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Preliminary exploration on the training of medical students' humanity and communication skills through situation simulation and behavior correction

2015· article· en· W3031531145 on OpenAlexaboutno aff
Beili Xu, Yiqin Huang, Yan Zhang, Hua Chen, Yiyun Cai, Yunchao Shao, Guoqiang Fei, Yanni Lai

Bibliographic record

VenueZhonghua yixue jiaoyu tansuo zazhi · 2015
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyHumanitySet (abstract data type)PsychologyCommunication skillsClass (philosophy)Medical educationInterrogationMathematics educationComputer scienceSocial psychologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

The course Doctoring is designed mainly to deal with the practice of medical communication skills and clinical situation by the view of humanity and professionalism. Undergraduates in the early learning phase were taught with advanced communication skills on the basis of Calgary-Cambridge guidelines by using standard patient based simulation and behavior correction. Teachers using methods like class teaching, group discussion, simulating diagnosis and treatment, self and mutual comments and final summary to give doctoring lessons which emphasize on students' equal opportunities to practice. Questionnaires were used to evaluate teaching effect. Most of the students learned empathy, the structure of the interrogation, the preliminary master communication skills, self-confidence to communicate with patients. All the students think that this course can help medical work in the future, so it is necessary to set up this course. Teachers also gave positive feedback. Key words: Situation simulation; Behavior correction; Undergraduate education; Doctor-patient communication; Medical humanities

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.110
GPT teacher head0.398
Teacher spread0.288 · 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 designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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