Evidence map›Paper›PMID 36626106›Full record

ArticleJournal of medical systems2023

Development and Validation of Conversational Agent to Pregnancy Safe-education.

João Luis Zeni Montenegro, Cristiano André da Costa, Rodrigo da Rosa Righi, Elson Romeu Farias, Lara Balen Matté

Abstract read
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In one paragraph

Article in Journal of medical systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

João Luis Zeni MontenegroSoftware Innovation Laboratory - SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, RS, 93022-000, Brazil.
Cristiano André da CostaSoftware Innovation Laboratory - SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, RS, 93022-000, Brazil. cac@unisinos.br.ORCID http://orcid.org/0000-0003-3859-6199
Rodrigo da Rosa RighiSoftware Innovation Laboratory - SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, RS, 93022-000, Brazil.
Elson Romeu FariasSchool of Public Health of the State Health Secretariat of RS, Universidade Luterana do Brasil - Ulbra, Canoas, Brazil.
Lara Balen MattéSoftware Innovation Laboratory - SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, RS, 93022-000, Brazil.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (BR) 405354/2016-9 and 303640/2017-0Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (BR) Financial Code 001
6 · The paper itself

Abstract

Pregnant women constantly need some information to support nutritional decisions during pregnancy, and many do not receive such assistance at all. This study aims to present a conversational agent to provide reliable information to pregnant women, focusing on nutritional education and evaluating the perception of pregnant women and health professionals about the agent. As a scientific contribution, this article developed and implemented a conversational agent in a real environment capable of generating reliable responses on the basis of a set of health documents. We proposed an intervention study with 25 women and 10 healthcare providers through a survey to measure the perceptions of these groups towards conversational agents. The results show that the intended design could ensure positive support for pregnant women, clarify certain issues for the public, and remove some knowledge barriers. The results showed no significant difference between the groups (p-value = 0.713). Depending on the perception of the pregnant group, the conversational agent model can teach new knowledge during the prenatal period (Mean = 4.56). The model presented for health professionals could already be indicated as a support tool for pregnant women (Mean = 4.7).

Indexed as

CommunicationPregnant PeopleFemaleHumansPregnancySurveys and QuestionnairesConversational agentHealth literacyInformation retrievalMachine learningNatural language processingPatient facing systems

Identifiers

PMID36626106

What OpenQuestion holds

Textmetadata
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.