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Can integrated interactive modules help enhance learning of anatomy, physiology, embryology and clinical sciences of the cardiovascular system?

2020· article· en· W3016326660 on OpenAlexaff
Majid Doroudi, David Shepherd, Carol-Ann Cournyea, Johnathan Tang, Mike Patterson

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPresentation (obstetrics)PaceContext (archaeology)PhysiologyCurriculumComputer scienceMedical educationAnatomyPsychologyMedicineBiologyRadiologyPedagogy

Abstract

fetched live from OpenAlex

Background Integration of the basic and clinical sciences is one of the cornerstones of the medical curricula. Studies have shown that integration helps the learners to not only know the application of what they learn in the lectures and labs but help them to retrieve the information better when they start their clinical rotations. Meantime, interactive modules allow for students to learn at their own pace and engage with their learning through quizzes and media to best supplement learning. Most medical schools have reduced the total hours allocated for anatomy & physiology teaching and laboratory practical hours. These changes have been a continuous debate and triggered the emergence of innovative teaching and learning strategies in order to maximize students’ learning of anatomy, physiology, and embryology in the new context. The purpose of this project was to develop integrated interactive modules that can help support the learning of the medical students in the UBC’s Faculty of Medicine. The modules were based on the content of a week‐long topic on the arterial septal defect and a week‐long topic on the ischemic heart disease and focused on the integration of clinical knowledge with the concurrent instruction of pertinent anatomy, physiology, and embryology of the heart. Methods Two interactive virtual patient modules were developed to integrate the anatomy, physiology, embryology, and clinical skills of the cardiovascular system. We used the Articulate Engage software suite to create the modules. The modules progress slide‐by‐slide in order to encourage a logical flow from patient presentation through to treatment. We have used interactive labeled diagrams to review the embryology, anatomy, and physiology of the cardiovascular system and in‐module quizzes to test student knowledge. As well, several multiple choice questions are included throughout the modules that must be completed in order to progress through the cases. We also had the following learning activities in our modules: team work & team communication, diagnosis and decision‐making, and challenging situation in patient care. The modules were made available to students on the curriculum website and completion of a voluntary survey was encouraged at the end of the module. Results 100% of the participants were either strongly agreed or agreed that the modules complemented or enhanced the learning in lectures, CBL and labs. Basic science knowledge of the heart of the participants improved from 16.7 % to 83.3%. More than 83% of the participants felt that the integration of the clinical case and the anatomy, physiology, and embryology was either effective or very effective in assisting learning of the curricular content. 100% of the participants would like to see more modules like this for curricular content. Conclusions The development of integrated interactive modules that explore clinical, anatomical, physiological, and embryological content curricular objectives improve students’ subjective knowledge of clinical and anatomical sciences. Students found it useful for their learning to integrate cases into the core concepts of basic sciences and would like to see more modules like this in the future.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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