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Record W1989841489 · doi:10.4300/jgme-d-13-00445.1

Competency-Based Education: Milestones or Millstones1?

2014· article· en· W1989841489 on OpenAlexaboutno aff
Geoff Norman, John J. Norcini, Georges Bordage

Bibliographic record

VenueJournal of Graduate Medical Education · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationMedicineData scienceComputer sciencePsychology

Abstract

fetched live from OpenAlex

In the past decade, there has been increasing interest in competency-based education (CBE), the notion that an expert physician is defined by a broad set of identified competencies. The idea has been advanced, nearly simultaneously, in several countries—Canada (CanMEDS roles),1 the United States (Accreditation Council for Graduate Medical Education [ACGME] competencies),2 the United Kingdom (Tomorrow's Doctor),3 and Scotland (the Scottish Doctor)4—and adopted by others. CanMEDS competencies have been adopted and adapted by 16 countries, including the Netherlands, Denmark, and Mexico.5 Moreover, the Carnegie Foundation's influential Flexner centenary strongly recommended adopting CBE and made the claim that “Adoption of OBE [outcome-based or competency-based education] would better equip medical graduates to respond effectively in complex situations and efficiently continue to expand the depth and breadth of the requisite competencies.”,6,7 Similar promises emerge from many of these foundational documents. Harden8(p666) ascribes a number of advantages to OBE/CBE: OBE is a sophisticated strategy for curriculum planning that offers a number of advantages. It is an intuitive approach that engages the range of stakeholders . . . it encourages a student-centred approach and at the same time supports the trend for greater accountability and quality assurance . . . [it] highlights areas in the curriculum which may be neglected . . . such as ethics and attitudes . . .. Regrettably, these declarations appear to be more a matter of faith than of evidence. The primary intent of CBE is, we believe, transparency, so that the profession and the public can be confident that a training program is producing competent physicians who are equipped with the knowledge and skills for practice. It is hard to challenge that premise; the issue is whether the proposed mechanisms can deliver on the promise. A corollary common to both CBE and its predecessor—behavioral objectives—is the notion that different learners will achieve different competencies at different rates, so that residents may be certified competent in starting an intravenous line early in their career but may take longer to achieve competency in intubation. The individual need not take additional time practicing skills for which he or she is competent and can, therefore, learn more efficiently by focusing on those skills for which competence has not yet been achieved. An extrapolation of that notion is that some residents may well achieve all the competencies available on a particular rotation earlier or later than others, and so, can progress through graduate medical education (GME) at a different pace. Whether the approach can be operationalized satisfactorily at the level of precision required to implement CBE, the fact remains that it may have positive side benefits, such as increased observations of residents, greater attention to the GME curriculum, and so forth. To ensure that those goals are met and the implementation of CBE is not sidelined by a ponderous administrative superstructure, this editorial is intended to elaborate potential problems at 3 levels: conceptual, psychometric, and logistic.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.019
Scholarly communication0.0100.026
Open science0.0030.010
Research integrity0.0070.021
Insufficient payload (model declined to judge)0.0110.005

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 head0.027
GPT teacher head0.365
Teacher spread0.339 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

Quick stats

Citations122
Published2014
Admission routes1
Has abstractyes

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