Over 1.5 million students in the United States experience homelessness. These students are entitled to educational support through the Education for Homeless Children and Youth program. However, many homeless students are not identified and therefore never receive this support. Across 1,732 local education agencies in New Jersey, New Mexico, and New York, we conducted a randomized controlled trial of increased email communication incorporating behavioral insights targeting homeless liaison staff in order to increase the identification of homeless students. The intervention had an impact on the mean number of identified homeless students among the treatment local education agencies (3.62, 90% CI [0.32, 6.92], p = .07). The impact remained when outliers with high leverage were removed (1.51 CI [0.24, 2.79], p = .05). Within this sample, our analysis indicates that more than 3,000 additional homeless students were identified with a low-cost, low-intensity, behavioral intervention during the second semester.
AnzeloneC.YuJ.SubediP. (2018). Using behavioral insights to increase participation in social services programs: A case study (No. OPRE Report 2018-73.). Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
2.
ArielyD. (2010). The upside of irrationality: The unexpected benefits of defying logic at work and at home (Reprint ed.). HarperCollins e-books.
CunninghamK. A. (2014). A question of priorities: A critical investigation of the McKinney-Vento Act. Critical Questions in Education, 5(3), 218–232.
6.
DabbishL. A.KrautR. E. (2006, November4–8). Email overload at work: An analysis of factors associated with email strain [Paper presentation]. 2006ACM Conference on Computer Supported Cooperative Work, Banff, Alberta, Canada. https://doi.org/10.1145/1180875.1180941
7.
EmmonsD. L.MazzuchiT. A.SarkaniS.LarsenC. E. (2018). Mitigating cognitive biases in risk identification: Practitioner checklist for the aerospace sector. Defense AR Journal, 25(1), 52–93. https://doi.org/10.22594/dau.16-770.25.01
8.
EpplerM. J.MengisJ. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society, 20(5), 325–344. https://doi.org/10.1080/01972240490507974
9.
FantuzzoJ. W.LeBoeufW. A.ChenC.-C.RouseH. L.CulhaneD. P. (2012). The unique and combined effects of homelessness and school mobility on the educational outcomes of young children. Educational Researcher, 41(9), 393–402. https://doi.org/10.3102/0013189X12468210
10.
GlobalSurg Collaborative. (2019). Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy. British Journal of Surgery, 106(2), e103–e112.
11.
HeybachL. M. (2009). Advocacy and obstacles in the education of homeless children and youth in Illinois. Public Interest Law Reporter, 14(3), 281–293.
12.
IngramE. S.BridgelandJ. M.ReedB.AtwellM. (2016, June). Hidden in plain sight: Homeless students in America’s public schools. Civic Enterprises and Hart Research Associates.
13.
JerejianA. C.ReidC.ReesC. S. (2013). The contribution of email volume, email management strategies and propensity to worry in predicting email stress among academics. Computers in Human Behavior, 29(3), 991–996. https://doi.org/10.1016/j.chb.2012.12.037
14.
KahnemanD. (2011). Thinking, fast and slow. Farrar, Straus & Giroux.
15.
LinW.GreenD. P.CoppockA. (2016). Standard operating procedures for Don Green’s lab at Columbia (Version 1.05).
16.
LowJ. A.HallettR. E.MoE. (2017). Doubled-up homeless: Comparing educational outcomes with low-income students. Education and Urban Society, 49(9), 795–813. https://doi.org/10.1177/0013124516659525
17.
LurieN. H. (2004). Decision making in information-rich environments: The role of information structure. Journal of Consumer Research, 30(4), 473–486. https://doi.org/10.1086/380283
18.
MalhotraN. K. (1982). Information load and consumer decision making. Journal of Consumer Research, 8(4), 419–430. https://doi.org/10.1086/208882
National Center for Education Statistics. (2019). Study of the Title I, Part A grant program mathematical formulas (Vol. 2020).
21.
National Center for Homeless Education. (2018). Serving students experiencing homelessness under Title I, Part A.
22.
New York State Education Department. (2018). 2019-2022 McKinney-Vento Grant program overview.
23.
ObradovićJ.LongJ. D.CutuliJ. J.ChanC.-K.HinzE.HeistadD.MastenA. S. (2009). Academic achievement of homeless and highly mobile children in an urban school district: Longitudinal evidence on risk, growth, and resilience. Development and Psychopathology, 21(2), 493–518. https://doi.org/10.1017/S0954579409000273
24.
Office of Inspector General. (2018). New York State’s and selected districts’ implementation of selected Every Student Succeeds Act requirements under the McKinney-Vento Homeless Assistance Act. U.S. Department of Education.
25.
PeetzJ.BuehlerR.WilsonA. (2010). Planning for the near and distant future: How does temporal distance affect task completion predictions?Journal of Experimental Social Psychology, 46(5), 709–720. https://doi.org/10.1016/j.jesp.2010.03.008
26.
ReinkeK.Chamorro-PremuzicT. (2014). When email use gets out of control: Understanding the relationship between personality and email overload and their impact on burnout and work engagement. Computers in Human Behavior, 36, 502–509. https://doi.org/10.1016/j.chb.2014.03.075
27.
ShapiroE. (2018, October15). Homelessness in New York public schools is at a record high: 114,659 students. The New York Times.
28.
Social and Behavioral Sciences Team. (2016). Annual report. The Office of Science and Technology Policy.
29.
SonntagA.ZizzoD. J. (2015). On reminder effects, drop-outs and dominance: Evidence from an online experiment on charitable giving. PLOS ONE, 10(8), e0134705. doi:10.1371/journal.pone.0134705
30.
StichJ.-F.TarafdarM.StaceyP.CooperC.L. (2019). E-mail load, workload stress and desired e-mail load: A cybernetic approach. Information Technology & People, 32(2), 430–452. https://doi.org/10.1108/ITP-10-2017-0321
31.
TsirosM.MittalV. (2000). Regret: A model of its antecedents and consequences in consumer decision making. Journal of Consumer Research, 26(4), 401–417. https://doi.org/10.1086/209571
U.S. Department of Education. (2019). ED data express. Washington, DC: U.S. Department of Education.
34.
VickersA. J.AltmanD. G. (2001). Analysing controlled trials with baseline and follow up measurements. BMJ, 323(7321), 1123–1124. https://doi.org/10.1136/bmj.323.7321.1123
35.
WrightM. C.BorbollaD.WallerR. G.Del FiolG.ReeseT.NesbittP.SegallN. (2019). Critical care information display approaches and design frameworks: A systematic review and meta-analysis. Journal of Biomedical Informatics, 3, 100041. https://doi.org/10.1016/j.yjbinx.2019.100041
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