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Record W2413683698 · doi:10.1097/acm.0b013e3181f13321

Foreword

2010· article· en· W2413683698 on OpenAlexaboutno aff
Steven L. Kanter

Bibliographic record

VenueAcademic Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationMedical educationSnapshot (computer storage)MedicinePolitical sciencePsychologyComputer science

Abstract

fetched live from OpenAlex

This “Snapshot of Medical Student Education in the United States and Canada,” published as a supplement to the September 2010 issue of Academic Medicine, comes at an important time. It is the 100th anniversary of the release of the Flexner Report, and it has been 10 years since the publication of the first “Snapshot” in a supplement to the September 2000 issue of the journal. As such, this collection of reports has value for both contemporary readers and future historians. First, the reports are structured to facilitate comparison between the medical student education programs described in the present collection and those described in the 2000 collection, and also to compare the current programs with one another. Second, both this collection and the one published in 2000 are comprehensive. They include reports from almost every accredited medical education program leading to the MD degree in the United States and Canada. Third, the reports offer an important picture of advances, innovations, and initiatives in these medical student education programs that can help contemporary readers understand the status of medical student education today, and that can help current and future historians gauge progress over the last decade and century. Fourth, the reports reveal important similarities and differences among medical student education programs. For example, some schools have specially-designed experiences in research (often called “scholarly concentrations”), while other schools offer students key clinical experiences in rural settings. Some schools have traditional clerkships, while others have longitudinal ones. Several schools have added buildings devoted to medical student education, and many have integrated ethics into the curriculum as a required component. Many schools are expanding their educational programs to additional campuses, and new medical schools are establishing their own innovative educational programs. This set of reports provides ready access to this information. Fifth, the reports include information on the governance and management structure of educational programs, which situates the curriculum within the context of a school and provides key insights about how decisions are made. These reports are valuable to both established and new medical schools. Established schools must engage in a self-study process to prepare for accreditation every eight years. During the self-study, a school's faculty and students examine the structure and function of their school's medical student education program. These reports provide important benchmarks and reveal a range of ideas for faculty and students to consider as they evaluate their own program. The reports provide similar opportunities for new schools that seek to build new and innovative programs. So, put this supplement in a special place on your bookshelf, or bookmark the supplement's Web page—it will serve you well as you engage in curricular renewal, prepare for reaccreditation, or seek to understand your own curricular efforts in a broader perspective. I wish to thank Brownie Anderson for recognizing the importance of these reports, pursuing the development of this collection, and serving ably as its editor. Al Bradford's tireless and excellent work on the September 2000 supplement earned him an invitation to do it again ten years later. He responded admirably, and much credit for the quality of this report belongs to Al. Steven L. Kanter, MD

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: none
Teacher disagreement score0.660
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.6600.585

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 head0.023
GPT teacher head0.388
Teacher spread0.365 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations8
Published2010
Admission routes1
Has abstractyes

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