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Integrating Clinical Medicine with the Basic Sciences; Pulmonology Learning Modules for Medical Students

2016· article· en· W4389024749 on OpenAlexaff
Majid Doroudi, Babak Hoomayon, Richard Cohen

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsUniversity of British Columbia HospitalSpinal Cord Injury BC
Fundersnot available
KeywordsMemorizationRote learningPresentation (obstetrics)Test (biology)Relevance (law)Medical educationMathematics educationComputer sciencePsychologyMedicineTeaching methodRadiology

Abstract

fetched live from OpenAlex

Many medical students find memorizing the detailed of their basic science courses difficult and daunting, especially if they do not know what the application of that subject is during their medical practice. The overall objective of this study was to encourage students to adopt a more anatomical approach to clinical skills and also to show explicitly the relevance of pulmonary anatomy and embryology, thus moving away from rote memorization of physical exam skills and anatomical knowledge. In order to accomplish the goal of interactive case‐based modules we used the Articulate Engage software suite to create two modules that progress slide by slide in order to encourage a logical flow from patient presentation through to treatment. Using Articulate we were also able to add interactive labeled diagrams to review pulmonary anatomy, pictures to study clinical skills, and integrated quizzes to test student knowledge. The pulmonary modules were evaluated objectively from the survey questionnaire results and subjectively from the comments from the results. Before the modules 20% of students felt they knew the pulmonary materials well or very well. After the modules 58% of students felt they knew the pulmonary materials well or very well. Over 90% of students surveyed strongly or very strongly wanted to see more modules like these modules in their program.

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)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.483
Teacher spread0.386 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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