? (easy-peasey-lemon-squeezy); 3. Can it be customised by the learner? (make it their own, home sweet home); 4. Is the e-portfolio portable? Can the learner access the portfolio after completing training? (make it to go); 5. What are the privacy settings and controls? Are the privacy/sharing settings transparent and easy for everyone to understand? (private parts); 6. What level of mobile support does the system provide? (anywhere, anyplace, anytime); 7. How can mentor feedback be incorporated? (feedback friendly); 8. What are the financial (and hidden) costs? (count the costs); 9. How long do you need the platform to be sustainable? Can the platform give you the sustainability that you need? (check the expiry date); 10. Access to support (know who's got your back); 11. What kind of support is available? How fast can you get the support you need? (share and share alike); and 12. Can it be scaled for use with each new cohort of learners? (keep up with the times). In addition to the twelve tips, this paper includes insightful commentary from a program director and a recently graduated resident who used the portfolio throughout her training. As we continue to move toward competency-based learning, e-portfolios will provide a platform and stimulus for sustained excellence in medical education.
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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.025 |
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".