ArticleBMC medical education2022
Predictors for adoption of e-learning among health professional students during the COVID-19 lockdown in a private university in Uganda.
Article in BMC medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technology Acceptance Model in Medical Education: Systematic Review.JMIR medical education · 2025Pooled it
- Development and validation of a comprehensive tool to study the various elements influencing the utilization of E-learning among undergraduate health professions students.BMC medical education · 2025Article
- Adoption of online mathematics learning in Ugandan government universities during the COVID-19 pandemic: pre-service teachers' behavioural intention and challenges.Discover education · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundDuring the recent Coronavirus pandemic, many universities realized that the traditional delivery of educational content was not adequate in the context of imposed restrictions. Adoption of e-learning was one obvious way to foster continuity of learning. Despite its rapid implementation during the lockdown in Uganda, it was not known whether health professional students were willing to adopt e-learning as a way to foster continuity of learning. We, therefore, adopted a Technology Acceptance Model to determine the predictors for the adoption of e-learning using learner and information technology variables.
methodsA cross-sectional study among 109 health professional students ≥18 years of age at Clarke International University was conducted. Adoption of e-learning was measured as a self-report. Data were obtained using a smart survey and descriptively summarized. The differences in the study outcome were compared using the chi-square test. The factors that independently influenced the adoption of e-learning were determined using binary logistic regression and reported as adjusted odds ratios (aORs) with a 95% confidence interval (CI).
resultsOf the 109 respondents, 71 (65.1%) adopted e-learning. Our data showed low odds of adoption of e-learning among participants in first year (aOR, 0.34: 95%CI, 0.14-0.79), low e-learning expectations (aOR, 0.01: 95%CI, 0.01-0.34), no confidence in using IT devices (aOR, 0.16: 95%CI, 0.00-0.77), no prior experience in e-learning (aOR, 0.11: 95%CI, 0.02-0.68), not considering e-learning flexible (aOR, 0.25:95%CI, 0.08-0.86) and high cost of internet (aOR, 0.13: 95%CI, 0.02-0.84).
conclusionWe identified predictors of e-learning adoption which include having completed at least 1 year of study, high e-learning expectations, confidence in using IT devices, prior experience in e-learning, considering e-learning to be flexible and internet access. This information can be used by universities to enhance infrastructure and prepare potential e-learners.
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