References
Aigberua, A. O., Izah, S. C., & Aigberua, A. A. (2023). Occurrence, source delineation, and health hazard of polycyclic aromatic hydrocarbons in tissues of Sarotherodon melanotheron and Chrysichthys nigrodigitatus from Okulu River, Nigeria. Environmental Monitoring and Assessment, 195(3), 364. https://doi.org/10.1007/s10661-023-10171-3
Amir, P. N., Sazali, M. F., Salvaraji, L., Dulajis, N., Rahim, S. S. S. A., & Avoi, R. (2021). Public health informatics in global health surveillance: A review. Borneo Epidemiology Journal, 2(2), 74-88. https://doi.org/10.51200/bej.v2i2.3628
Ayorinde, A., Grove, A., Ghosh, I., Harlock, J., Meehan, E., Tyldesley-Marshall, N., Briggs, A., Clarke, A., & Al-Khudairy, L. (2024). What is the best way to evaluate social prescribing? A qualitative feasibility assessment for a national impact evaluation study in England. Journal of Health Services Research & Policy, 29(2), 111-121. https://doi.org/10.1177/13558196231212854
Bekker, C. L., Bossina, S., de Vera, M. A., Bartlett, S. J., de Wit, M., March, L., Shea, B., Evans, V., Richards, D., Tong, A., et al. (2021). Patient perspectives on outcome domains of medication adherence trials in inflammatory arthritis: An international OMERACT focus group study. The Journal of Rheumatology, 48(9), 1480-1487. https://doi.org/10.3899/jrheum.201568
Bernardo, T. M., Rajic, A., Young, I., Robiadek, K., Pham, M. T., & Funk, J. A. (2013). Scoping review on search queries and social media for disease surveillance: A chronology of innovation. Journal of Medical Internet Research, 15(7), e2740. https://doi.org/10.2196/jmir.2740
Biancovilli, P., Makszin, L., & Jurberg, C. (2021). Misinformation on social networks during the novel coronavirus pandemic: A quali-quantitative case study of Brazil. BMC Public Health, 21(1), 1200. https://doi.org/10.1186/s12889-021-11165-1
Bigdeli, M., Jacobs, B., Tomson, G., Laing, R., Ghaffar, A., Dujardin, B., & Van Damme, W. (2013). Access to medicines from a health system perspective. Health Policy and Planning, 28(7), 692-704. https://doi.org/10.1093/heapol/czs108
Cornell, P. Y., Halladay, C. W., Montano, A. R., Celardo, C., Chmelka, G., Silva, J. W., & Rudolph, J. L. (2023b). Social work staffing and use of palliative care among recently hospitalized veterans. JAMA Network Open, 6(1), e2249731. https://doi.org/10.1001/jamanetworkopen.2022.49731
Cornell, P. Y., Hua, C. L., Halladay, C. W., Halaszynski, J., Harmon, A., Koget, J., & Silva, J. W. (2023a). Benefits and challenges in the use of RE-AIM for evaluation of a national social work staffing program in the Veterans Health Administration. Frontiers in Health Services, 3, 1225829. https://doi.org/10.3389/frhs.2023.1225829
Coşkun, Ö., Timurçin, U., Kıyak, Y. S., & Budakoğlu, I. İ. (2023). Validation of IFMSA social accountability assessment tool: Exploratory and confirmatory factor analysis. BMC Medical Education, 23(1), 138. https://doi.org/10.1186/s12909-023-04121-7
Cummins, J. A., & Lipworth, A. D. (2023). Reddit and Google activity related to non-COVID epidemic diseases surged at start of COVID-19 pandemic: Retrospective study. JMIR Formative Research, 7(1), e44603. https://doi.org/10.2196/44603
Davidson, K. W., Krist, A. H., Tseng, C. W., Simon, M., Doubeni, C. A., Kemper, A. R., Kubik, M., Ngo-Metzger, Q., Mills, J., & Borsky, A. (2021). Incorporation of social risk in US Preventive Services Task Force recommendations and identification of key challenges for primary care. JAMA, 326(14), 1410-1415. https://doi.org/10.1001/jama.2021.12833
Du, L., & Pang, Y. (2021). A novel data-driven methodology for influenza outbreak detection and prediction. Scientific Reports, 11(1), 13275. https://doi.org/10.2139/ssrn.3655520
Eizaguirre, M. B., Vanotti, S., Merino, Á., Yastremiz, C., Silva, B., Alonso, R., & Garcea, O. (2018). The role of information processing speed in clinical and social support variables of patients with multiple sclerosis. Journal of Clinical Neurology, 14(4), 472-477. https://doi.org/10.3988/jcn.2018.14.4.472
Gomory, T. (2021). Evaluating social work clinical practice in the real world: Feedback informed treatment. Szociális Szemle, 14(1), 2-9. https://doi.org/10.15170/socrev.2021.14.01.01
Graves, N., Barnett, A. G., Burn, E., & Cook, D. (2018). Smaller clinical trials for decision-making: Using p-values could be costly. F1000Research, 7, 1176. https://doi.org/10.12688/f1000research.15522.1
Habbema, D. (2018). Statistical analysis and decision making in cancer screening. European Journal of Epidemiology, 33(5), 433-435. https://doi.org/10.1007/s10654-018-0406-8
Harris, J. K., Hawkins, J. B., Nguyen, L., Nsoesie, E. O., Tuli, G., Mansour, R., & Brownstein, J. S. (2017). Using Twitter to identify and respond to food poisoning: The food safety STL project. Journal of Public Health Management and Practice, 23(6), 577-580. https://doi.org/10.1097/phh.0000000000000516
Hemenway, A. N., Meyer‐Junco, L., Zobeck, B., & Pop, M. (2022). Utilizing social and behavioral change methods in clinical pharmacy initiatives. Journal of the American College of Clinical Pharmacy, 5(4), 450-458. https://doi.org/10.1002/jac5.1593
Heriana, C. (2023). Analysis of receiving surveillance information system for public health centre (SISPHEC. ID Application) using technology acceptance model (TAM) at Kuningan District, Indonesia. Medical Technology and Public Health Journal, 7(1), 89-97. https://doi.org/10.33086/mtphj.v7i1.4097
Hickson, D. A., Waller, L. A., Gebreab, S. Y., Wyatt, S. B., Kelly, J., Antoine-Lavigne, D., & Sarpong, D. F. (2011). Geographic representation of the Jackson Heart Study cohort to the African-American population in Jackson, Mississippi. American Journal of Epidemiology, 173(1), 110-117. https://doi.org/10.1093/aje/kwq317
Hsieh, W. C., Chuang, T. H., & Wen, H. H. (2023). Taiwan's medical social work development and the impact of social work regulation. Research on Social Work Practice, 33(1), 41-51. https://doi.org/10.1177/10497315221127036
Izah, S. C., Richard, G., Stanley, H. O., Sawyer, W. E., Ogwu, M. C., & Uwaeme, O. R. (2024a). Potential applications of linear regression models in studying the relationship between fish and contaminants in their environment: One health perspective. Juniper Online Journal of Public Health, 8(4), 555743. https://doi.org/10.19080/JOJPH.2024.08.555743
Izah, S. C., Richard, G., Stanley, H. O., Sawyer, W. E., Ogwu, M. C., & Uwaeme, O. R. (2024b). Prospects and application of multivariate and reliability analyses to one health risk assessments of toxic elements. Toxicology and Environmental Health Sciences, 16(2), 127-134.
Izah, S. C., Stanley, H. O., Richard, G., Sawyer, W. E., & Uwaeme, O. R. (2024c). Environmental health risks of trace elements in sediment using multivariate approaches and contamination indices. International Journal of Environmental Science and Technology. https://doi.org/10.1007/s13762-024-05974-1
Izah, S. C., Stanley, H. O., Richard, G., Sawyer, W. E., Uwaeme, O. R., & Sylva, L. (2024d). Surface water quality: A statistical perspective on the efficacy of environmental and human health assessment tools. Water, Air, & Soil Pollution, 235(3). https://doi.org/10.1007/s11270-024-06965-1
Izah, S. C., Stanley, H. O., Richard, G., Sawyer, W. E., Uwaeme, O. R., & Sylva, L. (2024e). Source and health risks of trace metals in Clarias batrachus and Chrysichthys nigrodigitatus from surface waters in Bayelsa State, Nigeria: A probabilistic model. Frontiers in Sustainable Food Systems, 8, 1419143.
Izah, S. C., Sylva, L., & Hait, M. (2024f). Cronbach's alpha: A cornerstone in ensuring reliability and validity in environmental health assessment. ES Energy & Environment, 23, 1057. https://doi.org/10.30919/esee1057
Izah, S. C., Udofia, A. S., Johnson, I. U., & Etim, N. G. (2024g). Risk assessment and behavioral health statistics: Modeling lifestyle factors and exposure impacts on public health outcomes. Greener Journal of Epidemiology and Public Health, 12(1), 16-29.
Izah, S. C., Richard, G., Stanley, H. O., Sawyer, W. E., Ogwu, M. C., & Uwaeme, O. R. (2023). Integrating the one health approach and statistical analysis for sustainable aquatic ecosystem management and trace metal contamination mitigation. ES Food & Agroforestry, 14, 1012. https://doi.org/10.30919/esfaf1012
Izah, S. C., Etebu, E. N., Aigberua, A. O., Odubo, T. C., & Iniamagha, I. (2022). A meta-analysis of microbial contaminants in selected ready-to-eat foods in Bayelsa State, Nigeria: Public health implications and risk-reduction strategies. Hygiene and Environmental Health Advances, 4, 100017. https://doi.org/10.1016/j.heha.2022.100017
Jack, T. J., & Izah, S. C. (2025). Medical social work in hospice settings: Bridging clinical care and emotional support. Sustainable Social Development, 3(1), 31-36. https://doi.org/10.54517/ssd3136
Jack, T. J., & Izah, S. C. (2024a). Public health outcomes through medical social work: A focus on common bacterial infections. Journal of Advanced Research in Psychology & Psychotherapy, 7(1-2), 6-20.
Jack, T. J., & Izah, S. C. (2024a). Mental health interventions for post-disaster trauma in displaced communities in developing countries. Annals of Community Medicine and Practice, 9(2), 1064.
Jenson, J. M. (2014). Science, social work, and intervention research: The case of critical time intervention. Research on Social Work Practice, 24(5), 564-570. https://doi.org/10.1177/1049731513517144
Joshi, K. P., & Jamadar, D. C. (2021). Statistical software applications and statistical methods used in community medicine and public health research studies. National Journal of Community Medicine, 12(3), 53-56. https://doi.org/10.5455/njcm.20210329094615
Karran, J. C., Moodie, E. E., & Wallace, M. P. (2015). Statistical method use in public health research. Scandinavian Journal of Public Health, 43(7), 776-782. https://doi.org/10.1177/1403494815592735
Kiely, B., Croke, A., O'Shea, M., Boland, F., O'Shea, E., Connolly, D., & Smith, S. M. (2022). Effect of social prescribing link workers on health outcomes and costs for adults in primary care and community settings: A systematic review. BMJ Open, 12(10), e062951. https://doi.org/10.1136/bmjopen-2022-062951
Klingler, C., Silva, D. S., Schuermann, C., Reis, A. A., Saxena, A., & Strech, D. (2017). Ethical issues in public health surveillance: A systematic qualitative review. BMC Public Health, 17, 1-13. https://doi.org/10.1186/s12889-017-4200-4
Knoop, T., & Meyer, T. (2020). The effect of social work services on a self-reported functional outcome. Research on Social Work Practice, 30(5), 564-575. https://doi.org/10.1177/1049731520906607
Macgowan, M. J., & Wong, S. E. (2014). Single-case designs in group work: Past applications, future directions. Group Dynamics: Theory, Research, and Practice, 18(2), 138. https://doi.org/10.1037/gdn0000003
Mahmud, S. M., Thompson, L. H., Nowicki, D. L., & Plourde, P. J. (2013). Outbreaks of influenza-like illness in long-term care facilities in Winnipeg, Canada. Influenza and Other Respiratory Viruses, 7(6), 1055-1061. https://doi.org/10.1111/irv.12052
Maramaldi, P., Sobran, A., Scheck, L., Cusato, N., Lee, I., White, E., & Cadet, T. J. (2014). Interdisciplinary medical social work: A working taxonomy. Social Work in Health Care, 53(6), 532-551. https://doi.org/10.1080/00981389.2014.905817
Masud, N., Alenezi, S., Alsayari, O., Alghaith, D., Alshehri, R., Albarrak, D., & Al-Nasser, S. (2022, June). Social accountability in medical education: Students' perspective. In Frontiers in Education (Vol. 7, p. 868245). https://doi.org/10.3389/feduc.2022.868245
McGowan, B. S., Wasko, M., Vartabedian, B. S., Miller, R. S., Freiherr, D. D., & Abdolrasulnia, M. (2012). Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. Journal of Medical Internet Research, 14(5), e2138. https://doi.org/10.2196/jmir.2138
McNaughton, R. J., & Shucksmith, J. (2015). Reasons for (non) compliance with intervention following identification of 'high-risk' status in the NHS Health Check programme. Journal of Public Health, 37(2), 218-225. https://doi.org/10.1093/pubmed/fdu066
Meader, N., King, K., Moe-Byrne, T., Wright, K., Graham, H., Petticrew, M., Power, C., White, M., & Sowden, A. J. (2016). A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health, 16, 1-9. https://doi.org/10.1186/s12889-016-3373-6
Moser, A., Stuck, A. E., Silliman, R. A., Ganz, P. A., & Clough-Gorr, K. M. (2012). The eight-item modified Medical Outcomes Study Social Support Survey: Psychometric evaluation showed excellent performance. Journal of Clinical Epidemiology, 65(10), 1107-1116. https://doi.org/10.1016/j.jclinepi.2012.04.007
Nsoesie, E. O., Kluberg, S. A., & Brownstein, J. S. (2014). Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. Preventive Medicine, 67, 264-269. https://doi.org/10.1016/j.ypmed.2014.08.003
Ogwu, M.C., Izah, S.C., Sawyer WE, & Amabie, T. (2025). Environmental risk assessment of trace metal pollution: a statistical perspective. Environmental Geochemistry and Health. 47, 94 https://doi.org/10.1007/s10653-025-02405-z
Oriokot, L., Munabi, I. G., Kiguli, S., & Mubuuke, A. G. (2024). Perceptions and experiences of undergraduate medical students regarding social accountability: A cross-sectional study at a Sub-Saharan African medical school. BMC Medical Education, 24(1), 409. https://doi.org/10.21203/rs.3.rs-3756902/v1
Pierre-Louis, B., Guddati, A. K., Gorospe, V. E., Sultana, N., Aronow, W. S., Ahn, C., Clark, A. G., Wright, M., Fergus, I., et al. (2013). Identifying cardiovascular risk factors in a high-risk community: Strategies to address cardiovascular health disparities. Open Journal of Cardiology, 4, 1. https://doi.org/10.13055/ojcar_4_1_1.130726
Rosales, M., Lan, E., Guevara, R. E., Yumul, J. L., Morales, D. M., Higashi, J. M., & Chang, A. H. (2019). Spatial patterns of tuberculosis and diabetes mellitus in Los Angeles County, California. https://doi.org/10.21203/rs.2.13623/v1
Sadath, A., Muralidhar, D., Varambally, S., Justin, J., & Gangadhar, B. N. (2015). Effectiveness of group intervention for caregivers of persons with first episode psychosis. European Psychiatry, 30(S1), 1-1. https://doi.org/10.1016/s0924-9338(15)30669-6
Shawwat, M. A., & Atiyah, H. H. (2020). Physical behavior and interpersonal relationships among patients undergoing hemodialysis. International Journal of Health Sciences, 6(1), 8614-8621. https://doi.org/10.53730/ijhs.v6ns1.6503
Sørensen, K., Van den Broucke, S., Pelikan, J. M., Fullam, J., Doyle, G., Slonska, Z., Kondilis, B., Stoffels, V., Osborne, R. H., & Brand, H. (2013). Measuring health literacy in populations: Illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EU-Q). BMC Public Health, 13, 1-10. https://doi.org/10.1186/1471-2458-13-948
Sulistyawati, W., Fauziyah, N., & Niswati, N. (2023). Improving statistics learning outcomes through a differentiated learning approach for students with various levels of ability. Proceedings of the International Conference on Lesson Study, 1(1), 315-326. https://doi.org/10.30587/icls.v1i1.7046
Sun, Y., Yang, P., Wang, Q., Zhang, L., Duan, W., Pan, Y., Wu, S., & Wang, H. (2020). Influenza vaccination and non-pharmaceutical measure effectiveness for preventing influenza outbreaks in schools: A surveillance-based evaluation in Beijing. Vaccines, 8(4), 714. https://doi.org/10.3390/vaccines8040714
Sutherland, S., & Jalali, A. (2017). Social media as an open-learning resource in medical education: Current perspectives. Advances in Medical Education and Practice, 8, 369-375. https://doi.org/10.2147/amep.s112594
Tagliafico, A. S., Bignotti, B., Torri, L., & Rossi, F. (2022). Sarcopenia: How to measure, when, and why. La Radiologia Medica, 127(3), 228-237. https://doi.org/10.1007/s11547-022-01450-3
Teo, C. L., Chee, M. L., Koh, K. H., Tseng, R. M. W. W., Majithia, S., Thakur, S., Gunasekeran, D. V., Nusinovici, S., Sabanayagam, C., Wong, T. Y., et al. (2021). COVID-19 awareness, knowledge, and perception towards digital health in an urban multi-ethnic Asian population. Scientific Reports, 11(1), 10795. https://doi.org/10.1038/s41598-021-90098-6
Ukhalkar, P., Parakh, S., Phursule, R., & Sanu, L. (2021). Augmented analytics and modern business intelligence adoption to maximize business value. Journal of University of Shanghai for Science and Technology, 23(3), 286-296. https://doi.org/10.51201/jusst12685
van der Elst, K., Meyfroidt, S., De Cock, D., De Groef, A., Binnard, E., Moons, P., Verschueren, P., & Westhovens, R. (2016). Unraveling patient-preferred health and treatment outcomes in early rheumatoid arthritis: A longitudinal qualitative study. Arthritis Care & Research, 68(9), 1278-1287. https://doi.org/10.1002/acr.22824
Villani, J., Schully, S. D., Meyer, P., Myles, R. L., Lee, J. A., Murray, D. M., & Vargas, A. J. (2018). A machine learning approach to identify NIH-funded applied prevention research. American Journal of Preventive Medicine, 55(6), 926-931. https://doi.org/10.1016/j.amepre.2018.07.024
Wong, S. E. (2010). Single-case evaluation designs for practitioners. Journal of Social Service Research, 36(3), 248-259. https://doi.org/10.1080/01488371003707654
Xie, W., Ji, M., Zhao, M., Lam, K. Y., Chow, C. Y., & Hao, T. (2021). Developing machine learning and statistical tools to evaluate the accessibility of public health advice on infectious diseases among vulnerable people. Computational Intelligence and Neuroscience, 2021(1), 1916690. https://doi.org/10.1155/2021/1916690