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Record W4385442209 · doi:10.1177/07334648231191667

Healthcare Utilization and Advance Care Planning among Older Adults Across Cognitive Levels

2023· article· en· W4385442209 on OpenAlexaff
Zahra Rahemi, Ayse Malatyali, Juanita-Dawne Bacsu, Justine S. Sefcik, Darina Petrovsky, Zachary G. Baker, Kris Pui Kwan, Matthew Lee Smith, Swann Arp Adams

Bibliographic record

VenueJournal of Applied Gerontology · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsThompson Rivers University
FundersNational Institute of Nursing ResearchNational Institute on AgingNational Institutes of HealthClemson University
KeywordsDementiaCognitionHealth careGerontologyLogistic regressionMedicineAdvance care planningNursing homesDescriptive statisticsNursingDiseasePsychiatryPalliative care

Abstract

fetched live from OpenAlex

This study examined the impact of advance care planning (ACP) on healthcare utilization among older adults with normal cognition and impaired cognition/dementia. Using datasets from the Health and Retirement Study, we conducted a cross-sectional study on 17,698 participants aged 51 years and older. Our analyses included survey descriptive and logistic regression procedures. ACP measures included a living will and durable power of attorney for healthcare. Healthcare utilization was measured using the days spent in hospitals, hospice care, nursing homes, and home care. Of the participants, 77.8% had normal cognition, and 22% had impaired cognition/dementia. The proportion of impaired cognition/dementia was higher among racially minoritized participants, single/widowed participants, and those who lived alone and were less educated. The results showed that having an ACP was associated with longer stays in hospitals, nursing homes, and home healthcare in all participants.

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)0.000
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 score0.255
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.182
GPT teacher head0.476
Teacher spread0.295 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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