Evidence map›Paper›PMID 39108898›Full record

ArticleHeliyon2024

Exploring the barriers to feminine healthcare access among marginalized women in Bangladesh and facilitating access through a voice bot.

Sreya Sanyal Puja, Nahian Noor Neha, Ofia Rahman Alif, Tarannaum Jahan Sultan, Md Golam Zel Asmaul Husna, Ishrat Jahan, Jannatun Noor

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. 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

7 authors.

Sreya Sanyal PujaComputing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Nahian Noor NehaComputing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Ofia Rahman AlifComputing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Tarannaum Jahan SultanComputing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Md Golam Zel Asmaul HusnaNational Institute of Cancer Research and Hospital, Dhaka, Bangladesh.
Ishrat JahanNext-generation Computing (NeC) Research Group, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh.
Jannatun NoorComputing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In today's digital age, ensuring high-quality healthcare services is paramount for both physical and mental well-being. This is particularly challenging in developing countries like Bangladesh, where marginalized individuals, especially women, face barriers to accessing essential healthcare. Financial constraints and limited awareness often hinder their ability to avail themselves of private or public healthcare services. Besides, women have distinct healthcare difficulties compared to men, and they are also more likely to catch certain illnesses and diseases. However, there is a noticeable gap in the existing literature, with insufficient research specifically dedicated to addressing the healthcare challenges faced by this underprivileged group of women in developing countries. In this study, we delve deeper into the healthcare challenges faced by underprivileged women in Bangladesh, including issues related to menstruation and gynecological disorders through a field study in a slum area. Drawing valuable insights from this field study, we propose leveraging voice bots in the telecommunications sector as an innovative solution to provide accessible and targeted healthcare support for marginalized women. We present the prototype of such a voice bot to be available 24/7 with detailed workflow and performance evaluation. We expect that our study will contribute to the ICTD scholarship by informing future interventions for economically disadvantaged communities as well as informing policymakers grappling with the healthcare challenges faced by underprivileged women.

Indexed as

Artificial intelligenceFuzzy string matchingGynecological healthcareHealthcareHuman-computer interaction (HCI)Menstrual healthcareVoice botWomen's healthcare

Identifiers

PMID39108898
PMCPMC11301159

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.