Evidence map›Paper›PMID 33886625›Full record

ArticlePloS one2021

Healthcare providers' acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model.

Kirubel Biruk Shiferaw, Shegaw Anagaw Mengiste, Monika Knudsen Gullslett, Atinkut Alamirrew Zeleke, Binyam Tilahun, Tsion Tebeje, Robel Wondimu, Surafel Desalegn, Eden Abetu Mehari

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 1 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

56 citing papers in PubMed, 1 synthesis or guideline 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

9 authors.

Kirubel Biruk ShiferawHealth Informatics Department, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.ORCID 0000-0002-7411-1411
Shegaw Anagaw MengisteSchool of Business, Institute of Business, History & Social Sciences, University of South-Eastern Norway, Notodden, Norway.
Monika Knudsen GullslettFaculty of Health & Social Sciences, Science Center Health & Technology, University of South-Eastern Norway, Drammen, Norway.
Atinkut Alamirrew ZelekeMedical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Binyam TilahunDepartment of Health Informatics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
Tsion TebejeDepartment of Health Informatics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
Robel WondimuDepartment of Internal Medicine, Borre General Hospital, Borre, Ethiopia.
Surafel DesalegnDepartment of Emergency Medicine, School of Medicine, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
Eden Abetu MehariDepartment of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.ORCID 0000-0002-5779-5671

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn almost all lower and lower middle-income countries, the healthcare system is structured in the customary model of in-person or face to face model of care. With the current global COVID-19 pandemics, the usual health care service has been significantly altered in many aspects. Given the fragile health system and high number of immunocompromised populations in lower and lower-middle income countries, the economic impacts of COVID-19 are anticipated to be worse. In such scenarios, technological solutions like, Telemedicine which is defined as the delivery of healthcare service remotely using telecommunication technologies for exchange of medical information, diagnosis, consultation and treatment is critical. The aim of this study was to assess healthcare providers' acceptance and preferred modality of telemedicine and factors thereof among health professionals working in Ethiopia.

methodsA multi-centric online survey was conducted via social media platforms such as telegram channels, Facebook groups/pages and email during Jul 1- Sep 21, 2020. The questionnaire was adopted from previously validated model in low income setting. Internal consistency of items was assessed using Cronbach alpha (α), composite reliability (CR) and average variance extracted (AVE) to evaluate both discriminant and convergent validity of constructs. The extent of relationship among variables were evaluated by Structural equation modeling (SEM) using SPSS Amos version 23.

resultsFrom the expected 423 responses, 319 (75.4%) participants responded to the survey questionnaire during the data collection period. The majority of participants were male (78.1%), age <30 (76.8%) and had less than five years of work experience (78.1%). The structural model result confirmed the hypothesis "self-efficacy has a significant positive effect on effort expectancy" with a standardized coefficient estimate (β) of 0.76 and p-value <0.001. The result also indicated that self-efficacy, effort expectancy, performance expectancy, facilitating conditions and social influence have a significant direct effect on user's attitude toward using telemedicine. User's behavioral intention to use telemedicine was also influenced by effort expectancy and attitude. The model also ruled out that performance expectancy, facilitating conditions and social influence does not directly influence user's intention to use telemedicine. The squared multiple correlations (r2) value indicated that 57.1% of the variance in attitude toward using telemedicine and 63.6% of the variance in behavioral intention to use telemedicine is explained by the current structural model.

conclusionThis study found that effort expectancy and attitude were significantly predictors of healthcare professionals' acceptance of telemedicine. Attitude toward using telemedicine systems was also highly influenced by performance expectancy, self-efficacy and facilitating conditions. effort expectancy and attitude were also significant mediators in predicting users' acceptance of telemedicine. In addition, mHealth approach was the most preferred modality of telemedicine and this opens an opportunity to integrate telemedicine systems in the health system during and post pandemic health services in low-income countries.

Indexed as

COVID-19Health PersonnelTelemedicineAdultAttitude of Health PersonnelEthiopiaFemaleHumansMalePandemicsSelf EfficacySurveys and Questionnaires

Identifiers

PMID33886625
PMCPMC8061916

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.