Best Intentions are Not Enough: Techniques for Using Research and Data to Develop New Evidence-Informed Prevention Programs. Identifying Relevant Risk, Protective, and Promotive Factors


Many types of information can be used to identify risk, protective and promotive factors that could be targets for interventions. Here we highlight one research-based approach – meta-analysis – and briefly describe several additional information sources including developmental theory and the knowledge of experienced practitioners.

Meta-analysis is a useful technique both for identifying risk factors associated with a problem and for systematically isolating those that can be influenced through targeted interventions. Work by Mark Lipsey illustrates the use of meta-analysis to inform the process of identifying targets for change. Figure 1 shows results from Lipsey's recent (2011) meta-analysis examining predictors of adolescent antisocial behavior[1] Lipsey examined the average correlations between various predictor variables and subsequent antisocial behavior calculated across 419 independent longitudinal studies. (Because participant samples and methods varied across studies, a standardized correlation metric that adjusted for these differences was used.) The findings indicate that there is wide variation in the strength of the relationships between predictors measured at age 10 and later antisocial behavior. The category with one of the largest correlations in Figure 1 reflects prior delinquent behavior. This suggests that those interested in preventing antisocial behavior may want to think about constructing interventions that target middle school students with an early onset of delinquent behavior.

Externalizing (acting out) behavior and substance use also emerged as relatively strong targets for early intervention. This could mean that new interventions might want to address not only early delinquency, but early acting out behavior and substance abuse as mechanisms for reducing later antisocial behavior. By contrast, factors like self-esteem, internalizing symptoms (e.g., depression, sadness), and parental warmth have low correlations here, indicating that they are not relevant targets for intervention if your goal is to reduce adolescent antisocial behavior.


Figure 1: Risk and Promotive Factors at Age 10 Predicting Antisocial Behavior at Age 16


Source: Lipsey, M.W. (2011, April). Using research synthesis to develop "evidence-informed" interventions. Paper presented at the Emphasizing Evidence Based Programs for Children and Youth Forum, Washington, DC.

Another source for identifying key risk, protective and promotive factors in order to design an evidence-informed intervention is developmental theory. An ecological perspective on human development, for instance, highlights the importance of varied contexts for the development of children and youth (Brofenbrenner, 1979). Self-efficacy theories highlight elements related to persistence in the face of specific challenges (Bandura, 1982), such as the academic self-efficacy beliefs that are related to academic accomplishment. A recent review of parenting programs found that many interventions aimed toward improving child and adolescent outcomes identify social learning theory (27%) or cognitive behavioral theory (26%) as the foundation for their intervention strategy (Abt Associates, Inc., in progress). The FAST (Families and Schools Together) program has drawn on family stress theory, family systems theory, and social ecological theory to develop program activities, structure, and implementation (Small, Cooney & O'Connor, 2009). Which developmental theories are appropriate sources for ideas will vary depending on the particular problem being targeted and population toward which the intervention is directed. In addition, not all developmental theories provide insight into ways of influencing development – some simply describe invariant patterns. Even these theories may be useful, however, in considering characteristics, behaviors, or tendencies that cannot be changed and thus should not be targeted for intervention.

The knowledge of experienced practitioners, long-time community members, and tribal or First Nations keepers of cultural wisdom can also suggest relevant risk, protective, and promotive factors. Recognizing that some practitioners over-emphasize the value of information-only approaches, it must nevertheless be acknowledged that many, or perhaps most, strong program approaches have arisen from the efforts of local programs. One example is the Children's Aid Society program to prevent teen pregnancy, which was developed in New York City some years before being formally evaluated and found to have positive impacts (Philliber, Kaye, et al., 2002). In another example, interviews and epidemiological data among the Inuit (McGrath-Hanna , Green, et al., 2003) led to the discovery of the role of omega-3 fatty acid in human behavioral and physical health for infants, children and adults, now established in multiple randomized trials and longitudinal studies (Richardson, 2012; Sublette, Ellis, et al., 2011; Amminger, Schafer, et al., 2010). Insights from practitioners can be obtained by conducting interviews or focus groups, attending meetings of practitioners, and reading publications of practitioner associations. Insights from children and youth can also be sought in direct observations, interviews or focus groups.

Longitudinal studies can also be instructive in identifying relevant information about potential risk, protective, and promotive factors to target. With care, correlational data can also be informative. For example, decades ago, the correlation between smoking and lung cancer led cancer researchers to investigate the relevance of tobacco as a carcinogen (Hecht, 1999). Similar correlations can inform social interventions, though it is important to caution that such analyses do not prove causality and should take account of possible confounding factors.

Of course, demonstrating a causal relationship that justifies targeting a risk, protective, or promotive factor for change, in order to improve the outcome it predicts, requires two other forms of evidence. First, it must be shown that the predictive factor can be changed by intervention—that it is malleable. Second, change in that factor must then result in change in the behavior it is intended to prevent. For instance, in the case of substance abuse, multiple longitudinal studies have identified early disruptive, inattentive behaviors in the primary grades as predictors of serious drug use in adolescence and young adulthood. Experimental studies, in turn, have demonstrated that such early disruptive, inattentive behaviors are changeable using family (Sanders, 2012) or school-based strategies (Embry, 2002), including mass media. Moreover, when applied, those strategies are effective for preventing later substance abuse (Furr-Holden , Ialongo et al., 2004) and promoting other positive outcomes (Kellam, Mackenzie et al., 2011).

A list of predictive factors, such as those illustrated in Figure 1, therefore, neither tells us specifically what to do to alter them in a favorable direction or which ones, when manipulated, will actually be effective in preventing delinquency. Deeper digging is required to find or invent effective prevention, intervention, treatment, or recovery strategies.

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