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Record W4317435943 · doi:10.1097/hco.0000000000001010

Engaging medical students in cardiac surgery: a focus on equity, diversity, and inclusion

2023· review· en· W4317435943 on OpenAlexaffabout
Lina A. Elfaki, Akachukwu Nwakoby, Hillary Lia, Xin Zhao, Amanda Sicila, Nao Yoshida, Bobby Yanagawa

Bibliographic record

VenueCurrent Opinion in Cardiology · 2023
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMentorshipMedicineCardiac surgeryDiversity (politics)Medical educationUnderrepresented MinorityInclusion (mineral)Cardiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The Coronavirus Disease 2019 pandemic prohibited Canadian medical students from in-person observerships. This may be particularly detrimental to under-represented groups that may consider surgical subspecialties. To address the unprecedented need for alternative surgical career exploration and diversity within the profession, The University of Toronto Cardiac Surgery Interest Group and Division of Cardiac Surgery collaborated on virtual experiential programming. RECENT FINDINGS: Medical students were invited to virtual (1) observerships of a cardiac bypass case, (2) mentorship sessions with surgeons, (3) resident teaching sessions, (4) multidisciplinary case-based Heart Team discussions to further their understanding of the scope of Cardiac surgery, and (5) a virtual coronary anastomosis training program. Additionally, a comprehensive virtual program was spearheaded to increase interest in Cardiac surgery among low-income Black high school students. SUMMARY: Trainee response to the virtual education, mentorship, and skill acquisition was positive. Trainees reported high levels of interest in the profession, particularly among females and under-represented minorities, supporting the principles of equity diversity, and inclusion in Cardiac surgery.

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.047
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 head0.291
GPT teacher head0.492
Teacher spread0.201 · 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 designNot applicable
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

Citations5
Published2023
Admission routes2
Has abstractyes

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