HLTH 6433 Module 2 Data-Informed Decision Paper Example

Which Two Schools First? Using District and Partner Data to Choose and Defend the First School-Based Health Center Sites

Student Name

American College of Education

HLTH6433: Foundational Leadership in Health Education

Module 2 Assignment

Instructor Name

April 27, 2026

Introduction

Module 1 described a health services coordinator in a composite Carolina Piedmont district of about 6,800 students who hopes to open school-based health centers, which a recent review found widen children's access to care (Arenson et al., 2019), with a community health center partner. The partner can staff two centers in the first year. The district has three middle schools and two high schools, and principals, board members and parents at each have reasons to want the first center, or to fear it. The choice of sites is the project's first public decision, and how it is made will shape trust in everything that follows. This paper sets out the decision, the data used, the method for weighing them, the result, a test of how sensitive the result is to judgment calls and how the coordinator will present and defend it.

Data-Driven Decision Making

Mandinach (2012) treats data-driven decision making in schools as the work of turning raw data into information by organizing and summarizing it and then into usable knowledge by interpreting it in context and deciding what to do. The process depends on asking a clear question first, choosing data that bear on it and being honest about what the data cannot show. For this decision, the question is: at which two schools would a center meet the greatest unmet need and be most likely to succeed in its first year? That question has two parts, need and readiness, and the data are chosen to answer both.

The Data

Six indicators were assembled for each school. Economic need was measured by the share of students eligible for free or reduced-price meals, from 48% at the east high school to 81% at the south middle school. Educational effect was measured by chronic absence, the share of students missing at least 10% of school days, ranging from 14% to 29%. Unmet health need at school was measured by nurse dismissals, the number of students sent home for illness per 100 students last year, ranging from 11 to 24. Insurance status came from the partner's analysis of Medicaid enrollment by attendance zone, with the estimated share of uninsured students ranging from 5% to 12%. Emergency department use came from the regional hospital's count of visits by district-age residents for conditions usually treatable in primary care, by attendance zone, per 1,000 students. Readiness was measured by space, a principal's written interest and distance from the partner's existing clinic.

Protecting Privacy in the Data

All data were received in aggregate form by school or attendance zone, never at the student level. Nurse dismissal counts came from the district's own health office logs, summarized by the school nurses before sharing. The hospital and partner shared rates rather than records, under a data-use agreement reviewed by the district's attorney. No figure small enough to identify a student was reported. This approach limits some analyses, such as linking dismissals to insurance status, but it respects the confidentiality obligations that apply to education and health records. Federal guidance explains that records kept by school nurses employed by a district are generally education records under FERPA rather than health records under HIPAA, while records kept by an outside health provider are governed by HIPAA, a distinction that will matter again when the centers open (U.S. Department of Health and Human Services & U.S. Department of Education, 2019).

Weighing the Indicators

The steering group, including two principals, two school nurses, a parent, the partner's medical director and the coordinator, agreed on weights before seeing school names attached to the scores. Need indicators received 70% of the weight: free and reduced-price meals 15%, chronic absence 15%, nurse dismissals 15%, uninsured share 10% and emergency department use 15%. Readiness received 30%: suitable space 15%, written principal interest 10% and distance from the existing clinic 5%, with greater distance scored higher because it indicates less existing access. Each indicator was converted to a score from 0 to 10 relative to the range across the five schools. Setting the weights blind to school names was the single most important step for defending the result, because no one could say the weights had been chosen to produce it.

The Result

The south middle school scored highest overall, 8.6 of 10, with the highest meal eligibility, the most nurse dismissals, high emergency department use and a principal who had already identified two classrooms that could be converted. The north high school scored second, 7.4, with the highest chronic absence, a high uninsured share and space in a former vocational wing. The west middle school scored 6.9, close behind, with high need but no available space until a planned renovation. The east high school scored 4.2 and the central middle school 3.8, both with lower need and closer proximity to the existing clinic.

Testing the Result

Because weights involve judgment, the coordinator tested whether reasonable changes would alter the choice. With need and readiness weighted equally, the same two schools ranked first and second. With readiness removed entirely, west middle school moved into second place ahead of north high school by 0.2 points. With emergency department use removed, since the hospital data covered attendance zones rather than enrolled students, the ranking was unchanged. The south middle school ranked first under every scenario. The choice of north high school over west middle school therefore depends mainly on readiness, which is a legitimate consideration for a first-year launch but should be stated openly.

Limitations of the Data

The data have limits that the coordinator will acknowledge. Nurse dismissal counts depend on each nurse's recording habits. Uninsured estimates are modeled from attendance zones and enrollment data rather than measured directly. Emergency department data reflect where students live rather than where they attend school, which matters for students who transfer. None of the data capture mental health need well, although school counselors report it as the greatest unmet need at the high schools. These limits do not invalidate the decision, but they are reasons to revisit it after the first year with better measures.

Defending the Decision

The coordinator will present the decision to the school board in a public session with a short written brief. The brief will state the question, list the indicators and their sources, show the weights and how they were chosen, give each school's score, show the sensitivity tests and name the limitations. It will also state what the other schools can expect: west middle school is recommended as the third site once its renovation is complete, and all schools will receive expanded nurse telehealth access in the meantime. Presenting the method in full invites scrutiny, but it also allows board members and principals who disagree to see that the choice was made fairly, which matters more for the project's future than any single site.

Conclusion

Using data to choose the first two sites turns a potentially divisive decision into a transparent one. The south middle school is the clear first choice; the north high school's second place rests on readiness and is stated as such. The method, the privacy safeguards, the sensitivity tests and the plan for the remaining schools together give the coordinator a decision that can be defended in public. Module 3 turns to the relationships and trust that will determine whether the centers are welcomed once they open.

References

Arenson, M., Hudson, P. J., Lee, N., & Lai, B. (2019). The evidence on school-based health centers: A review. Global Pediatric Health, 6, Article 2333794X19828745. https://doi.org/10.1177/2333794X19828745

Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71-85. https://doi.org/10.1080/00461520.2012.667064

U.S. Department of Health and Human Services & U.S. Department of Education. (2019). Joint guidance on the application of the Family Educational Rights and Privacy Act (FERPA) and the Health Insurance Portability and Accountability Act of 1996 (HIPAA) to student health records (2019 update).