Low rates of actively contributing to the Social Health Insurance Fund (SHIF) under the Social Health Authority (SHA) could be due to health insurance knowledge inadequacies, possibly because of poor electronic health (eHealth) literacy. This study assesses whether eHealth literacy is associated with SHA/SHIF knowledge among undergraduate healthcare students in Kenya. An analytical cross-sectional study was conducted using the eHealth Literacy Scale (eHEALS) and an adapted Kaiser Family Foundation quiz. A total of 207 students in mainly six health-related academic programs in 21 institutions of higher learning in Kenya responded to the online survey. Only 54% and 21.7% of the participants had high (median ≥ 4 out of 5) levels of eHealth literacy and knowledge of SHA/SHIF, respectively. About 9.2% of the students had never heard of SHA/SHIF. Only high eHealth literacy compared to low eHealth literacy (OR = 6.2, p < 0.0001) and pursuing nursing, public health, and other programs compared to pursuing Bachelor of Medicine and Bachelor of Surgery (MBChB) (OR = 4.9–5.1, p = 0.01–0.03) significantly predicted SHA/SHIF knowledge. Thus, eHealth literacy levels and SHA/SHIF knowledge require improvement among undergraduate healthcare students in Kenya to prepare them as SHA ambassadors in their communities.

Background/Objectives: Strategies to improve the Social Health Authority (SHA)’s equity can be identified by analyzing the Kenya Demographic and Health Survey (KDHS) 2022. This study reports evidence of determinants of health insurance coverage in Kenya. Methods: Household- and individual-level datasets from the Kenya Demographic and Health Survey conducted between February and July 2022 were combined to form the analyzed dataset. Proportions of individuals with and without health insurance were estimated. The associations between potential determinants and health insurance status were calculated using the Rao–Scott chi-square. Logistic regression was used to analyze the determinants of health insurance coverage. Results: Most of the 14,232 participants were literate (75%), relatively poor (56%), in good health (79%), connected to electricity (55%), and radio listeners (61%). About 34% had health insurance, with 93% of the insured covered by the NHIF. Twenty predictors (Adjusted F = 4.2–434.1, p < 0.0001) were included in the complex sample logistic regression model, but only nine were statistically significant predictors of health insurance coverage. The key predictors were education level; wealth index; ownership of a solar panel, television, smartphone, and computer; age; and recent outpatient care (11–80% differences in odds). Conclusions: Health insurance coverage remains low in Kenya due to low education levels, poor economic status, and disparities in access to media. The SHA can emphasize media campaigns in the informal sector to increase premium payments. Accelerating socioeconomic advancement and adopting tax-based funding could speed up Kenya’s progress towards UHC.

Most TikTok users have low electronic health (eHealth) literacy. Yet, evidence on the availability, content, and engagement metrics of TikTok videos on eHealth literacy is scanty. This study analyzes the content and engagement metrics of recent TikTok videos on health literacy to identify the unmet eHealth literacy needs of TikTok users. A convergent mixed-methods study was conducted. The hashtag #eHealthliteracy was searched for videos published between January 2025 and February 2026. Engagement and content data were retrieved and analyzed statistically and thematically, respectively. Sixty of the 69 retrieved TikTok videos were shorter than 288 s. Favorite:like ratio was the only high engagement ratio at 15%; the rest were less than 4%. Only four of the 69 videos were generated using artificial intelligence, and their engagement metrics were the highest. Video lengths were negatively associated with all engagement metrics (β = −0.004 to −0.008, p < 0.001). Favorites, shares, and likes were significantly higher for educational videos compared to promotional videos (p = 0.0071, 0.0252, and 0.0413), with medium effect sizes of ε² = 0.1072, 0.0743, and 0.0617, respectively. Only six of the 69 videos were directly on eHealth literacy; the rest were on health literacy. The increasing availability of health information is not accompanied by eHealth literacy on TikTok. Future eHealth literacy videos should be short, institutional, AI-optimized, and embedded with health topics for better reach and engagement.

Background/Objectives: Nurse–mother communication is central to maternal participation in Neonatal Intensive Care Units (NICUs), yet high acuity and workflow rhythms can compress dialogue and weaken shared understanding. This study used Communication Accommodation Theory and the Transactional Model of Stress and Coping to explain multilevel drivers of communication barriers and to co-produce feasible improvement strategies. Methods: A dyadic qualitative design was conducted across four Level III NICUs. Data were triangulated from 37 semi-structured interviews (18 mothers and 19 nurses, recruited through purposive maximum-variation sampling), approximately 40 h of non-participant observation, and 12-unit documents. A team-based codebook thematic analysis was applied, integrating observational logs with interview and document data to refine patterns and mechanisms. Results: A context-produced pattern of interactional compression was identified. Mothers contributed 2 or fewer speaking turns in 21/30 logged bedside encounters and were present in 13/16 observed round episodes, speaking in 5/13 of those episodes. Interpretability and language access gaps were common: unexplained technical terms occurred in 24/46 logged interactions; teach-back prompts occurred in 7/18 education encounters; professional interpreters were present in 3/9 language-discordant events. Three participation configurations described coping-linked engagement: threat–compression (n = 8), convergence-to-coping (n = 6), and resource-scaffolded participation (n = 4). In co-production, stakeholders co-produced (i.e., collaboratively identified and prioritized) three mechanism-targeted changes: protected post-round question-and-answer time incorporating teach-back, standardized visual “mini-packs,” and 24/7 interpreter access. Conclusions: Nurse–mother communication in NICUs can be structurally compressed by workload rhythms and uneven interpretability supports. Co-produced organizational scaffolds may expand opportunities for accommodation, comprehension verification, and equitable maternal participation.

Low rates of actively contributing to the Social Health Insurance Fund (SHIF) under Social Health Authority (SHA) could be due to health insurance knowledge inadequacies possibly due to poor eHealth literacy. This study assesses whether eHealth literacy predicts SHA/SHIF knowledge among undergraduate students in health-related courses in Kenya. An analytical cross-sectional online survey was conducted using the eHealth Literacy Scale (eHEALS) and adapted Kaiser Family Foundation quiz to collect data. A total of 207 tertiary education students mainly pursuing six health courses in 21 institutions in Kenya responded to the survey. The median eHealth literacy score was 3.9/5, with about 54% having high eHealth literacy levels (median ≥ 4). About 9.2% of the students had never heard of SHA/SHIF while only 21.7% had high knowledge of SHA/SHIF (median ≥ 4), with the median knowledge score being 3/5. Only high eHealth literacy compared to low eHealth literacy (OR = 6.2, p<0.0001) and pursuing nursing, public health, and other courses compared to pursuing MBChB (OR = 4.9-5.1, p = 0.01-0.03) significantly predicted SHA/SHIF knowledge. Thus, health students in Kenya had modest eHealth literacy levels and SHA/SHIF knowledge. eHealth literacy was a significant predictor of SHA/SHIF knowledge, thus interventions can target it.