Evidence map›Paper›PMID 36968663›Full record

ArticleProcedia computer science2023

GISSA intelligent chatbot experience - How effective was the interaction between pregnant women and a chatbot during the COVID-19 pandemic?

Raquel Silveira, Cláudia Pernencar, Fábio de Sousa, Kamila Gomes, Deivith Oliveira, Inga Saboia, Cristiane Mourão, Odorico Monteiro, Ivana Barreto

Abstract read
In one paragraph

Article in Procedia computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Raquel SilveiraFundação-Oswaldo Cruz-Ceará.
Cláudia PernencarICNOVA-Instituto de Comunicação da NOVA, Faculdade de Ciências Sociais e Humanas da Universidade Nova de Lisboa, Lisboa, Portugal.
Fábio de SousaInstituto Federal de Educação do Ceará - IFCE.
Kamila GomesUniversidade Federal do Ceará.
Deivith OliveiraUniversidade Estadual do Ceará.
Inga SaboiaUniversidade Federal do Ceará - Instituto UFC Virtual.
Cristiane MourãoFundação-Oswaldo Cruz-Ceará.
Odorico MonteiroFundação-Oswaldo Cruz-Ceará.
Ivana BarretoFundação-Oswaldo Cruz-Ceará.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 increased the importance of patient's continuous assessment of health outcomes. In 2021 WHO proposed some Digital Health guidelines arguing that health systems should consider the use of emergent technologies in health care services. This health environment is providing intelligent systems to guide patients in self-care. One example of that is the chatbot which is, a conversational agent that have been assuming an important role in how to improve health knowledge, reducing the incidence of diseases and avoiding new ones. Pregnant women are a profile where the self-care referred before is a critical issue. Prenatal services reveal to be an important part of the care process where most complications for that women happen. This article aims to comprehend how pregnant women interact with a conversational agent and how relevant this Digital Health tool is for primary health care services. The study presents the process and results of a systematic literature review about the user experience with of a chatbot in pregnant women self-care context; a summary of GISSA intelligent chatbot development including the use of technologies such as DialogFlow; and the process and results of GISSA usability evaluation in research field. Results show that a small amount of articles was gathered and the chatbot as a tool is a relevant opportunity for primary care health services in Brasil.

Indexed as

ChatbotConversational AgentsCOVID-19DialogFlowDigital HealthNatural Language ProcessingPregnant womenUser Experience

Identifiers

PMID36968663
PMCPMC10030185

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.