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Record W2640652815 · doi:10.5116/ijme.5929.4381

Can CanMEDS competencies be developed in medical school anatomy laboratories? A literature review

2017· review· en· W2640652815 on OpenAlexaffabout
Joshua Hefler, Christopher J. Ramnanan

Bibliographic record

VenueInternational Journal of Medical Education · 2017
Typereview
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedical educationCore competencyHealth professionalsPsychologyMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of this literature review was to identify potential ways in which undergraduate medical anatomy education may be relevant to the CanMEDS Roles, a competency-based framework used throughout Canadian medical training. METHODS: A scoping review of medical education literature was conducted in March 2017 for English language publications that included key words related to anatomy education and to key competencies formally described for each of the Roles in the CanMEDS 2015 framework. Indicated benefits were then collated, characterized, and synthesized for each CanMEDS Role. RESULTS: There were 71 studies identified describing original findings. Perceived benefits of anatomy education were most often identified for competencies related to the Medical Expert Role. Multiple studies also cited benefits related to the Scholar, Professional and Collaborator Roles. There was a lack of literature related to the Health Advocate, Communicator, and Leader Roles. The majority of benefits defined in the literature were limited to student perceptions rather than objectively measured outcomes. CONCLUSIONS: There is some evidence to suggest that anatomy education can facilitate the development of core competencies related to several CanMEDS Roles, outside of simply developing medical knowledge in the Medical Expert Role. Future studies need to develop methods to objectively assess outcomes related to these competencies.

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)0.002
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.415
Teacher spread0.385 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations19
Published2017
Admission routes2
Has abstractyes

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