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Record W2098630132 · doi:10.1080/01421590802061134

Models of medical education in Australia, Europe and North America

2008· article· en· W2098630132 on OpenAlexaff
Susan P. Phillips

Bibliographic record

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsAccreditationCurriculumMedical educationProcess (computing)Medical knowledgePsychologyMedicineEngineering ethicsPedagogyComputer scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The universal goal of medical education is to train excellent physicians, able to maintain the health of individuals and communities. The route to achieving this goal has shifted over time. This paper describes the absolutes and ambiguities of philosophical vision, responsiveness to stakeholders, curriculum content and delivery, and assessment of trainees and training programs across much of the developed world. DESCRIPTION: Traditional medical education is content focused and organized by organ systems. Newer curricula, informed by current learning theories, emphasize a competencies based approach, with clinical scenarios at the centre of teaching and assessment of students. Associated with this is a shift from the in-depth knowledge of the specialist to a 'what must a generalist know' approach. These models are explored as are options for curriculum delivery, input from governments, students, the public and faculty, and methods and importance of accreditation. CONCLUSION: The goals and the process of training physicians to achieve these exhibit numerous commonalities across time and place throughout the developed world while still allowing for cultural or national adaptations. All models and content aim for minimum basic knowledge, while emphasizing communication skills, cultural awareness and professionalism amongst future physicians.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.369
Teacher spread0.313 · 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 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

Citations14
Published2008
Admission routes1
Has abstractyes

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