Essay Grade

J Child Fam Stud
DOI 10.1007/s10826-017-0987-y
ORIGINAL PAPER
Trauma-informed Temporary Assistance for Needy Families
(TANF): A Randomized Controlled Trial with a Two-Generation
Impact
Layla G. Booshehri1 Jerome Dugan1 Falguni Patel2 Sandra Bloom2
Mariana Chilton 2
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© The Author(s) 2017. This article is an open access publication
Abstract Temporary Assistance for Needy Families
(TANF) has limited success in building self-sufficiency, and
rarely addresses exposure to trauma as a barrier to
employment. The objective of the Building Wealth and
Health Network randomized controlled trial was to test
effectiveness of financial empowerment combined with
trauma-informed peer support against standard TANF programming. Through the method of single-blind randomization we assigned 103 caregivers of children under age six
into three groups: control (standard TANF programming),
partial (28-weeks financial education), and full (same as
partial with simultaneous 28-weeks of trauma-informed
peer support). Participants completed baseline and followup surveys every 3 months over 15 months. Group response
rates were equivalent throughout. With mixed effects analysis we compared post-program outcomes at months 9, 12,
and 15 to baseline. We modeled the impact of amount of
participation in group classes on participant outcomes.
Despite high exposure to trauma and adversity results
demonstrate that, compared to the other groups, caregivers
in the full intervention reported improved self-efficacy and
depressive symptoms, and reduced economic hardship.
Unlike the intervention groups, the control group reported
increased developmental risk among their children.
Although the control group showed higher levels of
employment, the full intervention group reported greater
* Mariana Chilton
mmc33@drexel.edu
1
College of Nursing & Health Professions, Drexel University, 1601
Cherry Street, Philadelphia, PA 19102, USA
2
Dornsife School of Public Health, Drexel University, 3600 Market
Street, 7th Floor, Philadelphia, PA 19104, USA
earnings. The partial intervention group showed little to no
differences compared with the control group. We conclude
that financial empowerment education with traumainformed peer support is more effective than standard
TANF programming at improving behavioral health, reducing hardship, and increasing income. Policymakers may
consider adapting TANF to include trauma-informed
programming.
Keywords TANF Randomized controlled trial Twogeneration Depression Trauma
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Introduction
The Temporary Assistance for Needy Families program
(TANF) is meant to help low income caregivers gain
employment skills, secure employment and reach selfsufficiency. However, after 20 years of research on the
impacts of TANF, it is clear that it falls short of helping
people enter the workforce and stay there, and that TANF
participants have serious behavioral health challenges that
affect their ability to reach self-sufficiency (Bryner and
Martin 2005; Dworsky and Courtney 2007; Martin and
Caminada 2011). In order to receive TANF, caregivers with
young children under age six are required to participate in
20 “work hours” per week that may include job search,
training, or other programming. However, evidence shows
that the majority of such programs do not address the wellbeing of families, nor are there incentives to help caregivers
find steady well-paying opportunities (Corcoran et al. 2004;
Danziger 2010; Hildebrandt and Stevens 2009; Kaplan et al.
2005). In many instances, TANF participants may get jobs,
J Child Fam Stud
but do not succeed in keeping them, only to return to TANF
again (Hildebrandt and Kelber 2012; Hildebrandt and Stevens 2009; Ziliak 2014).
Almost one third of TANF recipients have a worklimiting health condition, and high rates of exposure to
violence and adversity in their families and communities
(Cheng 2013; Kennedy 2006; Lown et al. 2006). Adverse
childhood experiences (ACEs) consisting of physical and
emotional abuse and neglect, sexual abuse, and household
dysfunction, such as having a household member in prison
or witnessing domestic violence, are especially prevalent
among those receiving TANF (Cambron et al. 2014). The
original ACEs studies were conducted at Kaiser Permanente
in Southern California in two waves of data collection of
over 17,000 members of their Health Maintenance Organization, where adverse childhood experiences reported by
survey respondents were compared with current health
status and behaviors (Anda et al. 2006; Felitti et al. 1998).
Since then additional research studies have linked ACEs to
work-limiting conditions such as depression, cardiovascular
disease, autoimmune diseases, and food insecurity, while
damaging work prospects and stable income (Adams et al.
2013; Anda et al. 2008; Breiding et al. 2014; Cambron et al.
2015; Chilton et al. 2015; Danese et al. 2009; Dube et al.
2009; Staggs et al. 2007). High exposure to adversity
among TANF-eligible caregivers also has crippling effects
for academic achievement, parenting, employment, and
executive functioning capabilities (Evans et al. 2011; Liu
et al. 2013; Lu et al. 2008; Randles 2014). As an antidote,
building up social support and promoting resilience have
been shown to help interrupt the cycle of adversity (Kneipp
et al. 2011; Larkin et al. 2014; Vayshenker et al. 2016). In
addition, trauma-informed approaches that integrate
knowledge and awareness of how trauma affects cognitive,
social and emotional functioning, that seek to ensure that
operations and processes do not re-traumatize individuals
and incorporate group therapy approaches, have shown
promise for reducing depression and other trauma-related
symptoms (Bethell et al. 2014; Staub and Vollhardt 2008).
Based on this research, it is clear that trauma-informed
approaches may benefit TANF programming that seeks to
improve employment and self-sufficiency outcomes for low
income caregivers, many of whom have experienced
adverse childhood experiences, intimate partner violence
and community violence.
Despite the growing evidence that ACEs and community
violence are prevalent among low income caregivers (Anda
et al. 2006; Baglivio et al. 2014; Dreier et al. 2001; Peterson
and Krivo 2005), TANF is one of the public assistance
programs that has seen many attempts to improve outcomes
without attention to this growing scientific base. Numerous
randomized controlled trials have sought to improve
employment outcomes, and these interventions have met
with varied success (Falk 2012; Gueron and Rolston 2013).
The majority of these large trials have integrated approaches
that work with each client on an individual basis to connect
them with employment and/or education, and to reduce
participation in TANF (Gueron and Rolston 2013). Other
TANF RCT’s have sought to address health barriers through
referrals and home visits (Kneipp et al. 2011, 2013). To
date, however, there has been no intervention that has
sought to integrate trauma-informed practice and programming into TANF programming for caregivers who are
required to carry out work participation in order to receive
benefits. In addition, there is growing interest in how public
assistance programs affect both caregiver and child.
Recognizing that childhood experiences shape adult behavior, health and income, and, in turn, that caregivers’ health
and success shape the health and wellbeing of their young
children, government agencies have begun to adopt a twogeneration framework that integrates some attention to child
health (Chase-Lansdale and Brooks-Gunn 2014; Office of
Family Assistance PeerTA Network 2015; Shonkoff and
Fisher 2013).
This randomized controlled trial, The Building Wealth
and Health Network (The Network RCT), sought to reduce
economic hardship and behavioral health challenges associated with self-sufficiency among families with at least one
child under age 6 who were required to fulfill 20 h of work
participation each week. The analysis examines the impact
of The Network RCT 28-week curriculum on behavioral
health outcomes, economic hardship, and labor force participation among TANF participants by investigating three
questions. Our first aim was to identify if there was selection bias in follow up response rates that could lead to
erroneous differences in outcome measurements unrelated
to treatment assignment. Secondly, we hypothesized that
compared to the control group, intervention participants
would experience statistically significant improvements in
behavioral health, economic hardship, and labor market
outcomes after exposure to the intervention. Thirdly, we
hypothesized that compared to those that had low participation in the intervention, those that had greater exposure to
the interventions would report improvements in health,
hardship, and employment.
Method
Participants
Participants were primary caregivers of young children
under the age of six who were receiving temporary assistance for needy families and who are required to work at
least 20 h per week in order to receive these benefits.
Recruited by research staff at three county assistance
J Child Fam Stud
offices, the 103 participants were randomized into three
groups: 31 in the control group, 35 in the partial intervention, and 37 in the full intervention (Table 1). While basic
characteristics have already been reported in a previous
publication (Sun et al. 2016), we highlight across all groups
high rates of depressive symptoms ranging from 49 to 62%,
and at least one concern of child developmental risk ranging
from 12.9 to 22.9%, and over half of the participants
reporting moderate to severe food, housing, or utility
hardship. Additionally, over 90% of the sample was
unemployed at baseline.
Procedure
Table 1 Baseline characteristics of participants in the Building
Wealth and Health Network RCT, Philadelphia 2014–2015
Intervention groups
Control
(n = 31)
Partial
(n = 35)
Full (n = 37)
Mean SD
Mean SD
Mean SD
Child’s age (months)
30.9
(16.0)
29.1
(17.8) 31.3
(21.5)
Caregiver’s age
26.4
(4.3)
24.6
(5.6)
25.3
(5.5)
N
%
N
%
N
%
Female
30
(96.8)
32
(91.4) 35
(94.6)
Male
1
(3.2)
3
(8.6)
(5.4)
US Born
31
(100.0) 34
(97.1) 36
(97.3)
Foreign born
0
0
1
(2.9)
(2.7)
Black non-Hispanic
25
(80.6)
30
(85.7) 36
(97.3)
Hispanic
3
(9.7)
1
(2.9)
1
(2.7)
Other
3
(9.7)
2
(5.7)
0
0
White non-Hispanic
0
0
2
(5.7)
0
0
Caregiver gender
2
Immigration status
Through single-blind randomization participants were
assigned into each group. After consenting to participate,
participants completed baseline and follow-up surveys
every 3 months over 15 months and received additional
resources and the opportunity to speak with a social worker
if needed. We conducted a mixed effects analysis to compare baseline to post-program outcomes at months 9, 12,
and 15. In a separate analysis, we included class participation as a control to model impact of class participation on
outcomes for the partial and full groups. While this does not
necessarily indicate adherence (Fixsen et al. 2005), class
participation is an indication of amount of exposure to the
group process so essential to learning about finances,
sharing resources, and having opportunities to build selfefficacy. Recruitment and randomization processes were
successful, as there were no statistically significant differences in all characteristics and baseline outcomes by group.
Baseline outcomes from our sample show high rates of
exposure to ACEs and community violence. Almost 40% of
all caregivers reported experiences of four or more adversities in their childhood, including abuse, neglect and
household dysfunction; 64.7% of caregivers had seen a
seriously wounded person after an incident of violence, and
27.2% had seen someone killed. Caregivers reported on the
health and wellbeing of their children at baseline, and 36%
reported their young children to be at risk for cognitive,
social, and emotional delay, and almost half (48.5%) of
their fathers spent time in prison. A full description of
methods and baseline characteristics are outlined in a previous publication (Sun et al. 2016). The Network RCT ran
from June 2014 to December 2015; Clinical trial registration number NCT02577705.
We used Audio Computer-Assisted Self-Interview
(ACASI) software to administer all surveys regarding
demographics, economic hardship, behavioral health,
exposure to adversity and violence, and labor market outcomes. The research staff included measures validated by
the clinical literature within the survey to ensure the
external validity of the findings and tested glitches and
1
Race/ethnicity
Sexual orientation
Heterosexual
24
(77.4)
29
(82.9) 33
(89.2)
Bisexual
6
(19.4)
4
(11.4) 4
(10.8)
Gay or lesbian
1
(3.2)
2
(5.7)
0
Living with a partner 4
(12.9)
5
(14.3) 3
Married
0
0
0
0
Never married
27
(87.1)
29
(82.9) 31
(83.8)
Separated
0
0
1
(2.9)
(5.4)
Some high school or 7
grade school
(22.6)
11
(31.4) 12
(32.4)
High school grad or
GED
11
(35.5)
14
(40.0) 10
(27.0)
At least some college 13
and above
(41.9)
10
(28.6) 15
(40.5)
0
Marital status
1
2
(8.1)
(2.7)
Education
Chi-square and Wilcoxen-Mann Whitney tests analysis showed no
between-group differences, except for caregiver age (p = 0.07). See
(Sun et al. 2016) for more comprehensive review
readability with multiple respondents who are similar to
those in the study. On average, the baseline survey took
about 60 min to complete; each follow-up survey took
approximately 30 min. Each participant was compensated
$25 dollars for participating in each survey. With six total
surveys, participants had the opportunity to receive up to
$150. Responses for follow-up questionnaires in months 3
and 6 were excluded from the study sample, as they were
administered before the end of the 28-week curriculum.
The Network RCT included three groups: control, a
partial intervention and full intervention. The control group
J Child Fam Stud
received standard TANF programming consisting of 20 h
per week of scheduled supervised job training and job
search activities. The partial intervention group received
assistance in opening a credit union savings account, into
which their own savings were matched by The Network
RCT. It also included 28-weeks of financial empowerment
education in weekly 3-hour classes. Content focused on
identifying and harnessing internal and external resources to
take steps towards self-sufficiency with education that
included basic concepts of saving for education, housing,
entrepreneurial activities, and retirement, and improving
credit and reducing debt. The full intervention group
received the same financial empowerment education and
matched savings accounts as the partial group, with an
added 28-week 4-hour peer support group called SelfEmpowerment Groups. The group name drew from the
Sanctuary Model®, a trauma-informed approach to social
services (Bloom and Sreedhar 2008). The curriculum drew
on key components from the model’s S.E.L.F. framework
by focusing on four domains: creating physical, psychological, social and moral safety (S), processing and managing
emotions (E), recognizing loss and letting go (L), and
developing goals for a sense of future (F). The language of
S.E.L.F. establishes a common framework that helps people
who have experienced adversity to work towards building a
stable foundation that supports their relationships with each
other, within their families and communities, and gives
opportunities for people to express their goals and potential
for success. Financial empowerment classes were led by a
facilitator contracted through a local financial services
organization. S.E.L.F. groups were led by two trained peer
group facilitators.
Measures
This study examines measures of family behavioral health
(depression, self-efficacy, and child developmental risk),
economic hardship (hardship index), and labor market
outcomes (employment status, earnings).
Depressive symptoms were measured using the validated
10-item Center for Epidemiologic Studies Depression Scale
(CES-D)(Kohout et al. 1993; Radloff 1977). Which is
reliable and consistent with the original version across a
wide variety of populations (Hann et al. 1999; Zhang et al.
2012). Each item measures depressive symptoms on a 3point scale (30 points total). Higher scores reflect greater
depressive symptoms; a score of 10 or more is an indication
of clinical depression.
Ability to manage stress, and capacity to address challenges is measured using the 10-item General Self-Efficacy
Scale (GSE) which has strong validity and reliability across
numerous populations including low income caregivers
(Scholz et al. 2002; Schwarzer and Jerusalem 1995). Each
item represents a measurement of an individual’s ability to
deal with different demanding situations on a 4-point scale
(38 points total); higher scores reflect a participant’s greater
ability to deal with demanding situations.
Child’s developmental risks were measured using the 10item Parent’s Evaluation of Developmental Status Scale
(PEDS) (Glascoe 1998b). Each item measures developmental risk on a 3-point scale; only affirmative responses to
developmental risk questions based on child’s age are tallied
(10 points total). These data are used to construct a developmental risk indicator, equal to 1 if one or more developmental risks are reported, and 0 otherwise. PEDS has
been validated with many disadvantaged US populations,
and has a sensitivity of 91–97% and specificity of 73–86%
(Glascoe 1998c). One or more developmental risks reported
by the parent, are associated with significant disability in
adult life (Glascoe 1998a, 2003; Glascoe and Marks 2011).
We measured economic hardship with an index that
aggregates responses from three validated measures: the U.
S. Household Food Security Survey Module (HFSSM), an
energy security survey, and housing security survey. Each
construct generates 3 mutually exclusive categories to
capture levels of material hardship in the previous 3 months.
The HFSSM is a validated 18-item scale developed by U.S.
Department of Agriculture to measure household food
insecurity, meaning the lack of access to enough food for an
active and healthy life for the household and/or children
(Bickel et al. 2000), which as excellent reliability ranging
from 0.86 to 0.93 (Carlson et al. 1999). Households were
coded as food secure, low food secure, very low food
secure. Because food insecurity is related to other forms of
hardship, we combined food insecurity with energy and
housing insecurity based on previous research (Frank et al.
2010). Energy insecurity was coded as energy secure (no
threatened or actual utility disconnections, no unheated/
uncooled days, and no use of a cooking stove for heating),
moderate energy insecurity (threatened utility disconnection
because of nonpayment), or severe energy insecurity
(unheated or uncooled day because of nonpayment, actual
utility disconnection, and/or heating the residence with a
cooking stove). Housing insecurity was categorized as
housing secure (≤1 move in previous year and not crowded
or doubled up), moderate housing insecurity (household is
crowded and/or doubled up and has ≤1 move), or severe
housing insecurity (household is crowded and/or doubled
up and has moved ≥2 times). Crowding was defined as 42
people per bedroom and doubling up as a positive answer to
the following question, adapted from the US Census: “Are
you temporarily living with other people even for a little
while because of economic difficulties?” (Cutts et al. 2011).
Cumulative hardship index scores ranged from 0 to 6, with
food, housing, and energy each contributing a possible
score of 0 (secure), 1 (moderately insecure), or 2 (severely
J Child Fam Stud
insecure) to generate scores indicating no hardship (score of
0 = 0), moderate hardship (scores of 1–3 = 1), or severe
hardship (scores of 4–6 = 2).
Labor market outcomes include self-reported current
employment status and hourly earnings. In regression analysis, hourly earnings are transformed into logs to address
skewness.
Response rates were 50% at month 9 (n = 52), 50% at
month 12 (n = 53), and 45% at month 15 (n = 46). We
conducted a rank test of the independence of response rates
across groups by follow-up months, and found no significant differences in the distribution of treatment assignment over time (p = 0.9253).
Data Analyses
Table 2 Characteristics of participants in the Building Wealth and
Health Network RCT, over course of study, Philadelphia 2014–2015
Descriptive statistics summarize respondent characteristics
and outcomes across intervention groups at baseline and
identify that across all follow-up periods there were
equivalent response rates. We analyzed differences in
response profiles between treatment groups using multivariate linear mixed effects modeling, with participant as a
random effect and time of assessment (baseline and 9, 12,
and 15 months) and treatment group indicators (control,
partial, full) as fixed effects. Other control variables in the
model include gender, race/ethnicity, educational attainment, exposure to adversity and violence, and the interaction between time of assessment and class completion. In a
separate analysis of the partial and full intervention participants only, we included class attendance to measure
effects of class participation on participant outcomes. Least
squares means were calculated using the mixed effects
analysis and differences are reported across time and
groups.
We chose mixed effects models over generalized linear
models (GLM) or generalized estimating equation models
(GEE) not only for their ability to control for the fixed
effects that influence these changes in outcomes, but also
for their ability to model correlation between measurements
of the same participant through the inclusion of a random
effect (Gardiner et al. 2009). Further, GLM has the strength
of generating consistent estimates of regression parameters
in the presence of data missing at random and non-ignorable
missing data, which avoids the need to utilize complete case
data to generate consistent coefficient estimates (Ibrahim
et al. 2005). As per convention for studies with small
sample size, p-value o 0.10 was considered to indicate
significant differences between subgroups.
Time periods
Baseline
(n = 103)
Month 9
(n = 52)
Month 12 Month 15
(n = 53)
(n = 46)
N
N
N
%
%
%
N
%
Intervention group
Control
31
(30.1) 19 (36.5) 17 (32.1) 14 (30.4)
Partial
35
(34.0) 15 (28.8) 18 (34.0) 15 (32.6)
Full
37
(35.9) 18 (34.6) 18 (34.0) 17 37.0
Female
97
(94.2) 48 (92.3) 49 (92.5) 42 (91.3)
Male
6
(5.8)
Caregiver gender
4
(7.7)
4
(7.5)
4
(8.7)
Immigration status
US Born
101 (98.1) 50 (96.2) 51 (96.2) 45 (97.8)
Foreign born
2
(1.9)
Black nonHispanic
91
(88.3) 47 (90.4) 49 (92.5) 42 (91.3)
Hispanic
5
(4.9)
1
(1.9)
1
(1.9)
1
Other
5
(4.9)
2
(3.8)
2
(3.8)
2
(4.3)
White nonHispanic
2
(1.9)
2
(3.8)
1
(1.9)
1
(2.2)
2
(3.8)
2
(3.8)
1
(2.2)
Race/ethnicity
(2.2)
Sexual orientation
Heterosexual
86
(83.5) 46 (88.5) 47 (88.7) 41 (89.1)
Bisexual
14
(13.6) 6
(11.5) 6
(11.3) 5
(10.9)
Gay or lesbian
3
(2.9)
0
0
0
0
Living with a
partner
12
(11.7) 5
(9.6)
7
(13.2) 5
(10.9)
Married
1
(1.0)
(5.8)
2
(3.8)
(4.3)
Never married
87
(84.5) 42 (80.8) 43 (81.1) 37 (80.4)
Separated
3
(2.9)
0
0
Marital status
3
2
(3.8)
1
(1.9)
2
2
(4.3)
Education
Results
Aside from caregiver age, where the partial intervention
group was slightly younger than the other groups (p =
0.07), there were no statistically significant differences in
participant demographics observed, suggesting successful
randomization. We display response rates and basic characteristics of the study sample in Table 2 for survey participation from baseline and follow-up months 9, 12, and 15.
Some high
30
school or grade
school
(29.1) 15 (28.8) 14 (26.4) 14 (30.4)
High school
grad or GED
35
(34.0) 16 (30.8) 15 (28.3) 14 (30.4)
At least some
college and
above
38
(36.9) 21 (40.4) 24 (45.3) 18 (39.1)
The Cochran-Armitage and Jonckheere-Terpstra tests showed no
differences in the distribution of characteristics across time
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Table 3 Mixed effects analysis of behavioral health, economic hardship, and labor market outcome changes in the control, partial and full
intervention groups in the Building Wealth and Health Network RCT, Philadelphia 2014–2015
Assessment perioda
Measure
Baseline
LSM
9 mo.
LSM
9 mo. vs Baseline
p-value
12 mo.
LSM
12 mo. vs Baseline
p-value
15 mo.
LSM
15 mo. vs Baseline
p-value
Groupb
p-value
Adult depressive symptoms
Control group
10.66
9.55
0.3998
11.55
0.5134
12.83
0.1349
Partial intervention
9.02
9.46
0.3689
9.97
0.9712
11.36
0.9215
0.4098
Full intervention
9.78
10.36
0.3085
10.26
0.8036
8.65
0.0640
0.0154
Control group
31.89
29.05
0.0589
30.36
0.3212
31.10
0.6293
Partial intervention
29.59
29.15
0.2237
30.89
0.1438
32.15
0.1064
0.5903
Full intervention
31.90
32.98
0.0388
32.39
0.2950
32.62
0.4537
0.4170
Adult self-efficacy
Child’s developmental risk
Control group
0.10
0.31
0.0680
0.10
0.9507
0.09
0.8882
Partial intervention
0.23
0.29
0.5741
0.14
0.4239
0.17
0.6031
0.5575
Full Intervention
0.11
0.19
0.4568
0.28
0.1321
0.27
0.1612
0.1883
Control group
2.39
2.16
0.5590
2.60
0.6115
2.27
0.7868
Partial intervention
2.42
2.12
0.4690
1.97
0.2457
2.04
0.3479
0.6663
Full intervention
2.59
2.56
0.9557
1.86
0.0640
2.19
0.3268
0.8783
Control group
0.12
0.43
0.0068
0.61
o0.0001
0.38
0.0384
Partial intervention
0.08
0.56
0.2511
0.57
0.9751
0.48
0.3508
0.4867
Full intervention
0.04
0.43
0.5951
0.44
0.5637
0.51
0.1570
0.3413
Control group
2.40
2.26
0.5998
2.18
0.4307
2.45
0.8720
Partial intervention
2.14
2.14
0.6773
2.18
0.4300
2.17
0.9578
0.0793
Full intervention
2.01
2.25
0.2471
2.37
0.0857
2.43
0.2853
0.8769
Hardship index
Employment
Earningsc
a
All values are least squares means. Statistically significant p values at p o 0.10 are shown in bold
b
Group difference at month 15. The excluded category is the control group
c
The mixed effects model was estimated in the log of earnings, but the least squares estimates of earnings are reported for readability. The p values
reported in this section are from the log of earnings mixed effects analysis
Behavioral health, hardship, and labor market outcomes
are displayed in Table 3. Participants in the full intervention
experienced statistically significant declines in depressive
symptoms by month 15 compared to baseline (−1.13
points; p = 0.0640) and this decline is significantly lower
compared to the control group at month 15 (p = 0.0154).
Neither participants in the control group nor the partial
intervention experienced any statistically significant changes in depressive symptoms. Compared to the baseline, the
full intervention experienced an increase in self-efficacy at
month 9 (1.08 points; p = 0.0388). During the same time
period, the control group experienced a statistically significant decline in self-efficacy at month 9 (−2.84 points; p
= 0.0589). Neither participants in the partial or full intervention experienced statistically significant changes in child
developmental risks. However, among the control group,
compared to the baseline, there was a statistically significant
increase in the probability of reporting child development
risks at month 9 (21%; p = 0.0680).
Compared to baseline, participants in the full intervention experienced statistically significant declines in economic hardship by month 12 (−0.73 points, p = 0.0640).
Neither the control nor partial intervention reported statistically significant changes in hardship throughout the study
period.
The control group experienced statistically significant
increases in employment in every follow-up period. In
particular, employment increased by 26 percent (p =
0.0384) by month 15. Neither the partial nor full intervention reported significant changes in employment over the
study period. However, compared to baseline, the full
intervention experienced a statistically significant increase
J Child Fam Stud
Table 4 Mixed effects analysis of the impact of class attendance on behavioral health, economic hardship, and labor market outcomes in the
Building Wealth and Health Network RCT, Philadelphia 2014–2015
Dependent variables
Depressive symptoms
Partial
Attendance rate (%)a
Full
Full
Self-efficacy
Partial
Full
−0.0526
−0.0276
−0.0001
−0.0048
0.0234
0.0463
P = 0.1283
P = 0.3916
P = 0.9563
P = 0.0284
P = 0.5754
P = 0.1048
Full
Log of
earnings
Partial
Full
Hardship index
Partial
Attendance rate (%)a
Development risk
Partial
Employment
Full
Partial
−0.0061
−0.0069
0.0030
0.0048
0.0041
−0.00204
P = 0.2853
P = 0.2174
P = 0.2849
P = 0.0443
P = 0.2227
P = 0.3341
Significant p values (p o 0.10) are shown in bold
a
At the end of the 28-week education program, the average attendance rate for the partial intervention group was 26.0% and for the full
intervention group was 23.6%
in earnings by month 12 (p = 0.0857), while the control and
partial intervention groups reported no significant changes
in hourly earnings.
The average class attendance for the partial and full
intervention at the end of the 28-week education program
was 26.0 and 23.6%, respectively (Table 4). This leads to an
important question of whether increasing exposure to either
intervention program could lead to increased positive
impact on participant outcomes. Increased class participation was not associated with statistically significant changes
in adult depressive symptoms, child development risk, selfefficacy, economic hardship, employment, or earnings for
the partial intervention group. However, increased class
attendance was associated with statistically significant
improvements in some outcomes for the full intervention. In
particular, the mixed effects coefficient estimates for class
participation presented in Table 4 demonstrate that
increasing class attendance by one percent was associated
with decreases in developmental risks for the participant’s
youngest child (coefficient estimate: −0.0048, p = 0.0284),
non-significant increases in self-efficacy (coefficient estimate: 0.0463, p = 0.1048), and increased probability of
employment (coefficient estimate: 0.0048, p = 0.0443).
Discussion
Results demonstrate that the randomization was effective.
Our survey response rate ranged from 45–50%, which is
higher than average for at risk low-income caregivers
(Western et al. 2016). There were no significant differences
by group in terms of baseline and follow-up characteristics
and survey response rate. This suggests that the results,
where groups are compared both within and across groups,
are likely due to the intervention itself.
The Network RCT intervention demonstrated important
and diverse findings. Changes in health, economic hardship
and employment varied at each follow-up time point. This is
reflective overall that behavioral and economic changes do
not happen simultaneously and that effects may change over
time. The improvements for caregiver in self-efficacy and
depression are promising, not only because they demonstrate improvements in emotional and behavioral wellness,
but also because of their positive impacts on employment.
The demonstrated improvements in self-efficacy by month 9
for the full intervention suggests that an underlying challenge in securing and maintaining employment can be
addressed and that it may have positive impacts on
employment. Self-efficacy is associated with greater motivation and job satisfaction, self-leadership strategies and job
performance, which are all necessary for success in the
work force (Cherian and Jacob 2013). Length of time in the
full intervention was associated with improvements in selfefficacy, though these results were only significant at the
90% confidence level. For the control group, self-efficacy
reduced at month 9, and stayed lower than the other groups
in the partial and full intervention, and results suggest that
the longer someone participated in the peer support group,
the more likely self-efficacy improved. The ability of the
program to reduce depressive symptoms was most effective
for the full intervention group by month 15, suggesting that
a shift in mental health takes a significant amount of time.
Trauma-informed group therapy is known to have positive
effects on behavioral health and parenting practices (Murphy et al. 2015). The Network RCT’s weekly sessions had a
significant clinical impact suggesting profound health
effects for non-medical, trauma-informed interventions.
This is especially important because any type of reduced
depression is known to have positive effects on helping
individuals to secure and maintain employment
J Child Fam Stud
(Schoenbaum et al. 2002). Both improved self-efficacy and
reduced mental health are known to improve parenting
practices, and therefore have an impact on the wellbeing of
children (Kohlhoff and Barnett 2013).
This two-generation effect is reflec