Evidence map›Paper›PMID 27623807›Full record

ArticleBMC research notes2016

Explanatory models of adult patients with type 2 diabetes mellitus from urban centers of central Ethiopia.

Bruck M Habte, Tedla Kebede, Teferi G Fenta, Heather Boon

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Article in BMC research notes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

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20citing papers in PubMed, 2 pooled it
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Bruck M HabteSchool of Pharmacy, College of Health Sciences (CHS), Addis Ababa University (AAU), P. O. Box 1176, Addis Ababa, Ethiopia. bruck.messele@aau.edu.et.
Tedla KebedeSchool of Medicine, CHS, AAU, P. O. Box 1176, Addis Ababa, Ethiopia.
Teferi G FentaSchool of Pharmacy, College of Health Sciences (CHS), Addis Ababa University (AAU), P. O. Box 1176, Addis Ababa, Ethiopia.
Heather BoonLeslie Dan Faculty of Pharmacy, University of Toronto, 144 College Street (Room 514), Toronto, ON, M5S 3M2, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundType 2 diabetes, which is increasing as a public health problem in the low resource settings of Africa has been associated with the high prevalence of micro-vascular complications and increasing levels of macro-vascular complications. There is evidence from the developed world that understanding patient perceptions of chronic illness is important to design effective strategies for helping patients manage these conditions. This study utilized Kleinman's model to explore the illness perceptions of type 2 diabetes patients attending treatment in Addis Ababa and Butajira (Ethiopia) and better understand how they manage their illness.

designQualitative interviews were conducted to elicit the explanatory models of purposively sampled type 2 diabetes patients attending treatment in three hospitals in central Ethiopia until saturation of key emerging themes was achieved. Analysis of interview transcripts was guided by Kleinman's model.

resultsA total of 39 participants, 24 from Addis Ababa and the rest from Butajira took part in the study. This study revealed that patients' explanatory models were informed by both the traditional and biomedical models with emotional distress evident in some of the participants. The traditional model seemed to reflect the strong religious and cultural influences for the majority of study participants. The findings also revealed that symptoms played significant roles in how patients viewed their illness including assessment of its severity. Most were uncertain about the cause of their illness, with those expressing certainty citing factors over which they believed they had little or no control. This may have contributed to the perceptions about the use of religious healing and traditional medicines in a complementary or alternative manner to the biomedical regimen which could affect their adherence to recommended regimens and their health outcomes.

conclusionThis study suggests the need for a strong diabetes care program that is sensitive to patients' experiences of their illness including emotional distress. Individuals providing the diabetes care should consider local and individual contexts and strive to make their approach patient-centered and engage active participation of patients. There appears to be a need for better training of health providers in different areas including health communications and the fundamentals of mental healthcare.

Indexed as

Models, BiologicalAdultAgedDiabetes Mellitus, Type 2EthiopiaFemaleHumansMaleMiddle AgedUrban PopulationAddis AbabaButajiraEthiopiaExplanatory modelsKleinman’s modelQualitative researchType 2 diabetes

Identifiers

PMID27623807
PMCPMC5022171

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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.