Evidence map›Paper›PMID 38116299›Full record

ArticleSAGE open medicine2023

Lessons learned on data collection for a digital health intervention-insights and challenges from Nigeria.

Edidiong Umoh, Chimaobi Isiguzo, Christie Akwaowo, Kingsley Attai, Nnette Ekpenyong, Humphrey Sabi, Emem Dan, Nwokoro Obinna, Faith-Michael Uzoka

Abstract read
In one paragraph

Article in SAGE open medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Edidiong UmohDepartment of Fisheries and Aquatic Environmental Management, Faculty of Agriculture, University of Uyo, University of Uyo Teaching Hospital, Uyo, Nigeria.ORCID https://orcid.org/0000-0002-1035-4879
Chimaobi IsiguzoDepartment of Surgery, Federal Medical Centre Owerri, Owerri, Nigeria.
Christie AkwaowoCommunity Health Department, University of Uyo, University of Uyo Teaching Hospital, Uyo, Nigeria.
Kingsley AttaiDepartment of Mathematics and Computer Science, Ritman University, Ikot Ekpene, Nigeria.
Nnette EkpenyongCommunity Health Department, University of Calabar, Calabar, Nigeria.
Humphrey SabiICT Department, The ICT University, Yaounde, Cameroon.
Emem DanUniversity of Uyo Teaching Hospital, Uyo, Nigeria.
Nwokoro ObinnaDepartment of Computer Science, University of Uyo, Uyo, Nigeria.
Faith-Michael UzokaDepartment of Mathematics and Computing, Mount Royal University, Calgary, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This article delves into the challenges of medical data collection during the COVID-19 pandemic in developing countries, using Nigeria as a case study. It emphasizes how data collection impacts research quality, reliability, and validity. Methods: Qualitative research utilizing purposive sampling was employed to explore experiences in designing a diagnostic tool for febrile diseases in Nigeria. A questionnaire with selectable and open-ended questions was utilized for data collection, and 23 respondents participated. Results: Among 74 potential participants, 23 valid responses were gathered, revealing significant themes related to experiences and challenges in medical data collection. A multidisciplinary team approach proved beneficial, fostering collaboration, enhancing knowledge, and promoting positive experiences. Despite challenges with paper questionnaires, most participants preferred them for ease of use. Connectivity issues hindered timely data uploading and disrupted virtual meetings. Conclusion: Innovative and flexible strategies, such as a blended data collection approach and well-coordinated teams, were vital in overcoming challenges. Electronic data collection tools, reminders, and effective communication played key roles, leading to positive outcomes. This study provides valuable insights for researchers and practitioners involved in data collection, particularly in developing countries like Nigeria.

Indexed as

challengesdata collectionDigital health interventionexperienceinsightsqualitative research

Identifiers

PMID38116299
PMCPMC10729616

What OpenQuestion holds

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LicenceCC BY-NC
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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.