Browsing by Author "Kuteesa, Job"

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    Quality of life of caregivers of patients diagnosed with severe mental illness at the national referral hospitals in Uganda.
    (BioMed Central, 2016) Ndikuno, Cynthia; Namutebi, Mariam; Mukunya, David; Olwit, Connie; Kuteesa, Job
    Background: Worldwide, 450 million people suffer from mental and behavioral disorders. In Uganda, it is estimated that 35% of the population that is 9,574,915 people suffer from some form of mental illness. Caregivers are increasingly bearing the responsibility of taking care of these patients, which can influence their QoL due to the social and economic costs they incur. The aim of the study was to assess the QoL of caregivers for patients diagnosed with severe mental illness attending the National Referral Hospitals in Uganda. Method: This was a cross sectional study. A pretested tool with two parts; a sociodemographic part and a validated WHOQOL-BREF, was used to collect data from 300 consecutive eligible participants. SPSS (Statistical Package for Social Sciences) Version 22 and Stata Version 14 were used in data entry and analysis. Results: Of the 300 participants, 57.3% of the caregivers had a poor QoL. The statistically significant factors associated with QoL were environment (Adjusted coefficient = 0.016, 95% CI = 0.009–0.023), caregiver satisfaction with their health (Adjusted coefficient = 0.405, 95% CI = 0.33–0.487), psychological wellbeing (Adjusted coefficient = 0.007, 95% CI = 0.0002–0.013), and education level (Adjusted coefficient = 0.148, 95% CI = 0.072–0.225). Conclusion: QoL of caregivers for patients diagnosed with mental illness is generally poor due to the added responsibilities and occupation of their time, energy and attention. This additional responsibility results in high levels of stress and caregivers may fail to have appropriate coping mechanisms. Interventions like support groups or counseling should be put in place to aid caregivers in their role and therefore improve QoL. This study adds to the international database of QoL literature and calls for more attention to be placed on caregivers in supporting their role and improving their QoL so as to lead to better patient outcomes among those diagnosed with mental illness. Keywords: Caregivers, Quality of life, Severe mental illness, WHOQOL-BREF, Uganda
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    Specialty career preferences among final year medical students at Makerere University College of health sciences, Uganda :
    (BMC, 2021) Kuteesa, Job; Musiime, Victor; Munabi, Ian G.; Mubuuke, Aloysius G.; Opoka, Robert; Mukunya, David; Kiguli, Sarah
    Background: Uganda has an imbalanced distribution of the health workforce, which may be influenced by the specialty career preferences of medical students. In spite of this, there is inadequate literature concerning the factors influencing specialty career preferences. We aimed to determine the specialty career preferences and the factors influencing the preferences among fifth year medical students in the School of Medicine, Makerere University College of Health Sciences (MakCHS). Methods: A sequential explanatory mixed methods study design with a descriptive cross-sectional study followed by a qualitative study was used. A total of 135 final year medical students in MakCHS were recruited using consecutive sampling. Self-administered questionnaires and three focus group discussions were conducted. Quantitative data was analysed in STATA version 13 (StataCorp, College Station, Tx, USA) using descriptive statistics, chi-square tests and logistic regression. Qualitative data was analysed in NVIVO version 12 (QRS International, Cambridge, MA) using content analysis. Results: Of 135 students 91 (67.4%) were male and their median age was 24 years (IQR: 24, 26). As a first choice, the most preferred specialty career was obstetrics and gynecology (34/135, 25.2%), followed by surgery (27/135, 20.0%), pediatrics (18/135, 13.3%) and internal medicine (17/135, 12.6%). Non-established specialties such as anesthesia and Ear Nose and Throat (ENT) were not selected as a first choice by any student. Female students had 63% less odds of selecting surgical related specialties compared to males (aOR = 0.37, 95%CI: 0.17–0.84). The focus group discussions highlighted controlled lifestyle, assurance of a good life through better financial remuneration and inspirational specialists as facilitators for specialty preference. Bad experience during the clinical rotations, lack of career guidance plus perceived poor and miserable specialists were highlighted as barriers to specialty preference. Conclusion: Obstetrics and Gynecology, Surgery, Pediatrics and Internal Medicine are well-established disciplines, which were dominantly preferred. Females were less likely to select surgical disciplines as a career choice. Therefore, there is a need to implement or establish career guidance and mentorship programs to attract students to the neglected disciplines. Keywords: Career, Medical, Preferences, Specialty, Student
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    Utilisation of ChatGPT and other Artificial Intelligence tools among medical faculty in Uganda : a cross-sectional study.
    (MedEdPublish, 2025) Mukunya, David; Nantale, Ritah; Kayemba, Frank; Ajalo, Elizabeth; Pangholi, Kennedy; Babuya, Jonathan; Akuu, Suzan Langoya; Namiiro, Amelia Margaret; Tweheyo, Ronald; Ekak, Steven; Nakitto, Brenda; Nantongo, Kirsten; Mpagi, Joseph Luwaga; Musaba, Milton W.; Oguttu, Faith; Kuteesa, Job; Mubuuke, Aloysius Gonzaga; Munabi, Ian Guyton; Kiguli, Sarah
    Background ChatGPT is a large language model that uses deep learning techniques to generate human-like texts. ChatGPT has the potential to revolutionize medical education as it acts as an interactive virtual tutor and personalized learning assistant. We assessed the use of ChatGPT and other Artificial Intelligence (AI) tools among medical faculty in Uganda. Methods We conducted a descriptive cross-sectional study among medical faculty at four public universities in Uganda from November to December 2023. Participants were recruited consecutively. We used a semi-structured questionnaire to collect data on participants’ sociodemographics and the use of AI tools such as ChatGPT. Our outcome variable was the use of ChatGPT and other AI tools. Data were analyzed in Stata version 17.0. Results We recruited 224 medical faculty, majority [75% (167/224)] were male. The median age (interquartile range) was 41 years (34–50). Almost all medical faculty [90% (202/224)] had ever heard of AI tools such as ChatGPT. Over 63% (120/224) of faculty had ever used AI tools. The most commonly used AI tools were ChatGPT (56.3%) and Quill Bot (7.1%). Fifty-six faculty use AI tools for research writing, 37 for summarizing information, 28 for proofreading work, and 28 for setting exams or assignments. Forty faculty use AI tools for nonacademic purposes like recreation and learning new skills. Faculty older than 50 years were 40% less likely to use AI tools compared to those aged 24 to 35 years (Adjusted Prevalence Ratio (aPR):0.60; 95% Confidence Interval (CI): [0.45, 0.80]). Conclusion The use of ChatGPT and other AI tools was high among medical faculty in Uganda. Older faculty (>50 years) were less likely to use AI tools compared to younger faculty. Training on AI use in education, formal policies, and guidelines are needed to adequately prepare medical faculty for the integration of AI in medical education. Keywords ChatGPT, medical faculty, Bing, Bard, Uganda, generative AI, medical education
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    Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda : a cross-sectional study.
    (PLOS One, 2025) Ajalo, Elizabeth; Mukunya, David; Nantale, Ritah; Kayemba, Frank; Pangholi, Kennedy; Babuya, Jonathan; Akuu, Suzan Langoya; Namiiro, Amelia Margaret; Nsubuga, Yakobo Baddokwaya; Mpagi, Joseph Luwaga; Musaba, Milton W.; Oguttu, Faith; Kuteesa, Job; Mubuuke, Aloysius Gonzaga; Munabi, Ian Guyton; Kiguli, Sarah
    Background Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, it has introduced new challenges and threats to medical education. We assessed the use of ChatGPT and other AI tools among medical students in Uganda. Methods We conducted a descriptive cross-sectional study among medical students at four public universities in Uganda from 1st November 2023 to 20th December 2023. Participants were recruited by stratified random sampling. We used a semi-structured questionnaire to collect data on participants’ socio-demographics and use of AI tools such as ChatGPT. Our outcome variable was use of AI tools. Data were analyzed descriptively in Stata version 17.0. We conducted a modified Poisson regression to explore the association between use of AI tools and various exposures. Results A total of 564 students participated. Almost all (93%) had heard about AI tools and more than two-thirds (75.7%) had ever used AI tools. Regarding the AI tools used, majority (72.2%) had ever used ChatGPT, followed by SnapChat AI (14.9%), Bing AI (11.5%), and Bard AI (6.9%). Most students use AI tools to complete assignments (55.5%), preparing for tutorials (39.9%), preparing for exams (34.8%) and research writing (24.8%). Students also reported the use of AI tools for nonacademic purposes including emotional support, recreation, and spiritual growth. Older students were 31% less likely to use AI tools compared to younger ones (Adjusted Prevalence Ratio (aPR):0.69; 95% CI: [0.62, 0.76]). Students at Makerere University were 66% more likely to use AI tools compared to students in Gulu University (aPR:1.66; 95% CI:[1.64, 1.69]). Conclusion The use of ChatGPT and other AI tools was widespread among medical students in Uganda. AI tools were used for both academic and non-academic purposes. Younger students were more likely to use AI tools compared to older students. There is a need to promote AI literacy in institutions to empower older students with essential skills for the digital age. Further, educators should assume students are using AI and adjust their way of teaching and setting exams to suit this new reality. Our research adds further evidence to existing voices calling for regulatory frameworks for AI in medical education.