Association between e-health literacy and perceived importance of future pandemic preparedness in sub-saharan Africa
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».