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Summing up. Recommendations and experiences for evaluation of community-level prevention programs
H D Holder1, A J Treno, R F Saltz
1Prevention Research Center, Berkeley, California, USA.
Evaluation Review
|March 8, 1997
Summary
Evaluating community-level prevention projects presents unique challenges. Traditional evaluation methods struggle with community-wide interventions due to difficulties in isolating effects and controlling variables.
Area of Science:
- Public Health
- Community Psychology
- Program Evaluation
Background:
- Community-level prevention projects aim to address issues at a broader societal scale.
- Shifting focus from individual to community-wide interventions introduces significant evaluation complexities.
- Existing evaluation methodologies often fall short in addressing the nuances of aggregate-level programs.
Purpose of the Study:
- To offer recommendations and insights for evaluating community-based prevention initiatives.
- To highlight the difficulties in assessing interventions targeting the entire community structure and environment.
- To address the gap between program development and evaluation techniques for community-level work.
Main Methods:
- The article discusses observational and recommendation-based approaches for evaluation.
- It contrasts individual-focused evaluation with community-aggregate level assessment.
- Challenges in applying traditional methods like random assignment and comparison groups are examined.
Main Results:
- Community-level interventions are demanding due to the lack of a specific, isolatable target group.
- Evaluation tools and techniques have not kept pace with the development of community-wide programs.
- The validity of controlling confounding variables is weakened when the community is the unit of analysis.
Conclusions:
- Evaluating community-level prevention requires adapting traditional methods or developing new ones.
- The complexity of community systems and external influences (history, trends) complicates outcome attribution.
- Further development in evaluation science is needed to effectively measure the impact of aggregate-level interventions.