Evidence map›Paper›PMID 40230961›Full record

ArticleOxford open digital health2024

Mixed-methods, cross-sectional assessment of a client-facing, family planning counseling chatbot in Côte d'Ivoire.

Kristen M Little, Ndola Prata, Kpebo Djoukou Olga Denise, Nadia Tefouet, Jean Christophe Fotso, Alexandra Angel, Sarah Brittingham, Paul Bouanchaud, Amadou Donapoho Soro, Lisa Dulli

Abstract read
In one paragraph

Article in Oxford open digital health, 2024. 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. Trial
  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

10 authors.

Kristen M LittleStrategy and Insights Department, Population Services International (PSI), Washington, DC, USA.ORCID https://orcid.org/0009-0002-3261-8078
Ndola PrataReproductive, Maternal, Newborn, Child and Adolescent Health (RMNCAH), Evidence for Sustainable Human Development (EVIHDAF), Yaounde, Cameroon.
Kpebo Djoukou Olga DeniseUnit of Research in Maternal and Child Health, National Institute of Public Health, Abidjan, Côte d'Ivoire.
Nadia TefouetReproductive, Maternal, Newborn, Child and Adolescent Health (RMNCAH), Evidence for Sustainable Human Development (EVIHDAF), Yaounde, Cameroon.
Jean Christophe FotsoReproductive, Maternal, Newborn, Child and Adolescent Health (RMNCAH), Evidence for Sustainable Human Development (EVIHDAF), Yaounde, Cameroon.
Alexandra AngelSexual and Reproductive Health Department, PSI, Washington, DC, USA.
Sarah BrittinghamScientific, Technical and Evidence Advancement Department, FHI 360, Durham, NC, USA.
Paul BouanchaudStrategy and Insights Department, Population Services International (PSI), Washington, DC, USA.
Amadou Donapoho SoroStrategic Information Department, PSI/Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Lisa DulliGlobal Health and Population Research Department, FHI 360, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evidence is limited about the feasibility, acceptability and effects on family planning (FP) intentions of digital tools, such as chatbots. To help fill this gap, we assessed a chatbot in Côte d'Ivoire with three components: (i) menstrual health information; (ii) FP information and (iii) method recommendations and referrals. We conducted a cross-sectional study to describe user characteristics and perceptions of chatbot usability, content and relevance, and perceived effects on FP-related self-efficacy. We used a mixed-methods approach, including an online survey and in-depth interviews (IDI) with chatbot users, and chatbot back-end data. Qualitative data were analyzed thematically. Quantitative data were analyzed descriptively. Between 11 January 2022 and 15 July 2022, we recruited 280 chatbot users for the survey, of whom 28 completed an in-depth interview. Survey participants averaged 22 years old, were majority female (96%), resided in Abidjan (71%) and had some university education (61%). Less than half (48%) were currently using a method to delay/avoid pregnancy. Participants primarily accessed the chatbot to get information for themselves (73%) and felt it was 'easy' (29%) or 'very easy' to use (65%). Most respondents somewhat or strongly agreed that the information provided was accurate/reliable (97%) and was useful to them (96%). Respondents reported feeling more confident in discussing FP topics with their partners and providers after interacting with the chatbot. Our findings suggest that chatbots may be a feasible and acceptable approach to sharing tailored FP information with end-users in a West African context, especially for young women in urban areas.

Indexed as

chatbotcounselingdigitalfamily planningWest Africa

Identifiers

PMID40230961
PMCPMC11932393

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.