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Record W3039354332 · doi:10.1016/j.jsurg.2020.06.023

Evaluating the Impact of Medical Student Inclusion Into Hands-On Surgical Simulation in Congenital Heart Surgery

2020· article· en· W3039354332 on OpenAlexafffund
Nicole Wing Lam Hon, Nabil Hussein, Osami Honjo, Shi‐Joon Yoo

Bibliographic record

VenueJournal of surgical education · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
FundersHospital for Sick Children
KeywordsCurriculumMedicineInclusion (mineral)Likert scaleSession (web analytics)Medical educationMedical schoolSurgical simulationSurgeryPsychologyPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: Over the last decade medical students' interest in pursuing surgery as a career has declined. This is more apparent in high-specialized specialities such as congenital heart surgery (CHS). Early hands-on simulation has shown to have a positive impact on medical students' interest in pursuing surgery, however, its incorporation into medical school curricula is lacking. This study aimed to evaluate the impact of incorporating medical students as surgical assistants during the Hands On Surgical Training course in CHS. METHODS: Local preclinical medical students were invited to participate as surgical assistants during the 5th annual Hands On Surgical Training course in CHS. Among those who responded to the invitation, students were randomly selected and allocated to assist a congenital heart surgeon. All selected students attended an assistants' session prior to the course to familiarize themselves with assisting and to practice basic surgical skills. At the end of both courses students completed a questionnaire based on Likert 5-point scale to evaluate the courses' usefulness. RESULTS: Fifteen medical students completed the questionnaires. All reported a beginner level of understanding of congenital heart disease. All students were highly satisfied with using 3D-printed models to help their understanding of congenital heart disease (4.80 ± 0.41) and agreed that the sessions improved their assisting skills (4.93 ± 0.26). All expressed a desire to attend similar sessions in the future and agreed that surgical simulation inclusion into medical school curricula would enhance learning (5.00 ± 0.00). Interest in pursuing a career in CHS increased from 33% (5) to 87% (13) by the end of the course. CONCLUSIONS: Integration of preclinical medical students into surgical simulation increases interest in pursuing highly specialised surgical specialities such as CHS. Early exposure and the incorporation of such simulation programs into medical school curricula will likely improve surgical skill acquisition and may enable students to be better informed when selecting future career choices.

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)0.006
metaresearch head score (Gemma)0.019
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 score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.108
GPT teacher head0.498
Teacher spread0.389 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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