Opportunities and challenges to Integrating mental health into HIV programs in a Low- and Middle-Income Country: Insights from the Nigeria Implementation Science Alliance
Bibliographic record
Abstract
Abstract Background: In Nigeria, there is an estimated 1.9 million people living with HIV (PLHIV), 53% of whom utilize HIV care and services. With decreasing HIV-related deaths and increasing new infections, HIV with its associated comorbidities continue to be a key public health challenge in Nigeria. Untreated, comorbid mental disorders are a critical but potentially modifiable determinant of optimal HIV treatment outcomes. This study aimed to identify the challenges and opportunities related to integrating mental health care into existing HIV programs in Nigeria. Method: Attendees at the Nigeria Implementation Science Alliance (NISA)'s 2019 conference participated in nominal group technique (NGT) exercise informed by the "Exploration, Preparation, Implementation, and Sustainment (EPIS)" framework. The NGT process was conducted among the nominal groups in two major sessions of 30-minutes phases followed by a 30-minute plenary session. Data analysis proceeded in four steps: transcription, collation, theming and content analysis. Results: The two major theoretical themes from the study were – opportunities and challenges of integrating mental health treatment into HIV services. Three sub-themes emerged on opportunities: building on health care facilities for HIV services (screening, counseling, task-sharing monitoring and evaluation frameworks) , utilizing existing human resources or workforce in HIV programs (in-service training and including mental health in education curriculum) and the role of social and cultural structures (leveraging existing community, traditional and faith-based infrastructures). Four sub-themes emerged for challenges: double burden of stigma and the problems of early detection (HIV and mental health stigma, lack of awareness) , existing policy gaps and structural challenges (fragmented health system) , limited human resources for mental health care in Nigeria (knowledge gap and burnout) and dearth of data/evidence for planning and action (research gaps) . Conclusions: Potential for integrating treatments for mental disorders into HIV services exist in Nigeria. These include opportunities for clinicians' training and capacity building as well as community partnerships. Multiple barriers and challenges such as stigma, policy and research gaps would need to be addressed to leverage these opportunities. Our findings serve as a useful guide for government agencies, policy makers and research organizations to address co-morbid mental disorders among PLHIV in Nigeria.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".