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Teaching Anatomy to meet the CanMEDs competencies

2017· article· en· W4389028457 on OpenAlexaffabout
Claudia Krebs, Bruce Wainman

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedical educationCurriculumSet (abstract data type)Small group learningMedicinePsychologyPedagogyComputer science

Abstract

fetched live from OpenAlex

The Royal College of Physicians and Surgeons of Canada developed CanMEDS to provide a framework for postgraduate medical education. This framework has been adopted by many countries and now serves as a guide in the development of undergraduate, postgraduate and continuing medical education. Globally, medical education is moving towards a competency‐based model where undergraduates and postgraduates achieve competencies developed by their programs and accrediting bodies. Anatomy is a natural integrator across disciplines within medical education. When we look at anatomy education, we can develop a curriculum that is based on the competencies as they are defined for undergraduate and postgraduate education – thereby providing a continuous educational framework that will inform students' professional development. Here we propose a curricular model for anatomy that integrates all CanMEDs competencies: the medical expert at the center, communicator, collaborator, leader, health advocate, scholar, and professional. The proposed curriculum integrates the classic approaches to anatomy through cadaver dissection with radiological imaging technology, small group learning, online resources, and interprofessional experiences. The communicator role relates to the relationship between physicians and their patients. Effective communication skills need to be practiced early on, and this can be achieved through the encouraging effective communication among group members. The collaborator role is addressed through deliberate interprofessional learning opportunities where students collaborate on a problem set or clinical case to achieve specific learning objectives. The role of leader can be achieved through peer teaching during their anatomy sessions, or encouragement to take ownership and leadership for their learning. The role of health advocate is addressed through an understanding of disease and its anatomical / pathological manifestation. Advocacy comes through a combination of knowledge and a sense of social responsibility to use this knowledge to improve health outcomes. The role of scholar is achieved through the learning of the didactic material, the exploration of pathology specific to the cadaver they are working on. This can be demonstrated through presentations or portfolios that integrate the pathology with the anatomy. The exploration of specific pathology can also lead to a research questions related to the findings, another manifestation of the scholar role. The role of professional is developed through reflective practices as a learning tool to develop the responsibility, dedication, growth, and identity formation for their role as an MD. This transformative learning can begin in the anatomy lab as students transition from being undergraduate students to being health professional students. An attitude of professionalism is integral for the development of a sense of responsibility and care for the cadaver as a precious learning resource. At the center of the Venn Diagram is the medical expert – an embodiment of all of these competencies. As we educate our students to become health professionals one of their first experiences is in the anatomy lab. We have the opportunity to integrate the values of these competencies into their education and create a curriculum based on a framework that will accompany them throughout the continuum of their education. Support or Funding Information AAA Visiting Scholar Award

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.255
Teacher spread0.243 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2017
Admission routes2
Has abstractyes

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