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Record W2604167513 · doi:10.15694/mep.2017.000066

Electronic portfolios for assessment in your postgraduate medical education program: essential questions to ask when selecting a platform for competency-based medical education (CBME)

2017· article· en· W2604167513 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedEdPublish · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPortfolioElectronic portfolioTask (project management)Computer scienceAsk priceBusinessMedical educationEngineering managementProcess managementKnowledge managementMedicineFinanceEngineering

Abstract

fetched live from OpenAlex

<ns4:p>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.</ns4:p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.473
Teacher spread0.432 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it