Advantages and disadvantages of digital mental health initiatives in Nigeria – a qualitative interview study
Bibliographic record
Abstract
BACKGROUND: The impact of COVID-19 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). AIMS: This thesis aims to map the current state of DMHIs in Nigeria and illustrate their progress, limitations, and challenges. METHODS: Twenty interviews were conducted with researchers, healthcare providers, and digital health experts. Interviews were recorded and transcribed. Then data were coded and analyzed using thematic analysis. RESULTS: The majority of DMHIs in Nigeria are private mental health service delivery platforms that connect directly to mental health professionals. The target audience encompasses 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, limitations of applicability, lack of accessibility to internet and technology, lack of sustainability and infrastructure, and lack of funding and policies. CONCLUSION: The lessons learned in the Nigerian context can inform the delivery of DMHIs in other low-resource settings. Future research should examine user and provider feedback of DMHIs to allow for comparative analysis, more conclusive and replicable results to inform DMHI design and implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".