Association between e-health literacy and perceived importance of future pandemic preparedness in sub-saharan Africa
Bibliographic record
Abstract
INTRODUCTION: Emerging and re-emerging infectious diseases continue to pose a severe threat to public health in Sub-Saharan Africa (SSA) and globally. Community-related interventions, such as community e-Health literacy, can contribute to the preparedness to respond effectively to emerging and re-emerging infectious diseases. This study investigated the relationship between e-Health literacy and SSA countries' perceptions of the importance of readiness for potential pandemics. METHOD: This cross-sectional study was conducted in sub-Saharan African countries (Nigeria, Rwanda, Burundi, and South Africa) among adults aged 18 years and above between July 2020 and August 2021, respondents were recruited through a non-probability sampling technique. Participants were asked to self-report the perceived importance of 13 items on future pandemic preparedness scored on a 5 Likert-point scale. The four key dimensions of pandemic preparedness were online medical consultation, online courses, messaging for healthcare, and shopping. E-Health literacy was the key exposure. The questionnaire was adapted from a previously validated e-Health literacy scale. Data was collected through a self-administered questionnaire online. Data analysis was done using Stata and descriptive statistics including frequency, proportions, means, and standard deviation were used to summarize variables. Inferential statistics including chi-square and logistic regressions were used to test the significance of association between e-health literacy and pandemic preparedness setting the level of significance at 5%. RESULTS: A total of 1295 people participated in this study. Roughly half of all participants, 685 (52.90%), were aged between 18 and 29 and 685 (52.90%) were females. The standardised average (SE) e-Health literacy score was 29.55 (0.19). Shopping was perceived as the most important dimension of pandemic preparedness across participating countries (mean (SE) of 3.32 (0.06) and above across all countries for online shopping), while online medical consultation was the least perceived as important (mean (SE) of 2.88 (0.08) or less in two countries for instant health advice from chatbot). In the fully adjusted model, e-Health literacy was associated with 8 out of 13 items of the perceived importance of the pandemic preparedness questionnaire. Those include online consultation with doctors (OR = 1.11, 95% CI 1.02-1.21), telephone health advice (OR = 1.07, 95%CI 1.00-1.15), medicine delivery (OR = 1.04, 95% CI 1.03-1.06), getting medicine prescribed in a hospital visit/follow-up in a community pharmacy (OR = 1.07, 95% CI 1.05-1.10), receiving health information via email (OR = 1.08, 95% CI 1.01-1.17) and via social media (OR = 1.08, 95% CI 1.03-1.14), online shopping (OR = 1.07, 95% CI 1.03-1.11) and instant streaming courses (OR = 1.09, 95% CI 1.02-1.16). CONCLUSIONS: The higher e-Health literacy scores were associated with a higher perception of most elements as important in future pandemic readiness. Strengthening e-Health literacy can be a key element of the preparation for pandemics in SSA countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".