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Development of a Model for Evidence Based Integration of Anatomy into a Competency Based Undergraduate Medical Education Curriculum

2017· article· en· W4389023381 on OpenAlexaffabout
Madeleine E. Norris, Marjorie I. Johnson, Kem A. Rogers, Charys M. Martin

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsCurriculumMedical educationMedicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

Introduction Within the past decade, competency‐based medical education (CBME) has acquired a lot of attention. This pedagogical approach is a framework that assesses student performance based on their ability to use their knowledge and skills, and ensures all graduates are proficient in all outlined competencies. Anatomical science is one of the foundations for clinical application, but it remains unclear to students and educators what anatomically related abilities students are expected to achieve prior to clerkship. The Schulich School of Medicine and Dentistry (The University of Western Ontario, London, Ontario, Canada) is undergoing a curriculum renewal to implement a CBME pedagogical approach. As a result, the integration of anatomy into the curriculum will be modified. To ensure our third‐year medical students are competent in the necessary fundamentals, an evidence‐based approach must be taken to integrate the anatomical sciences into the new curriculum. Thus, the specific aims of this study are to: i) develop a questionnaire to establish anatomical concepts necessary for clerkship, ii) interview all clerkship directors within UME, and iii) create an assessment to determine the anatomical knowledge base of third‐year students entering clerkship rotations. Methodology A questionnaire, targeting pre‐clerkship anatomical education, will be created and used as a guide during interviews with all clerkship directors. Information will be gathered from the clerkship directors using this questionnaire for all six clerkship rotations (family medicine, internal medicine, obstetrics and gynaecology, paediatrics, psychiatry, and surgery). The information collected will then be used to create assessments to analyze the current anatomical knowledge of third‐year students entering their clerkship year. Outcomes Information gathered from the interview questionnaire will provide insights into what anatomical knowledge students should be able to apply to clinical scenarios before starting each clerkship rotation. Furthermore, this interview should also reveal anatomical concepts that could be taught in the clerkship year to integrate basic science learning with clinical learning. The same information will be utilized to create an assessment, which will be used to determine if our third‐year clerks are currently prepared, in regards to their anatomical knowledge application, within the current UME curriculum. Discussion The primary goal of CBME is to produce physicians who are well‐equipped with the knowledge, skills, and attitudes needed to successfully practice medicine. Anatomical knowledge is one fundamental for this success. Information collected form clerkship directors, in regards to the application high‐yield anatomical knowledge necessary for third‐year clerks, will be linked to expected abilities and milestones defined by the competency‐based UME curriculum. Furthermore, the data collected from the assessments will provide evidence to inform how we design and deliver anatomy in the renewed CBME curriculum at the Schulich School of Medicine and Dentistry.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.312
Teacher spread0.284 · 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 designSimulation or modeling
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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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