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Record W2883791151 · doi:10.1177/2054358118786972

Introduction of Competence by Design to Canadian Nephrology Postgraduate Training

2018· article· en· W2883791151 on OpenAlexaffabout
Maury Pinsk, Jolanta Karpinski, Euan Carlisle

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster UniversityRoyal College of Physicians and Surgeons of CanadaUniversity of Manitoba
Fundersnot available
KeywordsMedicineNephrologyCompetence (human resources)Medical educationInternal medicineFamily medicinePsychology

Abstract

fetched live from OpenAlex

PURPOSE OF THE REVIEW: The Royal College of Physicians and Surgeons of Canada, with its Competence by Design initiative, is adopting the principles of competency-based medical education for residency training and continuing professional development. This initiative is being undertaken to meet the new standards of medical education in Canada, which include social accountability to meet performance-based outcomes of training. Nephrology is poised to implement Competence by Design into residency training in July 2018 and initiate a continuous quality improvement cycle to periodically renew and update the training requirements to be socially accountable and relevant in the modern age of medicine. The purpose of this review is to describe the process of entrustable professional activity and required training experience development and how they will affect subspecialty training in Canada. SOURCES OF INFORMATION: The construct of competency-based medical education was derived from existing literature searches of the medical education literature, including documentation provided by the Royal College of Physicians and Surgeons of Canada. The content for each entrustable professional activity and milestone was derived by consensus from the community expertise of the working group, existing speciality training requirements, and elements of training requirements that the Royal College has been mandated to superimpose on all training requirements to meet societal expectations. METHODS: The Royal College Specialty Committee in Nephrology participated in 2 years of preparation for this implementation, which has included the creation of a new educational design for the discipline and the elucidation of entrustable professional activities to describe the scope of nephrology practice and to guide teaching, learning, and assessment in residency, and ultimately maintenance of competence in practice. KEY FINDINGS: This article introduces the set of entrustable professional activities for adult and pediatric nephrology and describes the national consultation as part of an ongoing quality improvement of this work. LIMITATIONS: The implementation of Competence by Design will be tested by whether trainees embrace competency-based education by training to just entrustable professional activities, rather than the holistic model idealized in physician training. This is mitigated by the entrustable professional activity development incorporating multiple layers of competencies beyond a procedural skill. Time commitment for faculty will pose additional challenges in increasing the time for assessment of trainees, but is supported by electronic platforms at the Royal College to assist in data gathering and analysis. IMPLICATIONS: Competence by Design in nephrology is an outcomes-based curriculum and assessment platform that aims to train nephrologists to meet societal expectations in an ever-changing and complicated health care system. The goals are to increase safety and professional accountability to society and improve upon the already high standards of training within Canada.

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)0.043
metaresearch head score (Gemma)0.106
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.241
Threshold uncertainty score0.515

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.106
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.013
Science and technology studies0.0030.007
Scholarly communication0.0090.003
Open science0.0030.005
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.026
GPT teacher head0.307
Teacher spread0.282 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations21
Published2018
Admission routes2
Has abstractyes

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