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dc.contributor.authorShiferaw biruk, Kirubel
dc.contributor.authorChakilu Tilahun, Binyam
dc.contributor.authorFikadie Endehabtu, Berhanu
dc.contributor.authorGullslett, Monika Knudsen
dc.contributor.authorMengiste, Shegaw Anagaw
dc.date.accessioned2021-03-17T12:45:09Z
dc.date.available2021-03-17T12:45:09Z
dc.date.created2020-08-21T14:35:29Z
dc.date.issued2020
dc.identifier.citationShiferaw, K. B., Tilahun, B. C., Endehabtu, B. F., Gullslett, M. K., & Mengiste, S. A. (2020). E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey. BMC medical informatics and decision making, 20(1).en_US
dc.identifier.issn1472-6947
dc.identifier.urihttps://hdl.handle.net/11250/2733912
dc.description.abstractBackground: Chronic patients persistently seek for health information on the internet for medication information seeking, nutrition, disease management, information regarding disease preventive actions and so on. Consumers ability to search, find, appraise and use health information from the internet is known as eHealth literacy skill. eHealth literacy is a congregate set of six basic skills (traditional literacy, health literacy, information literacy, scientific literacy, media literacy and computer literacy). The aim of this study was to assess eHealth literacy level and associated factors among internet user chronic patients in North-west Ethiopia. Methods: Institutional based cross-sectional study design was conducted. Stratified sampling technique was used to select 423 study participants among chronic patients. The eHealth literacy scale (eHEALS) was used for data collection. The eHEALS is a validated eight-item Likert scaled questionnaire used to asses self-reported capability of eHealth consumers to find, appraise, and use health related information from the internet to solve health problems. Statistical Package for Social science version 20 was used for data entry and further analysis. Multivariable logistic regression was used to examine the association between the eHealth literacy skill and associated factors. Significance was obtained at 95% CI and p < 0.05. Result: In total, 423 study subjects were approached and included in the study from February to May, 2019. The response rate to the survey was 95.3%. The majority of respondents 268 (66.3%) were males and mean age was 35.58 ± 14.8 years. The multivariable logistic regression model indicated that participants with higher education (at least having the diploma) are more likely to possess high eHealth literacy skill with Adjusted Odds Ratio (AOR): 3.48, 95% CI (1.54, 7.87). similarly, being government employee AOR: 1.71, 95% CI (1.11, 2.68), being urban resident AOR: 1.37, 95% CI (0.54, 3.49), perceived good health status AOR: 3.97, 95% CI (1.38, 11.38), having higher income AOR: 4.44, 95% CI (1.32, 14.86), Daily internet use AOR: 2.96, 95% CI (1.08, 6.76), having good knowledge about the availability and importance of online resources AOR: 3.12, 95% CI (1.61, 5.3), having positive attitude toward online resources AOR: 2.94, 95% CI (1.07, 3.52) and higher level of computer literacy AOR: 3.81, 95% CI (2.19, 6.61) were the predictors positively associated with higher eHealth literacy level. Conclusion: Besides the mounting indication of efficacy, the present data confirm that internet use and eHealth literacy level of chronic patients in this setting is relatively low which clearly implicate that there is a need to fill the skill gap in eHealth literacy among chronic patients which might help them in finding and evaluating relevant online sources for their health-related decisions.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleE-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional surveyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© The Author(s)en_US
dc.source.volume20en_US
dc.source.journalBMC Medical Informatics and Decision Makingen_US
dc.source.issue1en_US
dc.identifier.doihttps://doi.org/10.1186/s12911-020-01202-1
dc.identifier.cristin1824527
dc.source.articlenumber181en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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