Evidence map›Paper›PMID 30054095›Full record

ArticleGeriatric nursing (New York, N.Y.)

Identifying potential long-stay residents in veterans health administration nursing homes.

Whitney L Mills, Jun Ying, Mark E Kunik

Open access · greenAbstract read
In one paragraph

Article in Geriatric nursing (New York, N.Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Whitney L MillsCenter for Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, 2450 Holcombe Blvd, Houston, TX 77030, USA; Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA. Electronic address: whitney.mills@va.gov.
Jun YingCenter for Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, 2450 Holcombe Blvd, Houston, TX 77030, USA. Electronic address: jun.ying@uth.tmc.edu.
Mark E KunikCenter for Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, 2450 Holcombe Blvd, Houston, TX 77030, USA; Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA. Electronic address: mkunik@bcm.edu.
Michael E. DeBakey VA Medical Center · US

Funding

RRD VA IK2 RX001241
6 · The paper itself

Abstract

Veterans Affairs nursing homes (Community Living Centers; CLCs) have largely shifted focus to providing short-term rehabilitative care, preferring longer-term care to be provided in contract nursing homes or at home. The goal of this retrospective cohort study is to identify resident characteristics associated with longer length of stay using the CLC Minimum Data Set (n = 35,114). Length of stay was defined as three groups: short (<90 days), moderate (91-365 days), and long (beyond 365 days). Residents who remained beyond 90 days were more likely to be black and to have a diagnosis of Alzheimer's disease, dementia, or schizophrenia, lived in another facility prior to admission, had a financial power of attorney, and had greater dependence in activities of daily living. Unique predictors of those who remained beyond 365 days were older age, cancer diagnosis, cognitive impairment, and admission from assisted living. Our findings can help CLC staff with admission decisions.

Indexed as

Long-Term CareAgedAge FactorsCognitive DysfunctionFemaleHumansLength of StayMaleNursing HomesRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsVeteransLength of stayNursing homePerson-environment fitTransitions

Identifiers

PMID30054095
PMCPMC9477184
OpenAlexW2883633197

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.