Kenya just launched long-acting HIV prevention. Uganda received its first Lenacapavir doses. But here's what I've learned from African health systems: the launch is easy. The real test is whether clinics can deliver, communities will trust, and funding will last after the headlines fade.

Evidence doesn't speak for itself—it speaks through relationships. In my work across KEMRI and Kenya's Ministry of Health, I've seen brilliant research dismissed not for technical flaws, but because researchers lacked follow-through or seemed driven by external agendas. Policy actors rarely reject findings on merit; they reject them based on trust. Building credibility means showing up consistently, acknowledging limitations, and staying engaged when implementation gets mess

Here's where trust is genuinely built or destroyed. Every research organization makes mistakes. Mislabeled samples. Coding errors in statistical analysis. Protocol deviations during recruitment. The question isn't whether errors occur—it's what happens when they're discovered.

Many professionals collect indicator data without being confident it actually reflects impact. This article draws on real-world experience to explore why indicators often miss the point, how well-intentioned frameworks can fail in practice, and what it takes to design indicators that genuinely support learning, decision-making, and meaningful change.

If Monitoring, Evaluation, and Learning (MEL) were only about tools and templates, most development organizations would be doing exceptionally well. They have log frames. They have indicators. They have dashboards, frameworks, and reports stacked neatly in shared folders. Yet, despite all this structure, the same questions keep coming back: Why didn’t this programme achieve what we expected? Why does the data look good, but the reality feel different? Why do we keep repeating

Many research organizations collect vast amounts of data but struggle to turn it into better decisions. Understanding the difference between monitoring, evaluation, and learning (MEL) is essential for improving research quality, relevance, and impact. This article breaks down how each function works in practice, why confusing them weakens research outcomes, and how researchers can use evidence not just to report results but to learn, adapt, and improve future studies.

Many NGO reports never shape decisions despite solid analysis behind them. This piece examines how clarity, storytelling, and practical recommendations can turn reports into tools for action.

Choosing between quantitative and qualitative research is not about preference—it’s about purpose. This article explains when to use each approach, with practical examples from health and development programmes to support better, evidence-based decisions.

Many NGOs collect large volumes of data but struggle to turn evidence into effective decisions. This article explores how evidence-based decision-making should drive NGO strategy, why data often fails to influence action, and practical ways organisations can translate research and monitoring data into real impact.

Get ready for Black Friday! Discover the tech, data tricks, and health impacts hidden behind those '70% off' deals. Is it all worth it?

Tired of the 3 P desk jockey slump? Ditch the sugar crash and conquer the workday with these brain-boosting snack hacks! #productivity #

Ditch the "man up" this June. Men's Mental Health Month is here.

The healthcare system fails the black patient through systemic racism.

Freelancing offers a great deal of freedom, but it also presents a distinct challenge: the irresistible allure of the couch. During...