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Internationalization of Medical Education and the Anatomy Course ‐ Outcomes of an International Partnership of 11 Anatomy Departments in 4 Continents

2019· article· en· W3007076525 on OpenAlexaffabout
Anette Wu, Geoffroy Noël, Richard Wingate, Heike Kielstein, Mandeep Gill, Takeshi Sakurai, Suvi Viranta, C. L. Chien, Hannes Traxler, Jens Waschke, Franziska Vielmuth, Shuji Kitahara, Kevin A. Keay, Paulette Bernd

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsInternationalizationMedical educationPresentation (obstetrics)General partnershipDonationPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Study Objective The purpose of the study is to assess the value of an internationalization of medical education (IoME) program between 11 Anatomy departments that aims to prepare preclinical medical and dental students for future healthcare leadership roles, by providing them with skills in Public Health awareness, cultural competency, reflection on the topic of body donation, early networking opportunities and basic sciences experiences. Statement of Methods We previously reported on our experience in IoME with 6 countries that was initiated through the Anatomy courses. The present study includes 11 Anatomy departments on 4 continents (Australia, Austria, Canada, Finland, Germany (2), Japan (2), Taiwan, UK and USA) with a total of 183 preclinical Anatomy participants (n=18 dental students, n=165 medical students). Students worked in smaller groups than in the previous years (n=3–4). Discussion topics i.e. differences in the Anatomy courses, international healthcare education and delivery, Global/Public Health, health ethics and health law were further refined. Students worked on a collaborative paper, created a video presentation and presented their work at a large virtual conference. Subsequently they traveled to the partner countries to perform basic sciences research. Questionnaires after the travels were analyzed and are now being presented. A new data point includes an international discussion on the topic of body donation. Students interviewed each other about their thoughts on body donation and their experience working with donated bodies in the different countries and submitted written statements. Comparison of these written statements are underway. Summary of Results Results indicate that students felt that they learned from each other during the small group sessions in regard to healthcare education and delivery, Global/Public Health, health ethics and health law. We observed a level of appreciation of what they have at home and cultural awareness. Results also demonstrate that the travels help with building research techniques skills, cultural competency and social connections. We expect differences in the reflection on the topic of body donation due to the differences in the body donation process in the different countries. Conclusions We here present our updated experience with a unique international student exchange program that is introduced via the Anatomy course. Anatomy departments can serve as an anchor for international student exchanges, contribute to studies of cultural differences in body donation practices and contribute to Global/Public Health awareness education. Support or Funding Information None This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.382
Teacher spread0.367 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2019
Admission routes2
Has abstractyes

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