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Record W2138191966 · doi:10.1093/ageing/afm047

Undergraduate training in geriatric medicine: getting it right

2007· article· en· W2138191966 on OpenAlexaboutno aff
Frank Lally, Peter Crome

Bibliographic record

VenueAge and Ageing · 2007
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedicineGeriatricsSpecialtyMedical educationQuarter (Canadian coin)Vulnerability (computing)Medical schoolUnit (ring theory)Family medicineNursingPsychologyPedagogyPsychiatry

Abstract

fetched live from OpenAlex

It is pehaps inevitable that every specialty within medicine believes that its fundamental principles should be included in the core undergraduate curriculum and should be accompanied by a compulsory placement. The General Medical Council's (GMC) policy document ‘Tomorrow's Doctors’ [ 1 ] stresses the importance of students learning about the special problems associated with older people's health. For example, it states that young doctors must respect age and the vulnerability of particular patient groups including older people. They also emphasise that graduates must understand human development, which includes growing old. Importantly, the document states that students must have opportunities to interact with a range of people including visiting an older person, a learning experience that is now included in many curricula. However, they are silent on the issue of whether there should be a compulsory attachment to a geriatric medicine unit, as indeed they are, about other hospital specialties. The present policy suggests that a quarter to a third of the curriculum should be based on student-selected components. The interpretation and implementation of such advice will be different in each medical school but the risk is, that at this time of demographic change, with growing numbers of older people, and with other demands on the curriculum, exposure to geriatric medicine might be overlooked and reduced.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.798
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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 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

Citations29
Published2007
Admission routes1
Has abstractyes

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