Development of a Data Visualization Tool to Evaluate the Impact of a Maternal and Child Health (MCH) Nutrition Training Program on MCH Populations

Matern Child Health J. 2023 Apr;27(4):611-620. doi: 10.1007/s10995-023-03606-7. Epub 2023 Mar 2.

Abstract

Introduction: Maternal and Child Health (MCH) Nutrition Training Programs aim to train graduate-level registered dietitian/nutritionists (RDNs) to improve the health of MCH populations. Metrics exist to evaluate the production and success of skilled graduates; however, metrics are needed regarding the reach of MCH professionals. This study aimed to develop, validate, and administer a survey to estimate the reach of a MCH Nutrition Training Program's alumni within the MCH population.

Methods: First, content validity of the survey was established with input from an expert panel (n = 4); face validity was established using cognitive interviews (n = 5) with RDNs; a test-retest (n = 37) was conducted to establish instrument reliability. The final survey, emailed to a convenience sample of alumni, received a response rate of 57% s(n = 56 of 98). Descriptive analyses were completed to identify MCH populations that alumni served. Survey responses were used to develop a storyboard.

Results: Most respondents were employed (93%; n = 52) and serving MCH populations (89%; n = 50). Of those serving MCH populations, 72% indicated working with families, 70% with mothers/women, 60% with young adults, 50% with children, 44% with adolescents, 40% with infants, and 26% with children and youth with special health care needs. The storyboard was created and visually represents connections between public health nutrition employment classification, direct reach, and indirect reach of sampled alumni to MCH populations served.

Conclusion: The survey and storyboard are important tools that allow MCH Nutrition training programs to demonstrate their reach and to justify the impact of workforce development investments on MCH populations.

MeSH terms

  • Adolescent
  • Child
  • Child Health*
  • Data Visualization*
  • Female
  • Health Personnel / education
  • Humans
  • Infant
  • Public Health / education
  • Reproducibility of Results