Evidence map›Paper›PMID 36251646›Full record

ArticlePloS one2022

Expert-generated standard practice elements for evidence-based home visiting programs using a Delphi process.

Emily E Haroz, Allison Ingalls, Karla Decker Sorby, Mary Dozier, Miranda P Kaye, Michelle Sarche, Lauren H Supplee, Daniel J Whitaker, Fiona Grubin, Deborah Daro

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.8field-weighted citation impact, top 20% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

10 authors at 8 institutions in 1 country.

Emily E HarozCenter for American Indian Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Allison IngallsCenter for American Indian Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.ORCID 0000-0002-3255-7351
Karla Decker SorbyMinnesota Department of Health, Family Home Visiting, Bemidji, MN, United States of America.
Mary DozierDepartment of Psychology, University of Delaware, Newark, DE, United States of America.
Miranda P KayePennsylvania State University, State College, PA, United States of America.
Michelle SarcheColorado School of Public Health, Centers for American Indian & Alaska Native Health, Aurora, CO, United States of America.
Lauren H SuppleeWilliam T. Grant Foundation, New York, NY, United States of America.ORCID 0000-0003-3766-3441
Daniel J WhitakerSchool of Public Health, Georgia State University, Atlanta, GA, United States of America.
Fiona GrubinCenter for American Indian Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Deborah DaroChapin Hall, University of Chicago, Chicago, IL, United States of America.
Johns Hopkins University · USColorado School of Public Health · USGeorgia State University · USMinnesota Department of Health · USPennsylvania State University · USUniversity of Chicago · USUniversity of Delaware · USWilliam Thomas Grant Foundation · US

Funding

SuSt-AInS: Sustainment of suicide prevention programs in American Indian SettingsK01MH116335 · NIMH · JOHNS HOPKINS UNIVERSITY · PI HAROZ, EMILY · 2018 to 2022
$733k
NIMH NIH HHS K01 MH116335
6 · The paper itself

Abstract

backgroundStates, territories, non-profits, and tribes are eligible to obtain federal funding to implement federally endorsed evidence-based home visiting programs. This represents a massive success in translational science, with $400 million a year allocated to these implementation efforts. This legislation also requires that 3% of this annual funding be allocated to tribal entities implementing home visiting in their communities. However, implementing stakeholders face challenges with selecting which program is best for their desired outcomes and context. Moreover, recent reviews have indicated that when implemented in practice and delivered at scale, many evidence-based home visiting programs fail to replicate the retention rates and effects achieved during clinical trials. To inform program implementers and better identify the active ingredients in home visiting programs that drive significant impacts, we aimed to develop an expert derived consensus taxonomy on the elements used in home visiting practice that are essential to priority outcome domains.

methodsWe convened a panel of 16 experts representing researchers, model representatives, and program implementers using a Delphi approach. We first elicited standard practice elements (SPEs) using open-ended inquiry, then compared these elements to behavior change techniques (BCTs) given their general importance in the field of home visiting; and finally rated their importance to 10 outcome domains.

resultsOur process identified 48 SPEs derived from the panel, with 83 additional BCTs added based on the literature. Six SPEs, mostly related to home visitor characteristics and skills, were rated essential across all outcome domains. Fifty-three of the 83 BCTs were rated unnecessary across all outcome domains.

conclusionsThis work represents the first step in a consensus-grounded taxonomy of techniques and strategies necessary for home visiting programs and provides a framework for future hypothesis testing and replication studies.

Indexed as

House CallsPostnatal CareBehavior TherapyFemaleHumansIndigenous PeoplesPregnancy

Identifiers

PMID36251646
PMCPMC9576067
OpenAlexW4306630413

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.