Evidence map›Paper›PMID 28750083›Full record

ArticlePloS one2017

Health care workers' perceptions of point-of-care testing in a low-income country-A qualitative study in Southwestern Uganda.

Reza Rasti, Deborah Nanjebe, Jonas Karlström, Charles Muchunguzi, Juliet Mwanga-Amumpaire, Jesper Gantelius, Andreas Mårtensson, Lourdes Rivas, Francesc Galban, Philippa Reuterswärd and 4 more

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 3 pooled it
–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

29 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

14 authors.

Reza RastiDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0001-7816-8338
Deborah NanjebeEpicentre Mbarara Research Center, Mbarara, Uganda.
Jonas KarlströmUNICEF, Health Section, New York, United States of America.
Charles MuchunguziMbarara University of Science and Technology, Mbarara, Uganda.
Juliet Mwanga-AmumpaireEpicentre Mbarara Research Center, Mbarara, Uganda.
Jesper GanteliusDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
Andreas MårtenssonDepartment of Women's and Children's Health, International Maternal and Child Health (IMCH), Uppsala University, Uppsala, Sweden.
Lourdes RivasDivision of Proteomics and Nanobiotechnology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
Francesc GalbanDivision of Proteomics and Nanobiotechnology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
Philippa ReuterswärdDivision of Proteomics and Nanobiotechnology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
Helene Andersson SvahnDivision of Proteomics and Nanobiotechnology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
Helle M AlvessonDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
Yap BoumEpicentre Mbarara Research Center, Mbarara, Uganda.
Tobias AlfvénDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPoint-of-care (POC) tests have become increasingly available and more widely used in recent years. They have been of particular importance to low-income settings, enabling them with clinical capacities that had previously been limited. POC testing programs hold a great potential for significant improvement in low-income health systems. However, as most POC tests are developed in high-income countries, disengagement between developers and end-users inhibit their full potential. This study explores perceptions of POC test end-users in a low-income setting, aiming to support the development of novel POC tests for low-income countries.

methodsA qualitative study was conducted in Mbarara District, Southwestern Uganda, in October 2014. Fifty health care workers were included in seven focus groups, comprising midwives, laboratory technicians, clinical and medical officers, junior and senior nurses, and medical doctors. Discussions were audio-recorded and transcribed verbatim. Transcripts were coded through a data-driven approach for qualitative content analysis.

resultsNineteen different POC tests were identified as currently being in use. While participants displayed being widely accustomed to and appreciative of the use of POC tests, they also assessed the use and characteristics of current tests as imperfect. An ideal POC test was characterized as being adapted to local conditions, thoughtfully implemented in the specific health system, and capable of improving the care of patients. Tests for specific medical conditions were requested. Opinions differed with regard to the ideal distribution of POC tests in the local health system.

conclusionPOC tests are commonly used and greatly appreciated in this study setting. However, there are dissatisfactions with current POC tests and their use. To maximize benefit, stakeholders need to include end-user perspectives in the development and implementation of POC tests. Insights from this study will influence our ongoing efforts to develop POC tests that will be particularly usable in low-income settings.

Indexed as

Health Knowledge, Attitudes, PracticeHealth PersonnelPerceptionQualitative ResearchBiomarkersFocus GroupsHumansInterviews as TopicPatient CarePoint-of-Care TestingPovertyUgandaBiomarkers

Identifiers

PMID28750083
PMCPMC5547696

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.