Generating evidence for health policy in challenging settings: lessons learned from four prevention of mother-to-child transmission of HIV implementation research studies in Nigeria
Bibliographic record
Abstract
BACKGROUND: Implementation research (IR) facilitates health systems strengthening and optimal patient outcomes by generating evidence for scale-up of efficacious strategies in context. Thus, difficulties in generating IR evidence, particularly in limited-resource settings with wide disease prevention and treatment gaps, need to be anticipated and addressed. Nigeria is a priority country for the prevention of mother-to-child transmission of HIV (PMTCT). This paper analyses the experiences of four PMTCT IR studies in Nigeria, and proffers solutions to major challenges encountered during implementation. STUDIES INCLUDED AND FINDINGS: Multicentre PMTCT IR studies conducted in Nigeria during the Global Plan's assessment period (2011 to 2015) were included. Four studies were identified, namely The Baby Shower Trial, Optimizing PMTCT, MoMent and Lafiyan Jikin Mata. Major common challenges encountered were categorised as 'External' (beyond the control of study teams) and 'Internal' (amenable to rectification by study teams). External challenges included healthcare worker strikes and turnover, acts and threats of ethnic and political violence and terrorism, and multiplicity of required local ethical reviews. Internal challenges included limited research capacity among study staff, research staff turnover and travel restrictions hindering study site visits. Deliberate research capacity-building was provided to study staff through multiple opportunities before and during study implementation. Post-study employment opportunities and pathways for further research career-building are suggested as incentives for study staff retention. Engagement of study community-resident personnel minimised research staff turnover in violence-prone areas. CONCLUSIONS: The IR environment in Nigeria is extremely diverse and challenging, yet, with local experience and anticipatory planning, innovative solutions can be implemented to modulate internal challenges. Issues still remain with healthcare worker strikes and often unpredictable insecurity. There is a dire need for cooperation between institutional review boards across Nigeria in order to minimise the multiplicity of reviews for multicentre studies. External challenges need to be addressed by high-level stakeholders, given Nigeria's crucial regional and global position in the fight against the HIV epidemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".