A 2-1-1 research collaboration: participant accrual and service quality indicators.

American Journal of Preventive Medicine(2012)

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Abstract
Background: In times of crises, 2-1-1 serves as a lifeline in many ways. These crises often cause a spike in call volume that can challenge 2-1-1's ability to meet its service quality standards. For researchers gathering data through 2-1-1s, a sudden increase in call volume might reduce accrual as 2-1-1 has less time to administer study protocols. Research activities imbedded in 2-1-1 systems may affect directly 2-1-1 service quality indicators. Purpose: Using data from a 2-1-1 research collaboration, this paper examines the impact of crises on call volume to 2-1-1, how call volume affects research participant accrual through 2-1-1, and how research recruitment efforts affect 2-1-1 service quality indicators. Methods: t-tests were used to examine the effect of call volume on research participant accrual. Linear and logistic regressions were used to examine the effect of research participant accrual on 2-1-1 service quality indicators. Data were collected June 2010-December 2011; data were analyzed in 2012. Results: Findings from this collaboration suggest that crises causing spikes in call volume adversely affect 2-1-1 service quality indicators as well as accrual of research participants. Administering a brief (2-3 minute) health risk assessment did not affect service quality negatively, but administering a longer (15-18 minute) survey had a modest adverse effect on these indicators. Conclusions: In 2-1-1 research collaborations, both partners need to understand the dynamic relationship among call volume, research accrual, and service quality and adjust expectations accordingly. If research goals include administering a longer survey, increased staffing of 2-1-1 call centers may be needed to avoid compromising service quality. (Am J Prev Med 2012;43(6S5):S483-S489) (C) 2012 American Journal of Preventive Medicine
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Key words
risk assessment,information services,disasters,linear models,data collection
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