Evidence map›Paper›PMID 32222699›Full record

ArticleJMIR research protocols2020

Engaging Patients and Professionals to Evaluate the Seriousness of Maternal and Child Health Outcomes: Protocol for a Modified Delphi Study.

Lisa M Bodnar, Dmitry Khodyakov, Katherine P Himes, Jessica G Burke, Sara Parisi, Jennifer A Hutcheon

Abstract read
In one paragraph

Article in JMIR research protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  5. The impact of panel composition and topic on stakeholder perspectives: Generating hypotheses from online maternal and child health modified-Delphi panels.Health expectations : an international journal of public participation in health care and health policy · 2022
    Article
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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

6 authors.

Lisa M BodnarDepartment of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0001-9427-5467
Dmitry KhodyakovRAND Health Care, Santa Monica, CA, United States.ORCID https://orcid.org/0000-0002-2818-6906
Katherine P HimesDepartment of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-4949-2929
Jessica G BurkeDepartment of Behavioral and Community Health Sciences, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-0439-7653
Sara ParisiDepartment of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-7094-2912
Jennifer A HutcheonDepartment of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-6618-6018

Funding

Innovative approaches to inform evidence-based pregnancy weight gain guidelinesR01HD094777 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BODNAR, LISA M, HUTCHEON, JENNIFER ANNE · 2018 to 2021
$2.1M
NICHD NIH HHS R01 HD094777
6 · The paper itself

Abstract

backgroundMaternal weight gain during pregnancy is one of the few potentially modifiable risk factors for many adverse maternal and child health outcomes. Defining the optimal pregnancy weight gain range is difficult because, while lower weight gain may prevent some outcomes, such as maternal and child obesity, it may increase the risk of others such as fetal growth restriction and infant death. These health outcomes vary in their seriousness to mothers and their health care providers, and these differences in seriousness should be taken into account when determining optimal weight gain ranges. However, the relative seriousness that women and their care providers place on different health outcomes is unknown.

objectiveWe will determine the seriousness of 11 maternal and child health outcomes that have been consistently associated with pregnancy weight gain. We will achieve this by engaging patients and maternal and child health professionals using an online modified Delphi panel process.

methodsWe aim to recruit a racially/ethnically and geographically diverse group of 90 US maternal and child health professionals and 90 women who are pregnant or less than 2 years postpartum. We will conduct 3 concurrent panels using the ExpertLens system, a previously evaluated online modified Delphi system that combines 2 rounds of rating with 1 round of feedback and moderated online discussion. In Round 1, panelists are asked to rate the seriousness of each health outcome on a scale of 0-100 and to provide a rationale for their scores. In Round 2, panelists will review their responses relative to those of other panelists. They will discuss their seriousness ratings anonymously using a moderated online discussion board. In Round 3, participants will revise their Round 1 responses based on group feedback and discussion. Each round will be open for 1-2 weeks.

resultsThe study protocol was reviewed by our ethics boards and did not require approval as human research. A pilot study of 6 professionals and 7 patients was completed in December 2019.

conclusionsOur numeric estimates of the seriousness of maternal and child health outcomes will enable future studies to determine pregnancy weight gain ranges that balance the risks of low and high weight gain for mothers and children. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16478.

Indexed as

childrenDelphi methodExpertLensmothersonline stakeholder engagement panelspatient engagementpregnancy

Identifiers

PMID32222699
PMCPMC7298634

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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.