Three Components of a Successful Prevention Program
One of the core challenges in prevention science and public health is establishing whether a program causes the outcomes we observe in the field. Are the gains we see (i.e., reduced substance use, improved mental well-being, or lowered dropout rates) directly attributable to the intervention we designed and implemented? Or might they result from unrelated social trends, fluctuating resources, or measurement artifacts?
To approach these questions, Randomized Controlled Trials (RCTs) have become the gold standard. In an RCT, we randomly assign participants or sites to treatment and control (or comparison) groups, then compare outcomes. If the treatment group shows a significantly better result than the control group, we often conclude that the program “worked.” But such a conclusion can be deceptively straightforward when programs are, in fact, systems—they typically involve an interplay of Theory of Change (T), Implementation (I), and Evaluation (E).
Programs as Systems
Consider a program meant to decrease adolescent vaping. The Theory of Change sets out the blueprint of why specific activities (peer mentoring, parental engagement, etc.) are expected to make a difference. Implementation is the process of putting those activities into action—hiring and training staff, ensuring session fidelity, adapting schedules to participants’ needs, and more. Finally, Evaluation measures outcomes, seeking evidence that the program actually achieved its stated goals.
When a prevention program is conceptualized as this interconnected system, a typical RCT that simply asks, “Does the program work?” can obscure which part of the system is actually influencing the observed outcomes. This can lead to confusing or contradictory findings, positive results might mask a faulty theory (if staff happened to do something else that helped), and negative or null results might disguise an essentially correct theory that was never implemented well.
Why Typical RCT Frameworks Can Be Limiting
- Black-Box Design
Traditional RCTs treat the entire program as a black box. If we see a difference in outcomes, we infer that the program caused it. Yet we rarely dissect which portion of the program functioned well or poorly. If results are mediocre, we cannot easily pinpoint whether it was the theory that failed or the implementation.
These three components (T, I, E) can have different effects on the outcomes for example:
- If T is flawed (the theory is incorrect or incomplete), the intervention might accidentally help in other ways or might fail entirely.
- If I is compromised (poor training, inadequate resources, low fidelity), the intervention as delivered differs substantially from the intervention as theorized.
- If E is misaligned (measuring the wrong outcome or using unreliable methods), results can be misleading, even if the theory and the implementation were sound.
- Confounded by Implementation
Consider an RCT run across multiple sites. Some sites deliver the program faithfully; others do not. If you average outcomes across all sites, the overall effect could appear modest—even though high-fidelity sites might have shown strong results. Hence, the classical RCT approach can mislead us into dismissing a promising program (or adopting one that only worked under special conditions).
Strengthening Causal Claims: A Systems View
By viewing prevention programs as systems, we can better diagnose why we see certain outcomes:
Disaggregate the Results Break down the program into T, I, and E. Evaluate fidelity, did staff deliver the planned activities (Implementation)? Did the underlying logic hold up in practice (Theory)? Did the measurement tools capture the actual changes (Evaluation)?
Assess Fidelity and Adaptation A high- or low-fidelity site comparison can reveal how much Implementation quality shapes outcomes. If only high-fidelity sites show significant improvements, the program’s fundamentals might be valid; the challenge is ensuring consistent delivery.
RCTs Are Still Useful—But We Need More
This is not to say that RCTs lack value. Random assignment remains a powerful way to rule out many external factors that might otherwise explain an effect. The point is that an RCT, on its own, cannot fully disentangle why a program succeeded or failed—particularly if we do not measure or account for the intricacies of T, I, and E. The RCT’s rigorous comparison can show whether a difference in outcome exists, but interpreting that difference requires a deeper system-level understanding.
Conclusion
Determining whether an observed effect is genuinely caused by a prevention program involves more than a simple yes/no answer from an RCT. When we treat these interventions as systems—each with a distinct theory, mode of implementation, and evaluation metric—our classical causal framework can become opaque, leading to confusion and misguided conclusions. Flaws in implementation might overshadow a perfectly valid theory; incorrect or narrow evaluation metrics can inflate or hide real changes.