Assessing Community-Based Injury Prevention Services in U.S. Childrens Hospitals

Nancy L. Weaver; Victoria Kortlandt; Janice Williams; Keri Jupka; Trent D. Buskirk; Salwa Maalouf; Stacy Biddinger; Nancy Hanson; Karen Seaver Hill; Nancy L. Weaver; Victoria Kortlandt; Janice Williams; Keri Jupka; Trent D. Buskirk; Salwa Maalouf; Stacy Biddinger; Nancy Hanson; Karen Seaver Hill

doi:10.3934/Publichealth.2014.4.199

AIMS Public Health

2014, Volume 1, Issue 4: 199-210. doi: 10.3934/Publichealth.2014.4.199

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Research article

Assessing Community-Based Injury Prevention Services in U.S. Childrens Hospitals

1.
Department of Behavioral Science and Health Education, College for Public Health & Social Justice, 3545 Lafayette Avenue, Saint Louis University, St. Louis, MO 63104, USA;
2.
Department of Biostatistics, College for Public Health & Social Justice, 3545 Lafayette Avenue, Saint Louis University, St. Louis, MO 63104, USA;
3.
Department of Emergency Medicine, Carolinas Medical Center, Charlotte NC 28232, USA;
4.
College for Public Health & Social Justice, 3545 Lafayette Avenue, Saint Louis University, St. Louis, MO 63104, USA;
5.
Children's Hospital Association, Washington, DC 20005, USA

Received: 01 August 2014 Accepted: 02 October 2014 Published: 31 October 2014

Objective: Not-for-profit hospitals are required to meet federal reporting requirements detailing their community benefit activities, which support their tax-exempt status. Children's hospitals have long provided community injury prevention (IP) programming and thus can inform public health outreach work in other areas. This work describes IP programming as a community service offered by children's hospitals in the U.S. Methods: The IP specialist at 232 US-based member institutions of the Children's Hospital Association were invited to complete an assessment of their hospital's IP outreach programming. Results: 47.7 percent of hospitals request financial data from IP programming for tax reporting purposes. Almost all offer injury prevention (IP) services; the majority are in the community (60.3%) and 34.5% are hospital-based. Most IP units are independent (60.3%) and 71.8% are responsible for their own budgets. Conclusions: By integrating dissemination and implementation sciences and community health needs assessments, these findings can help advance community services provided by hospitals to impact public health.

Keywords:

Citation: Nancy L. Weaver, Victoria Kortlandt, Janice Williams, Keri Jupka, Trent D. Buskirk, Salwa Maalouf, Stacy Biddinger, Nancy Hanson, Karen Seaver Hill. Assessing Community-Based Injury Prevention Services in U.S. Childrens Hospitals[J]. AIMS Public Health, 2014, 1(4): 199-210. doi: 10.3934/Publichealth.2014.4.199

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Abstract

References

[1]	Hellinger F. (2009) Tax-exempt hospitals and community benefits: a review of state reporting requirements. J Health Polit Policy Law 34(1): 37-61.
[2]	ACHI Community Health Assessment Toolkit. (2014) Available from: http://www. assesstoolkit. org. Accessed July 2, 2014.
[3]	Community Commons. (2014) Available from http://www. communitycommons. org. Accessed Juy 2, 2014.
[4]	Rosenbaum S. (2013) Principles to consider for the implementation of a community health needs assessment process.
[5]	Pressley J, Barlow B, Durkin M, et al. (2005) A national program for injury prevention in children and adolescents: The injury free coalition for kids. J Urban Health 82(3): 389-402.
[6]	7. Children's Safety Network. (2012) Injury prevention: hat works? A summary of costs-outcome analysis for injury prevention programs.
[7]	8. Centers for Disease Control and Prevention. (2012) National action plan for child injury prevention: an agenda to prevent injuries and promote safety of children and adolescents in the United States. Atlanta: National Center for Injury Prevention and Control.
[8]	9. Duncan P, Amy E. (2009) Bright futures for the busy clinical practice: A clinical guide for pediatricians. Adol Health Update 22(1): 1-
[9]	10. Hagan J, Duncan P. (2008) Bright futures: guidelines for health supervision of infants, children and adolescents. Elk Grove Village: American Academy of Pediatrics.
[10]	11. Trauma Programs. (2012) New verification site visit outcomes. Chicago: American College Surgeons.
[11]	12. Kerner J, Rimer B, Emmons K. (2005) Introduction to the special section on dissemination. dissemination research and research dissemination: how can we close the gap. Health Psychol24(5): 443-446.
[12]	13. Glasgow R, Lichtenstein E, Marcus A. (2003) Why don't we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health 93: 1-1267.
[13]	14. Fixsen DL, Naoom SF, Blase KA, et al. (2005) Implementation research: a synthesis of the literature. Tampa: University of South Florida.
[14]	15. Wandersman A, Duffy J, Plaspohler P, et al. (2008) Bridging the gap between prevention research and practice: The interactive systems framework for dissemination and implementation. Am J Comm Psychol 41: 171-181. doi: 10.1007/s10464-008-9174-z
[15]	16. Aarons GA, Sommerfeld DM, Walrath-Greene CM. (2009) Evidence-based practice implementation: The impact of public versus private sector organization type on organizational support, provider attitudes, and adoption of evidence-based practice. Implem Sci 4(1): 83.
[16]	17. Feldstein AC, Glasgow RE. (2008) A practical, robust implementation and sustainability model (PRISM) for integrating research findings into practice. Jt Comm J Qual Patient Saf 34(4):228-243.
[17]	18. Dobbins M, Hanna S, Ciliska D, et al. (2009) A randomized controlled trial evaluating the impact of knowledge translation and exchange strategies. Implem Sci 4(1): 61.
[18]	19. McCormack B, McCarthy G, Wright J, et al. (2009) Development and testing of the Context Assessment Index (CAI). Worldviews Evidence-based Nurs 6(1): 27-35.
[19]	20. Estabrooks CA, Squires JE, Cummings GG, et al. (2009) Development and assessment of the Alberta Context Tool. BMC Health Serv Res 9(1): 234.
[20]	21. Holt DT, Armenakis AA, Feild HS, et al. (7) Readiness for organizational change: the systematic development of a scale. J Appl Behav Sci 43(2): 232-255.
[21]	22. Jacobs JA, Clayton PF, Dove C, et al. (2012) A survey tool for measuring evidence-based decision making capacity in public health agencies. BMC Health Serv Res 12: 57. doi: 10.1186/1472-6963-12-57
[22]	23. Kajermo KN, Boström AM, Thompson DS, et al. (2010) The BARRIERS scale—the barriers to research utilization scale: A systematic review. Implem Sci 5(1): 32.
[23]	24. Ohman-Strickland PA, John Orzano A, Nutting PA, et al. (2007) Measuring organizational attributes of primary care practices: development of a new instrument. Health Serv Res 42(3 Pt1):1257-1273.
[24]	25. Aarons GA. (2005) Measuring provider attitudes toward evidence-based practice: consideration of organizational context and individual differences. Child Adolesc Psychiatr Clin N Am 14(2):255-271.
[25]	26. PASW STATISTICS 18. 0 Command syntax reference [computer program]. (2011) Chicago: SPSS Inc.
[26]	27. Hanson N. (2011) Defining the children's hospital role in child maltreatment. 2nd ed. Alexandria:National Association of Children's Hospitals and Related Institutions.
[27]	28. Shelton D. (2009) Community health needs assessment survey. Alexandria: National Association of Children's Hospitals and Related Institutions.
[28]	29. Children's Hospital Association. (2012) Survey findings children's hospitals child abuse service. Available from: http://www. childrenshospitals. org.

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