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Record W2410144470 · doi:10.11622/smedj.2016043

Evaluation of a training programme to induct medical students in delivering public health talks

2016· article· en· W2410144470 on OpenAlexaff
Ngiap Chuan Tan, YL Koh, Seng Bin Ang, HH Chan, CH How, EG Tay, SW Hwang

Bibliographic record

VenueSingapore Medical Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHand and Upper Limb Clinic
Fundersnot available
KeywordsMedicineCLARITYMedical educationCurriculumPublic healthHealth careAudience responseNursingPsychologyPedagogy

Abstract

fetched live from OpenAlex

INTRODUCTION: It is uncommon for medical students to deliver public health talks as part of their medical education curriculum. This study evaluated the effectiveness of a novel training programme that required medical students to deliver public health talks during their family medicine (FM) clerkship in a Singapore primary care institution. METHODS: The FM faculty staff guided teams of third-year medical students to select appropriate topics for health talks that were to be conducted at designated polyclinics. The talks were video-recorded and appraised for clarity, content and delivery. The appraisal was done by the student's peers and assigned faculty staff. The audience was surveyed to determine their satisfaction level and understanding of the talks. The students also self-rated the effectiveness of this new teaching activity. RESULTS: A total of 120 medical students completed a questionnaire to rate the effectiveness of the new teaching activity. 85.8% of the students felt confident about the delivery of their talks, 95.8% reported having learnt how to deliver talks and 92.5% perceived this new training modality as useful in their medical education. Based on the results of the audience survey, the speakers were perceived as knowledgeable (53.1%), confident (51.3%) and professional (39.0%). Assessment of 15 video-recorded talks showed satisfactory delivery of the talks by the students. CONCLUSION: The majority of the students reported a favourable overall learning experience under this new training programme. This finding is supported by the positive feedback garnered from the audience, peers of the medical students and the faculty staff.

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)0.044
metaresearch head score (Gemma)0.056
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.195
GPT teacher head0.471
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations6
Published2016
Admission routes1
Has abstractyes

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