Background: The direct and indirect impact of the SARS-CoV-2 virus and its mitigation measures have exacerbated the global mental health crisis. Digital mental health interventions (DMHIs) may have the potential to address health system gaps and global health inequalities in low-and middle-income countries (LMICs).
Purpose: This thesis aims to map the current state of DMHIs available in Nigeria and illustrate their progress, limitations, and challenges. This study aims to expand upon the findings of recent studies in LMICs by incorporating the perspectives of individuals who play a prominent role in global mental health. The lessons learned in the Nigerian context can inform the delivery of DMHIs in other low-resource settings.
Methods: This research was conducted using case study methodology. Twenty semi-structured interviews were conducted with mental health researchers, healthcare providers, digital health experts and policy makers. Data sources such as news articles, websites, research papers, and interviews were used. Interviews were recorded and transcribed, and data from multiple sources were then converged, coded, and analyzed using Dedoose via thematic analysis.
Findings: The vast majority of DMHIs in Nigeria are private mental health service delivery platforms that connect directly to mental health professionals. The target audience for most DMHIs are broad and encompass all mental health conditions and ages. Advantages of DMHIs include increasing efficiency, accessibility, addressing stigma, and filling the mental health service gap. Disadvantages include skepticism in DMHIs, limitations of applicability, lack of accessibility to internet and technology, lack of sustainability, and lack of infrastructure, funding, and policies.
Conclusions: There is a need to leverage DMHIs within the Nigerian population for mental health promotion. Future research should examine feedback from users and providers of DMHIs to allow for comparative analysis, more conclusive and replicable results to inform DMHI design and implementation. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28479 |
| Date | January 2023 |
| Creators | Chen, Tiffany |
| Contributors | Gombay, Christy, Archer, Norm, Acai, Anita, Global Health |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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