HLTH 5043 Module 3 Evaluation Approach Comparison Example

Arrows, Dimensions and Steps: Comparing a Classic Logic Model, RE-AIM and the 2024 CDC Framework for Evaluating a County Birth Equity Program

Student Name

American College of Education

HLTH5043: Evaluation of Determinants of Health

Module 3 Assignment

Instructor Name

October 19, 2026

The Program to Be Evaluated

The previous modules documented a 2.7-fold gap in infant deaths between Black and White infants in a composite county and traced it to structural racism and segregation, discrimination and chronic stress, gaps in care, and material hardship that shapes sleep environments. In response, the health department is planning a birth equity program for Black mothers on the county's east side with three components: community doulas who support mothers from mid-pregnancy through the first months after birth; group prenatal care offered in partnership with the community hospital; and a postpartum home visit that provides a portable crib and safe sleep guidance tailored to the family's housing.

Before the program starts, the department must decide how it will be evaluated. This paper compares three approaches: a classic logic model, the RE-AIM framework and the 2024 CDC Program Evaluation Framework. Each organizes an evaluation differently, and each reveals something the others can miss.

The Classic Logic Model

A logic model is a picture of how a program is expected to work, linking the resources invested to the activities carried out, the direct products of those activities and the changes that follow. One of the most widely used guides describes the chain as resources, activities, outputs and short-term, intermediate and long-term outcomes (W.K. Kellogg Foundation, 2004), and presents the logic model as a tool for planning, communicating and evaluating a program.

Applied to the birth equity program, the logic model makes the theory explicit. Resources include funding, doulas, clinic space and cribs. Activities include doula visits, group sessions and home visits. Outputs are counts, such as mothers enrolled, sessions attended and cribs delivered. Short-term outcomes include knowledge and trust; intermediate outcomes include earlier prenatal care, fewer preterm births and safer sleep practices; and the long-term outcome is a smaller gap in infant deaths. Its strength is clarity: staff, funders and community members can see the program's reasoning on one page and argue about its weak links. Its limitation is that it describes how the program should work, not whether it reaches the right people or lasts, and its tidy arrows can hide the conditions outside the program that shape results.

RE-AIM

Glasgow et al. (1999) introduced RE-AIM because, in their view, a program's value to a population depends on much more than whether it works in a trial. The framework has five dimensions: reach, the share and representativeness of the intended population who participate; efficacy or effectiveness, the effect on outcomes; adoption, the share and representativeness of settings and staff that take up the program; implementation, the extent to which it is delivered as intended; and maintenance, whether effects and delivery last over time.

For a birth equity program, RE-AIM asks questions a logic model can skip. Reach is central: if the program enrolls mostly mothers who already have early prenatal care, stable housing and trust in the system, it could look successful while leaving the highest-risk families untouched. Adoption asks whether the community hospital's obstetric clinicians will refer to group care. Implementation asks whether doulas can maintain the planned visit schedule with high caseloads. Maintenance asks what happens when initial grant funding ends. A program that works beautifully for the families easiest to reach can widen the very gap it was built to close. RE-AIM's limitation is that its dimensions are a checklist rather than a causal story; it tells an evaluator what to measure but not why the program should work.

The 2024 CDC Program Evaluation Framework

The CDC updated its program evaluation framework in 2024. Kidder et al. (2024) set out six steps that run from understanding the setting and describing the program, through choosing questions and a design and collecting trustworthy data, to drawing conclusions and using them, with three commitments woven through every step: working collaboratively with those affected, advancing equity, and learning from findings and putting them to use. The framework is a process for planning and conducting an evaluation rather than a model of the program itself, and it incorporates the logic model within its step of describing the program.

For this program, the framework's greatest contribution is its insistence that evaluation be done with, not only on, the community. Assessing context would bring in the history of distrust between east-side residents and local institutions. Engaging collaboratively would involve mothers and doulas in choosing evaluation questions, and advancing equity would mean examining who benefits and who does not at every step. Its limitation is breadth: it tells an evaluator how to proceed but leaves the choice of measures and design to the team.

Comparing the Three

The three approaches answer different questions. The logic model answers how the program is supposed to work. RE-AIM answers whether it reaches, is adopted by, is delivered in and endures within the settings and populations that matter. The CDC framework answers how to carry out an evaluation that is credible, useful and fair. They are not competitors so much as layers. A team using only the logic model might report that 200 mothers enrolled and preterm birth fell among them, without asking whether those mothers were the ones at highest risk. A team using only RE-AIM might measure reach and implementation carefully without a clear theory of why the program should reduce preterm birth. A team using only the CDC framework would have a sound process but would still need a program description and a set of dimensions to measure.

Fit to This Program

Two features of the birth equity program make the choice consequential. First, its goal is to reduce a disparity, not just to improve an average, so the evaluation must measure effects for the population at greatest risk and not only for participants. That favors RE-AIM's attention to reach and the CDC framework's equity action. Second, the long-term outcome, infant death, is rare in any single year, so the evaluation will rely heavily on intermediate outcomes, such as preterm birth and safe sleep, whose connection to the long-term outcome must be argued. That favors the logic model's explicit causal chain, supported by evidence such as a randomized trial in which group prenatal care reduced preterm birth, with a stronger effect among African American women (Ickovics et al., 2007).

Recommendation

The department should combine the three. It should use the 2024 CDC framework as the overall process, forming an evaluation committee that includes east-side mothers, doulas and hospital staff. Within the step of describing the program, it should build a logic model with the committee, testing each arrow against evidence. Within the step of focusing the evaluation, it should use RE-AIM's five dimensions to organize questions, with reach measured against all Black births on the east side and not only against enrollees. The next module develops the logic model in detail, and the final module turns the combined approach into a full evaluation plan.

References

Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89(9), 1322-1327. https://doi.org/10.2105/AJPH.89.9.1322

Ickovics, J. R., Kershaw, T. S., Westdahl, C., Magriples, U., Massey, Z., Reynolds, H., & Rising, S. S. (2007). Group prenatal care and perinatal outcomes: A randomized controlled trial. Obstetrics & Gynecology, 110(2), 330-339. https://doi.org/10.1097/01.AOG.0000275284.24298.23

Kidder, D. P., Fierro, L. A., Luna, E., Salvaggio, H., McWhorter, A., Bowen, S.-A., Murphy-Hoefer, R., Thigpen, S., Alexander, D., Armstead, T. L., August, E., Bruce, D., Clarke, S. N., Davis, C., Downes, A., Gill, S., House, L. D., Kerzner, M., Kun, K., ... Young, K. (2024). CDC program evaluation framework, 2024. MMWR Recommendations and Reports, 73(6), 1-37. https://doi.org/10.15585/mmwr.rr7306a1

W.K. Kellogg Foundation. (2004). Logic model development guide.