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Record W2781487553 · doi:10.5430/ijhe.v7n1p10

Integrating Simulation Scenarios and Clinical Practices Guided by Concepts of Translational Medicine

2018· article· en· W2781487553 on OpenAlexvenueno aff
Jing Yang, Simin Huang, Zejian Li, Lie Feng, Chunting Lu

Bibliographic record

VenueInternational Journal of Higher Education · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersJinan University
KeywordsInternshipCurriculumMedical educationClass (philosophy)Objective structured clinical examinationMedicineSignificant differencePsychologyMedical physicsComputer scienceInternal medicineArtificial intelligencePedagogy

Abstract

fetched live from OpenAlex

Purpose: To develop a novel method for closely and effectively integrating simulation scenarios and clinical practices to improve clinical skills training in the concepts of translational medicine.Methods: Forty-two and 38 third-year medical students in the classes of 2010 and 2009 at Jinan University were selected as an observation group and a control group, respectively. The former group was taught according to a new, integrated mode, while the latter received traditional methods. Students' scores on practical tests in physical examination, internal punctures, and case analysis; theory-based exams on diagnostics and internal medicine; and questionnaire surveys were compared and analyzed. In addition, system-oriented curricula were explored and implemented.Results: A novel mode that closely and effectively integrates theory and practice in the observation group had been established although there were no statistically significant difference (P>0.05) between Grade 2010 and Grade 2009 in clinical basic skills training scores. However, there were statistically significant differences (P<0.05) in scores on practical tests of physical examination and internal punctures among the diagnostic, internal medicine and internship periods in the class of 2010 but no statistically significant difference (P>0.05) in case analysis scores. Therefore, system-oriented curricula were initially designed and explored in excellent students from Grade 2010 to reinforce clinical thinking.Conclusion: The novel program integrating simulation scenarios and clinical situations for training students in diagnostics and internal medicine skills can improve medical students’ clinical comprehensive abilities and achieve effects that are similar to those of the traditional method. This program is more popular with students and ensures patient safety as well. In addition, different characteristics of clinical skills training have been compared for the further exporation of system-oriented curricula.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.529
Teacher spread0.467 · 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 designNot applicable
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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Citations1
Published2018
Admission routes1
Has abstractyes

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