Evidence mapPaperPMID 35978460Full record

ArticleNursing open2023

Technology-based innovative healthcare solutions for improving maternal and child health outcomes in low- and middle-income countries: A network meta-analysis protocol.

Md Obaidur Rahman, Noyuri Yamaji, Kiriko Sasayama, Daisuke Yoneoka, Erika Ota

Abstract read
In one paragraph

Article in Nursing open, 2023. 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
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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. 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

5 authors.

Md Obaidur RahmanDepartment of Global Health Nursing, Graduate School of Nursing Science, St. Luke's International University, Tokyo, Japan.ORCID 0000-0002-2219-3013
Noyuri YamajiDepartment of Global Health Nursing, Graduate School of Nursing Science, St. Luke's International University, Tokyo, Japan.ORCID 0000-0002-4212-6723
Kiriko SasayamaDepartment of Global Health Nursing, Graduate School of Nursing Science, St. Luke's International University, Tokyo, Japan.ORCID 0000-0003-2917-5536
Daisuke YoneokaCenter for Surveillance, Immunization, and Epidemiologic Research, National Institute of Infectious Diseases, Tokyo, Japan.ORCID 0000-0002-3525-5092
Erika OtaDepartment of Global Health Nursing, Graduate School of Nursing Science, St. Luke's International University, Tokyo, Japan.ORCID 0000-0002-3945-7441

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThe aims of the study were to synthesize the role of technology-based healthcare interventions (TBIs) and to identify the most effective interventions for the best functional maternal and child health (MCH) outcomes among low-risk pregnant women in low- and middle-income countries (LMICs).

designA systematic review and network meta-analysis (NMA).

methodsWe will perform a comprehensive search in electronic databases and other resources to identify relevant randomized controlled trials. Two reviewers will independently perform study selection, data extraction and quality assessment. Our primary outcomes include proportion of recommended antenatal care visits, skilled delivery care, postnatal care visits and exclusive breastfeeding practices. We will use pairwise random-effects meta-analysis and NMAs to estimate direct, indirect and relative effects using the relevant intervention classifications for each outcome separately. We plan to assess hierarchy of interventions, statistical inconsistency and certainty of evidence.

resultsThis review will compare the effectiveness of different form of TBIs on a comprehensive range of MCH outcomes and will provide the outcome-specific reliable evidence of the most effective interventions on improving MCH in LMICs. The review findings will guide researchers, stakeholders or policymakers on the potential use of TBIs in the given contexts that could achieve the best functional MCH outcomes in LMICs.

Indexed as

Developing CountriesOutcome Assessment, Health CareChildDelivery of Health CareFemaleHumansNetwork Meta-Analysis as TopicPregnancySystematic Reviews as TopicTechnologyantenatal caredelivery careexclusive breastfeedinghealthcare service utilizationLMICsmaternal and child healthnetwork meta-analysisperinatal carepostnatal caresystematic review

Identifiers

PMID35978460
PMCPMC9748107

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.