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Record W2899558970 · doi:10.1093/geroni/igy023.721

IAGG WORLD-WIDE REPORTS ON THE ELDER CARE WORKFORCE

2018· article· en· W2899558970 on OpenAlexaboutno aff
Toni C. Antonucci, J. L. Angel, Jean‐Pierre Michel

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceLatin AmericansPopulation ageingPopulationAging in the American workforceWork (physics)GerontologyPolitical scienceEconomic growthMedicineEngineeringEnvironmental health

Abstract

fetched live from OpenAlex

This symposium details the state of the elder care workforce around the world. Reports from IAGG regional chairs indicate that while each region has specific needs and concerns, all uniformly report insufficient numbers of aging experts and the lack of available training to meet current and increasing demands. Our African regional chair, Isabella Aboderin, reports that while Sub Saharan Africa is among the youngest regions in the world, they are experiencing a widespread increase in the number of elders and have few specialized experts or training centers to prepare skilled professional to meet their needs. Latin America is facing a 47% increase in its aging and disabled population according to regional chair, Marianela Hekman. Although there is an impressive increase in the number of professionals interested in aging, Latin America faces a serious deficiency in educational programs targeting aging and looks to IAGG for guidance. North American regional chair, Kenneth Madden, provides insights concerning the aging professional work force in Canada where they have made concerted efforts to increase the number of aging professionals, but struggle with a very uneven distribution of resources. For example, one entire province may have one geriatric specialist while a city in another province has many. Finally, Clemens Tesch-Roemer reports from the European region and elaborates on their struggles with the future of informal and formal care and the education of elder care workforce. In short, each region offers insights into the world-wide struggle to prepare to meet the needs of our aging population.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.327
Teacher spread0.294 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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