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Bridging the Workforce Gap for Our Aging Society: How to Increase and Improve Knowledge and Training. Report of an Expert Panel

2005· article· en· W1502247334 on OpenAlex
Alice Mankin LaMascus, Marie Bernard, Patricia P. Barry, Judith A. Salerno, Joan Weiss

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Geriatrics Society · 2005
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsInstitute of Aging
FundersUniversity of Michigan
KeywordsGeriatricsWorkforceMedicineHealth careCurriculumEconomic shortageMedical educationKnowledge baseAging in the American workforceBridging (networking)GerontologyNursingGovernment (linguistics)Psychology

Abstract

fetched live from OpenAlex

The healthcare workforce is currently unprepared for the increasing number of older persons and the complexities of their healthcare needs. Too few healthcare workers are adequately trained in geriatrics, and developers of educational curricula across healthcare disciplines have been slow to incorporate or require geriatric training. In April 2003, leaders in geriatrics met in Washington, D.C., to discuss and recommend solutions to the growing shortage of an appropriately trained workforce for geriatric research, education, and patient care. After considering data, presenting statistics, and offering insights into the future, the conference concluded by formulating recommendations to meet specific challenges. This report is a summary of the conference proceedings and recommendations, and it serves as a reminder that demographic trends and an everexpanding geriatric knowledge base demand not only attention, but also action.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.088
GPT teacher head0.435
Teacher spread0.347 · 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