Evidence map›Paper›PMID 30186070›Full record

ReviewHong Kong journal of occupational therapy : HKJOT2017

An Ecological Framework to Support Small-Scale Shared Housing for Persons with Neurocognitive Disorders of the Alzheimer's and Related Types: A Literature Review.

Kevin N Bortnick

Abstract readReview
In one paragraph

Review in Hong Kong journal of occupational therapy : HKJOT, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

1 author.

Kevin N BortnickDepartment of Occupational Therapy, University of St. Augustine for Health Sciences, St. Augustine, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSmall scale shared housing arrangements (SHAs) is a deinstitutionalized model of care designed to resemble a typical home of <10 people and are increasingly available for persons with neurocognitive disorders of the Alzheimer's and related types (NCD). However, there is little aggregate evidence of their effect on persons with NCD thus, a literature review was performed.

methodsDatabase searches were conducted across CINAHL Complete, OTseeker, PubMed, Ovid, Academic One File, ProQuest Nursing and Allied Health, the World Wide Web and Google Scholar using the several key words that included neurocognitive disorders, Alzheimer's, dementia, quality of life, well-being, occupational performance, activities of daily living, small scale shared housing, sheltered housing and group homes. Bibliographic references from final articles were also examined. Selection criteria involved three steps: screening perspective articles by title and abstract, assessing full text for eligibility and finally, reviewing full-texts.

results16 studies were selected for final review where most found the association of SHAs with various occupational performance indicators unique to the NCD population better than or equal to controls (traditional models of long term care). A small minority of studies had mixed or inconclusive results. No study found SHAs necessarily worse than controls.

conclusionThe SHA model has many benefits for person's with NCD and may be especially advantageous for those in the early stages of the disease process. The occupational therapy profession should continue to raise awareness of SHAs and consider ecological theory as a valid basis for their expansion.

Indexed as

Alzheimer's Diseasequality of lifeshared housing

Identifiers

PMID30186070
PMCPMC6091999

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.