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Record W2033216803 · doi:10.3402/meo.v8i.4342

A 3 Week Geriatric Education Program for 4<sup>th</sup>Year Medical Students at Dalhousie University

2003· article· en· W2033216803 on OpenAlexaffabout
Laurie Mallery, Janet Gordon, Susan Freter

Bibliographic record

VenueMedical Education Online · 2003
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeriatricsCurriculumMedical educationMedical schoolMedicineEducational programDemographicsPopulationFamily medicinePsychologyGerontologyPedagogySociologyPolitical science

Abstract

fetched live from OpenAlex

Purpose -Population demographics are shifting towards an increased average age. Yet, many medical schools still do not have mandatory comprehensive education in Geriatric Medicine. In 2001, the Division of Geriatric Medicine at Dalhousie University developed a required three-week geriatric course for fourth year medical students. This paper describes the details of the curriculum so that it can be reproduced in other settings. Results - The curriculum was successfully implemented. An examination, held at the end of each 3- week rotation, documented extensive learning of important concepts in Geriatric Medicine. The students gave positive feedback about the benefits of this training program. Conclusion -A well developed formal education program teaches students specific skills in Geriatric Medicine, which may improve the care of the growing elderly population. Key words: geriatric, geriatrics, elderly, curriculum, medical school 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 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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.003

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 head0.027
GPT teacher head0.422
Teacher spread0.395 · 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 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

Citations2
Published2003
Admission routes2
Has abstractyes

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