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Record W2143252620 · doi:10.3109/0142159x.2012.689446

An evaluation of the ‘5 Minute Medicine’ video podcast series compared to conventional medical resources for the internal medicine clerkship

2012· article· en· W2143252620 on OpenAlexaff
Neeraj Narula, Liban Ahmed, Jill Rudkowski

Bibliographic record

VenueMedical Teacher · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsMedical educationMedicineOnline videoResource (disambiguation)Educational resourcesClinical clerkshipInternal medicineMultimediaComputer scienceCurriculumPsychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: '5 Minute Medicine' (5MM) is a series of video podcasts, that in approximately 5 min, each explain a core objective of the internal medicine clerkship that all clinical clerks should understand. Video podcasts are accessible at www.5minutemedicine.com AIM: The aim of this study was to investigate how well received 5MM video podcasts are as an educational tool for clinical clerks to use while on call. METHODS: Clinical clerks rotating through their internal medicine clerkship rotation were asked to use the 5MM video podcasts or conventional resources to prepare themselves prior to seeing patients. Questionnaires were distributed to students to determine effectiveness, appropriateness and time-efficiency of the resources students used. RESULTS: Students almost unanimously strongly agreed or agreed that the 5MM video podcasts were effective learning tools, appropriate for clinical clerks and time-efficient, more so than conventionally used resources. The vast majority of clerks selected the 5MM videos as their preferred resource of all resources available to them. Most clerks felt the 5MM videos were better than textbooks and conventional online resources. CONCLUSION: Video podcasts such as the 5MM videos are welcomed as educational tools and may have a role in the future of undergraduate medical education.

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)0.027
metaresearch head score (Gemma)0.079
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.275
GPT teacher head0.493
Teacher spread0.218 · 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
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

Citations51
Published2012
Admission routes1
Has abstractyes

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