3 research outputs found

    Influence of Teacher-Student Relationships on Students’ Loneliness in Coeducational and Single-Gender Public Secondary Schools in Kenya: a case of Murang’a County

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    The objective of this study was to establish the influence of teacher-student relationship on loneliness among secondary school students. The study was carried out in sub county public schools in Murang’a County, central region of Kenya. A cross sectional survey design was used. Stratified random sampling was used to get a sample of 592 participants from eight sub counties in Murang’a County. Loneliness was measured using Perth aloneness loneliness scale (PALs) while teacher-student relationship (TSR) was measured using ten statements with graded responses in a five point Likert scale developed for this study. The PAL and TSR scales together with personal data questions formed sections of self administered questionnaire. Administration of the questionnaire was done during normal school days by research assistants. The data was coded and analyzed using statistic program for social sciences (SPSS) version 20. Findings were that TSR was inversely and highly significantly related to loneliness. Regression analysis revealed that TSR predicts 16.2% of loneliness among students. The results are discussed in relation to implications in teacher training curriculum and loneliness counseling in schools. Key words: Teacher, Students,  Relationships, Loneliness. .Keny

    Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

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    Background In 2019, the World Health Organization recognised diabetes as a clinically and pathophysiologically heterogeneous set of related diseases. Little is currently known about the diabetes phenotypes in the population of low- and middle-income countries (LMICs), yet identifying their different risks and aetiology has great potential to guide the development of more effective, tailored prevention and treatment. Objectives This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied. Methods The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken. Results Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application. Conclusions Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems’ weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches