# The Application of a Theoretical Framework to Assess the Acceptability of a Health-Related Social Needs Screening Tool Among Black Patients In New York City

> **NIH NIH F31** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $48,736

## Abstract

This NRSA F31 diversity proposal for Ms. Deborah Onakomaiya aims to provide mentorship, training, and
advance research experiences to prepare Ms. Onakomaiya for an independent research career in
implementation science and health disparities research. The research portion of this proposal aims to conduct a
mixed-methods study, informed by the theoretical framework of acceptability (TFA), to develop a
multidimensional understanding of the Accountable Health Communities Health-Related Social Needs
Screening (AHC-HRSN) screening tool among Black patients who receive care at NYU Langone Health
(NYULH). Recent studies suggest major gaps in how best to identify patients’ health-related social needs
(HRSNs) and offer assistance in a respectful way that upholds patient trust. The few studies that explored the
acceptability of HRSN tools largely focused on physician and caregiver perspectives and do not define
acceptability of HRSN or the drivers for acceptability of HRSN. To date, no study has utilized a theory-driven
approach to systematically assess the acceptability of HRSN screening tools among Black patients, who are
most likely to experience a high burden of HRSN. An essential first step or precursor in successfully addressing
HRSN is to examine the acceptability of screening tools among those who will be screened (e.g. patients).
Additionally, with the 2024 Centers for Medicare & Medicaid Services and Joint Commission on Accreditation of
Healthcare Organizations mandates to increase HRSN screening, there is a need for studies that demonstrate
the acceptability of HRSN tools. TFA is well positioned to capture nuanced factors that drive patient acceptability.
If an intervention, practice, or treatment is considered acceptable, patients are more likely to adhere to the
recommendations and benefit from improved clinical outcomes. Also the training plan for this proposal is
composed of mentored research, didactic and informal training, experiential learning and professional
development activities. The applicant will be supported by several resources at NYULH and by a strong
mentorship team with expertise in health services research, implementation science, health disparities research
and mixed-methods research. The research-training plan will allow Ms. Onakomaiya to prepare for a research
career by a) Learning and applying rigorous implementation and psychometric methods; b) developing expertise
in health services and health disparities research; and c) generating and disseminating scientific knowledge to
inform public health practice. This study covers a high priority area for NIMHD and will be one of the first in the
U.S. to examine the proposed project in an integrated health system like NYULH . As this project will leverage
NYULH’s infrastructure which is primed to implement system-wide screening for HSRNs among its patient
population. This project will support NIMHDs goals to promote research to understand and to improve the health
of racial/ethnic...

## Key facts

- **NIH application ID:** 11016929
- **Project number:** 5F31MD019216-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Deborah Onakomaiya
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,736
- **Award type:** 5
- **Project period:** 2023-09-19 → 2025-09-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11016929

## Citation

> US National Institutes of Health, RePORTER application 11016929, The Application of a Theoretical Framework to Assess the Acceptability of a Health-Related Social Needs Screening Tool Among Black Patients In New York City (5F31MD019216-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11016929. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*
