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Record W2969298764 · doi:10.1177/2333721419870629

The Joint Impact of Age at Death and Dementia on Long-Term Care Use in the Last Years of Life: Changes From 1996 to 2013 in Finland

2019· article· en· W2969298764 on OpenAlexaff
Mari Aaltonen, Leena Forma, Jutta Pulkki, Jani Raitanen, Pekka Rissanen, Marja Jylhä

Bibliographic record

VenueGerontology and Geriatric Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of British Columbia
FundersAcademy of Finland
KeywordsDementiaGeeLong-term careGerontologyConcomitantDemographyGeneralized estimating equationMedicineDiseasePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Welfare states increasingly rely on aging in place policies and have cut back on institutional long-term care (LTC) provision. Simultaneously, the major determinants of LTC use, that is, dementia and living to very old age, are increasing. We investigated how increasing longevity and concomitant dementia were associated with changes in round-the-clock LTC use in the last 5 years of life between 1996 and 2013. Retrospective data drawn from national registers included all those who died aged 70+ in 2007 and 2013, plus a 40% random sample from 2001 ( N = 86,554). A generalized estimating equations (GEE) were used to estimate the association of dementia and age with LTC use during three study periods 1996-2001, 2002-2007, and 2008-2013. Between the study periods, the total number of days spent in LTC increased by around 2 months. Higher ages at death and the increased number of persons with dementia contributed to this increase. The group of the most frequent LTC users, that is, people aged 90+ with or without dementia, grew the most in size, yet their LTC use decreased. The implications of very old age and concomitant dementia for care needs must be acknowledged to guarantee an adequate quantity and quality of care.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.992

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 head0.057
GPT teacher head0.367
Teacher spread0.310 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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