African Journal of Empirical Research

Depression is a leading cause of illness and disability among adolescents worldwide, yet evidence on its burden among school-going adolescents in rural Kenyan counties remains limited. This study determined the prevalence and socio-demographic factors of depression among secondary school students in Bungoma County, Kenya, to provide baseline evidence for mental health planning. The study was guided by Hopelessness Theory of Depression and Ecological Systems Theory. An analytical mixed methods cross-sectional study design was adopted. The target population consisted of secondary school students, with a sample size of 400 respondents determined using Taro Yamane formula. The respondents were selected through multistage stratified and systematic sampling from three sub-counties of Bungoma County (Mt. Elgon, Kimilili and Bungoma South). Quantitative data were collected using the Patient Health Questionnaire (PHQ-9) and a structured questionnaire, and analyzed using descriptive statistics and ordinal logistic regression in SPSS version 28. Qualitative data were obtained through ten Focus group discussions with students and twelve in-depth interviews with class teachers, and analyzed thematically. The overall prevalence of depressive symptoms (PHQ-9 score ≥ 5) was 55.5% (n = 222), 41.5% (n = 166) had mild depression, 9.5% (n = 38) moderate depression, 3.5% (n = 14) moderately severe depression and 1.0% (n = 4) severe depression, while 44.5% (n = 178) were not depressed. Female students were found to be almost twice as likely to experience higher levels of depression compared to the males (AOR= 1.71, P= 0.04). On the other hand, students in mixed day and boarding schools were 78% less likely to experience higher level of depression, (AOR = 0.22, P<0.001), compared to those in girls’ boarding and day schools. Further, students who live with extended family were more than twice as likely as those with both parents to experience higher depression levels (AOR = 2.10, P= 0.04). The qualitative data pointed to gender and family structure as possible associations of depression. Depression is highly prevalent among secondary school students in Bungoma County, with one in seven students experiencing moderate to severe depression. Routine school-based mental health screening using validated tools such as the PHQ-9 is recommended to enable early identification, counselling and referral of affected students.

Access to functional digital infrastructure is a prerequisite for teachers to convert information and communication Technology (ICT) competence into classroom practice, yet the quality and distribution of infrastructure remain uneven in many developing education systems. This paper examined the effect of the status of institutional ICT infrastructure and digital tools on teacher use of digital technologies in public junior schools (JS) in Kakamega County, Kenya. Relying on the Theory of Organizational Information Processing, the study used a convergent parallel mixed-methods design involving 332 responding JSS teachers, 11 Sub-County Directors of Education and observations from 78 schools. Factor analysis of 12 infrastructure indicators produced a factor-based index of 3.500, interpreted as good, although important differences were evident across infrastructure components. Electricity, computer rooms, broadband connectivity, and technical support were relatively strong, whereas specialized digital tools, suitable educational software and security mechanisms were weaker. Teachers most frequently identified inadequate computers/devices (26.8%) and unstable or absent internet connectivity (22.9%) as the leading infrastructure challenges. Pearson correlation showed a significant positive moderate relationship between infrastructure status and technology use (r = .417, p < .001). In Model 1, infrastructure significantly predicted technology use (B = .525, β approximately .417, p < .001; R² = .174). After adjustment for gender, age, education, teaching experience, subject and JSS enrolment, the detailed regression table reported B = .579, β = .465, t = 7.94, p < .001, R² = .473 and adjusted R² = .451. The null hypothesis was rejected. The paper concludes that functional infrastructure and appropriate digital tools are essential enabling conditions, but infrastructure development should address advanced tools, software, connectivity, maintenance and security rather than focusing only on basic provision. This study also recommends that the infrastructure budgets should move beyond hardware acquisition to include suitable educational software, specialized digital tools, maintenance, backups, antivirus protection and user-safety measures.