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DOUBLE HELIX CURRICULUM AS AN INNOVATIVE MODEL OF MEDICAL EDUCATION: THEORETICAL REFLECTIONS AND PRACTICAL ANALYSIS OF THE ADVANTAGES OF AN INTEGRATED APPROACH

2025· article· uk· W4413853413 on OpenAlexaboutno aff
Tetiana Yashkina

Bibliographic record

VenueПерспективи та інновації науки · 2025
Typearticle
Languageuk
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumHelix (gastropod)Engineering ethicsMathematics educationEngineeringComputer scienceSociologyPsychologyPedagogy

Abstract

fetched live from OpenAlex

The article explores the Double Helix Curriculum (DHC) as an innovative model of medical education designed to ensure the equal and continuous integration of biomedical and humanitarian components throughout all stages of physician training.It substantiates the relevance of implementing DHC in the context of contemporary healthcare challenges, which require physicians to possess not only advanced scientific knowledge but also developed clinical reasoning, interdisciplinary collaboration skills, ethical reflection, and communication abilities within complex sociocultural environments.Drawing on international experience from institutions such as the University of Michigan, UCSF, and the University of Toronto, the author outlines the model's two intertwined "strands": the scientific strand (covering basic and clinical sciences) and the humanitarian strand (encompassing medical ethics, communication, social determinants of health, self-reflection, and narrative medicine).The article emphasizes that, within DHC, professional identity is cultivated systematically rather than incidentally, through students' engagement in communities of practice, narrative modeling of clinical cases, facilitated reflection, and integrated learning scenarios.A comparative analysis with other educational approaches (Spiral Curriculum, Case-Based Learning, Vertically Integrated Curriculum, Problem-Based Learning, Competency-Based Medical Education) demonstrates DHC's unique capacity to merge cognitive and value-based competencies.The paper also examines opportunities for adopting DHC in Ukrainian medical education, identifying potential barriers such as curriculum inertia, insufficient faculty preparation for interdisciplinary teaching, and lack of validated tools to assess humanitarian competencies, as well as suggesting strategies to address them.The author concludes that DHC promotes the harmonization of Ukrainian medical education with European standards and fosters the development of a holistic new-generation physician who combines scientific rigor with professional empathy and humanistic values.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.357
Teacher spread0.342 · 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 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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