MétaCan
Menu
Back to cohort
Record W2056996646 · doi:10.1207/s15328015tlm1704_10

Dimensions of Clinical Medicine: An Interclerkship Program

2005· article· en· W2056996646 on OpenAlexaff
Henry A. Sakowski, Ronald J. Markert, William B. Jeffries, Robert M. Coleman, Bruce Houghton, Sade Kosoko-Lasaki, Mark Goodman, Eugene C. Rich

Bibliographic record

VenueTeaching and Learning in Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsAccess Alliance Multicultural Health and Community Services
Fundersnot available
KeywordsCurriculumMedical educationMedicineClinical PracticeAlternative medicineMedical schoolPalliative careFamily medicineNursingPsychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: During the 3rd year of medical school, clerkships provide essential clinical experiences. However, other aspects of patient care may be overlooked during this critical phase of medical education. Faced with the challenge of integrating "orphan" topics central to effective and compassionate medical practice, medical schools have begun to develop interclerkships or intersessions. DESCRIPTION: The Dimensions of Clinical Medicine (DCM) interclerkship series at Creighton University included evidence-based medicine, sexuality in clinical medicine, palliative care, professionalism, cultural sensitivity/awareness, complementary and alternative medicine, bioterrorism, and clinical ethics. Each interclerkship comprises 2 half-day sessions conducted at the end of each clerkship rotation. EVALUATION: Students approved of the interclerkship format and valued the active learning strategies employed in the course. Many students felt the time allotted for each program was excessive. We describe educational issues and practical concerns that are central to an effective intersession program. CONCLUSION: The interclerkship format is a viable approach for incorporating orphan topics into the clinical curriculum.

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)0.010
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.066
GPT teacher head0.475
Teacher spread0.409 · 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 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

Citations7
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueTeaching and Learning in MedicineSame topicInnovations in Medical EducationFrench-language works237,207