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Record W2007614245 · doi:10.1097/acm.0b013e3182623143

Preparing Students for Clerkship

2012· article· en· W2007614245 on OpenAlexaff
Simon R. Turner, Jonathan White, Cheryl Poth, W. Todd Rogers

Bibliographic record

VenueAcademic Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPreparednessMedical educationClinical clerkshipShadow (psychology)Medical schoolProgram evaluationPsychologyIntervention (counseling)Scale (ratio)MedicineCurriculumNursingPedagogy

Abstract

fetched live from OpenAlex

The preparation of medical students for clerkship has been criticized, in terms of both students understanding of their new role as clinical trainees and their ability to carry out that role. To begin to address this gap, the authors report the development, implementation, and assessment of a novel program in which first-year medical students shadow first-year residents during their clinical duties. The program matches each student to a single resident, whom they shadow for several hours, once per month, for eight months. In the programs inaugural year (2009-10), 83 student-resident pairs participated; over 70% responded to pre- and post-intervention questionnaires, which included an 18-item preparedness scale. The authors used those responses to evaluate the program. Compared to students in a control group, the students in the program assessed themselves as better prepared to learn in a clinical setting. The low-cost student-resident shadowing program described in this article provided an early and structured introduction to the clinical environment, which may help prepare students for the transition into clerkship.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 integrity0.0000.001
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 head0.072
GPT teacher head0.465
Teacher spread0.393 · 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 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

Citations38
Published2012
Admission routes1
Has abstractyes

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