Introduction
Attention Deficit Hyperactivity Disorder (ADHD) is currently, in the United States, the most diagnosed form of psychopathology in the preschool years (Armstrong and Nettleton
2004). A recent study (Egger et al.
2006) estimated the prevalence of ADHD at 3.3% in the preschool population and reported that preschoolers with ADHD experience significant functional and psychosocial impairment. Researchers have found that children diagnosed with ADHD in the preschool years are at great risk for poorer outcomes (e.g., Egger et al.
2006; Willoughby et al.
2000).
The ADHD Spectrum and Risk for Diagnosis of ADHD
The conventional approach to assessment of ADHD is based on a
categorical
approach (
condition
“present”
or
“absent”) as defined by the criteria stated in the Diagnostic and Statistical Manual (DSM) of the American Psychiatric Association or the manual for the International Classification of Diseases (ICD) of the World Health Organization. In recent publications (Swanson et al.
2009,
2011), we proposed that psychiatric diagnosis for ADHD could be enhanced if a major paradigmatic shift occurred, resulting in conceptualization of ADHD as a spectrum disorder (the “
continuum
approach”), where diagnosed cases represent one extreme on the continuum of behavior manifested in the population. Our research has demonstrated that using an ADHD measurement scale derived from the continuum theory of ADHD produces a normal distribution of attention and behavioral regulation in a population-based sample (Lakes, Swanson, Riggs, Schuck, and Stehli, under review). In other words, individual capacities to regulate behavior and attend to tasks occur on a continuum in the population; this continuum approximates a normal curve, with some individuals showing exceptional abilities and with others demonstrating serious deficits. From this perspective, a preschooler may be at risk for a diagnosis of ADHD if he or she has high levels of inattention and hyperactivity that approach the extreme end of the continuum, where symptom severity and impairment warrant a clinical diagnosis.
Interventions for Preschoolers with ADHD Symptoms
The interventions currently used to treat preschoolers with ADHD symptoms include pharmacological and nonpharmacological approaches. In recent years, psychopharmacological treatment for the preschool population has tripled (Zito et al.
2000), in spite of documented reluctance and uncertainties related to the use of such medications and their side effects on such young children (Volkow and Insel
2003; Zito et al.
2000). It has been stated that this trend is likely due, at least in part, to the scarcity of research regarding appropriate psychosocial interventions for preschool ADHD (Sonuga-Barke et al.
2006). It also has been argued that nonpharmacological interventions for preschool children should include parent education programs to address and reduce symptoms of ADHD (Sonuga-Barke et al.
2006; Tamm et al.
2005) and should occur early in development when prevention can be especially effective (Arons et al.
2002). Interventions for preschoolers may be more successful than those for school-age children because behavior is less entrenched and behavioral control is emerging as part of development (Keenan and Wakschlag
2000).
Theoretical Basis for Parent Interventions to Improve Symptoms in Children at Risk for ADHD
The biological basis for Attention Deficit Hyperactivity Disorder (ADHD) has been well established (see Swanson et al.
2007). Swanson et al. demonstrated that children with ADHD who had a genetic risk for ADHD (presence of the DRD4 7–repeat allele) differed from children with ADHD who did not have this risk allele. While the group without the allele had both behavioral and neuropsychological deficits, the group with the allele had only the behavioral deficits. This led the authors to propose that there are different etiologies of ADHD—one that involves environmental factors and results in a full syndrome, and another that involves a genetic predisposition toward ADHD, which the authors suggested might be a temperamental trait. They suggested that gene-environment interactions might explain the developmental course of ADHD, at least for the group of children with the risk allele.
Although the idea that parenting alone causes ADHD has not received support (Doyle
2004), previous research has documented associations between negative parenting and symptoms of ADHD and related conduct problems. Negative parenting, including coercive, intrusive, and restrictive practices, is associated with ADHD and comorbid problems (Doyle
2004), and positive parenting (e.g., praise, positive affect, warmth) has been shown to reduce the risk for conduct problems among children with ADHD (Chronis et al.
2007).
Belsky’s differential susceptibility hypothesis (Belsky
1997) proposes that children with difficult temperaments are more susceptible to the influence of parenting, at least when it comes to outcomes involving externalizing behavior. As others (e.g., Bradley and Corwyn
2008) have noted, this hypothesis is consistent with Rothbart’s (
2004) argument that sensitivity to environmental events is one of the pathways between temperament and childhood disorders. Bradley and Corwyn tested the differential susceptibility hypothesis, and found that “children with difficult temperaments were more affected by the kinds of parenting they received than children with average and easy temperaments” (p. 128). One exception was noted concerning harsh punishment: “although children with difficult temperaments were adversely affected by harsh parenting, the impacts on them were not significantly different from the impacts on other children” (p. 128).
Recent research has documented a gene-environment interaction that supports both Swanson et al’s (
2007) hypothesis and the differential susceptibility hypothesis. Sheese et al. (
2007) genotyped children between the ages of 18 and 21 months and observed them interacting with their caregivers. In their genetic analyses, the authors focused specifically on the presence or absence of the DRD4 7-R allele, which has been linked to ADHD. The child’s temperament was also measured, with a focus on sensation seeking, described by the authors as a temperamental variable related to high levels of activity and impulsiveness. Poor parenting quality predicted higher sensation seeking in children with the DRD4 7-R allele, but not in children without the allele. The authors concluded that the presence of the DRD4 7-R allele increases a child’s sensitivity to environmental influences such as parenting.
In summary, although parenting has not been shown to cause ADHD, there is now preliminary evidence that children who have a genetic predisposition toward hyperactivity and inattention may be more negatively impacted by less optimal parenting practices. For children with such risks, early parenting intervention has the potential to make a marked impact on the child’s development of attention and behavioral regulation (and, therefore, his or her risk for a later diagnosis of ADHD or comorbid conduct problems). Moreover, the theoretical basis for parenting intervention for preschool children with symptoms or diagnosis of ADHD is supported by prior research demonstrating positive changes in child behavior following parent intervention (see review by Daley et al.
2009). Parent intervention for preschoolers has the potential to reduce symptoms of ADHD (and, thereby risk of later diagnosis of ADHD) as well as to reduce the risk of comorbid conduct problems among children who have ADHD.
Challenges to Providing Parent Intervention: Early Identification and Intervention Implementation
Currently, a key challenge to treating preschool children at risk for ADHD (and related disorders) is that preschoolers are both under-identified and under-referred. According to one estimate, only a quarter of preschoolers with ADHD are referred for evaluation and treatment (Egger et al.
2006). Another common issue in the treatment of preschool children at risk for ADHD is that underserved populations are often less likely to receive treatment than majority populations. In the United States, researchers have indicated that older (4 and 5 years), white, middle class, and more impaired children represent the group most likely to receive treatment (e.g., Lavigne et al.
1998). It is well documented that in the United States, minorities suffer from mental health disparities, which put them at risk for being under-identified (US Department of Health, Human Services
1999). Therefore, it is of foremost importance for mental health professionals to employ intervention programs that include efforts to reduce barriers to treatment for diverse underserved populations. Previous reviews of parent training programs have urged researchers to study interventions in diverse samples (e.g., Valdez et al.
2005).
Studies also have shown a disproportionate participation rate in parent training programs by families who can most benefit from them (Reyno and McGrath
2006). Thus, significant challenges to clinicians implementing or recommending parent training programs for high-risk preschoolers include promoting parent participation (i.e., recruitment
and retention) as well as optimizing treatment outcomes (which will be dependent on recruitment and retention, but will be moderated by additional factors). Reyno and McGrath utilized meta-analysis to simultaneously examine predictors of treatment response in parent training participants and found moderate standardized effect sizes for low socioeconomic status (SES), severe pretreatment child difficulty, and maternal psychopathology. These findings suggest that, in addition to providing curricula designed to reduce risk for ADHD, parent-training interventions should include features to reduce dropout and improve outcomes, particularly for families with severe pretreatment child difficulty, low SES, and maternal psychopathology.
CUIDAR in Southern California provides
service
before
diagnosis (Tamm et al.
2005) using a culturally sensitive, community-based model of service delivery to provide parent education aimed at improving parent–child relationships and reducing child risk for ADHD. Through CUIDAR Community Parent Education (COPE: Cunningham et al.
1995) classes, parents learn about appropriate child development and positive parenting skills, and at the conclusion of intervention, their responses to intervention are assessed and documented. Many families indicate that their needs have been met (e.g., improvements have been noted or parents have realized that their child’s behavior was developmentally appropriate) and no further intervention is requested or recommended. Families continuing to experience difficulties receive a clinic referral (Tamm et al.
2005). The parent-training model utilized by CUIDAR and efforts to reduce premature dropout and improve treatment outcomes for families at risk are described further in this manuscript.
Traditional clinic-based programs have been shown to unavoidably possess certain barriers that may prevent families from utilizing needed services (Cunningham et al.
1995). These barriers include travel time, cost, childcare, geographical location, cultural barriers, conflicts with work schedules, and the stigma associated with attending mental health centers. CUIDAR addresses many of these barriers by providing childcare for all children in the family, providing meals for all participating family members during the classes, and providing classes in local community centers (e.g., schools and churches) at various times to increase access and reduce conflicts with work schedules. All CUIDAR services are provided in English and in Spanish and are free of charge to families. CUIDAR is advertised through local community centers, and educators and other community members are encouraged to promote upcoming classes to parents they know. The public funding that supports the CUIDAR programs requires that participation be open to all interested parents; therefore, although CUIDAR recruitment materials target preschool children with attention and hyperactivity difficulties, there are no specific inclusion criteria other than residence in the county where the program is being supported.
Our first description and preliminary evaluation of the CUIDAR program reported that parents used positive parenting skills more frequently and effectively and used physical punishment less frequently after completing the 10-week intervention (Tamm et al.
2005). In addition, Tamm et al. reported high levels of parent satisfaction with the program and reductions in child impulsive, oppositional, and social problems. Our second CUIDAR study (Lakes et al.
2009) reported results from the first replication of CUIDAR in another county of California and demonstrated that CUIDAR effectively served a population comparable to local demographics, with minorities and low-income families slightly over-represented (thus, having participation rates that do not reflect racial/ethnic disparities as demonstrated in public and private mental health programs in the same region); successfully recruited parents of children at risk for serious behavioral disorders; and produced high levels of parent satisfaction. Lakes et al. also observed improvements in child SDQ Total Difficulties scores and Conduct Problems scores. Although there are two published studies describing CUIDAR, the program is relatively new and has limited research support, particularly addressing outcomes and predictors of outcomes. The present study extends previous research by examining parent and child intervention outcomes and important predictors of outcomes. In addition, the present study is the first report of outcomes that includes a follow-up survey approximately one year after completion of CUIDAR.
Research Questions and Hypotheses: (1) Following
parent
participation
in CUIDAR, will
parents’
behaviors
improve
and
will
difficult
child
behaviors
decrease? We predicted that positive intervention outcomes would be observed at post intervention and sustained at follow-up. (2) Are
there
key
demographic (e.g., racial/ethnic) or
family
structure
factors
that
predict
treatment
outcomes? As noted earlier, poorer treatment participation and outcomes are associated with factors such as minority status and low socioeconomic status. Due to CUIDAR’s focus on access for minority and low-income families, we predicted that socioeconomic factors would have limited impact on outcomes. (3) Is
the
CUIDAR
model
effective
in
eliminating
common
barriers
to
treatment
intervention
for
an
under-served
population? We expected parents would report minimal barriers to participation, and there would be no significant differences in reported barriers between parents who completed the program and those who did not.
Results
FollowingparentparticipationintheCUIDARCommunityParentEducation (
COPE)
program:
willparents’behaviorsandattitudesbecomemorepositivetowardstheirchildren;
andwilldifficultchildbehaviorsdecrease? Means and standard deviations for the PSA are displayed in Table
2. A series of paired
t-tests were performed to examine whether the frequency of reported parenting behaviors significantly changed from pre to post intervention and were sustained at follow-up intervention. From pre to post intervention eight out ten parenting behaviors positively changed: praise/positive attention; ignoring problem behavior; using a star chart; reducing the use of physical punishment; rewarding for positive behavior; using transitional statements; using when-then statements; and planning ahead. From pre to follow-up intervention, differences in the use of transitional statements, planning ahead, and star charts were significant. Differences in the use of star charts were not in the predicted direction; for this variable only, the pre intervention mean for the follow-up sample was substantially different than for the full sample (M = 2.22 vs. M = 1.57). In response to an open-ended question, parents also reported that they learned important information about their children, which resulted in having more positive attitudes toward their child (e.g., understanding their child better, reducing the use of adverse parenting, having more patience with their child, and improving communication with their child).
Table 2
Means, Standard Deviations, and t-test results: parent behavior (n = 123)
Praise/positive attention | 3.34 (.78) | 3.52 (.65) | 3.35 (.92) | −2.58 | .011** | .25 | .00 | 1.0 | 0 |
Ignoring problem behavior | 1.98 (.80) | 2.31 (.76) | 2.16 (.93) | −4.05 | .000*** | .46 | −1.39 | .17 | .29 |
Star chart | 1.50 (.92) | 2.41 (1.10) | 1.57 (.93) | −7.59 | .000*** | .90 | 3.10 | .004** | −.66 |
Time-outs | 2.04 (.96) | 2.16 (.92) | 2.24 (.96) | −1.25 | .214 | .13 | .50 | .62 | −.09 |
Physical punishment | 1.61 (.68) | 1.40 (.67) | 1.28 (.74) | 3.11 | .002** | −.31 | 1.14 | .26 | −.25 |
Take away privileges | 2.02 (.78) | 2.07 (.80) | 2.00 (.78) | −.74 | .463 | .06 | .16 | .88 | −.04 |
Rewards | 2.42 (.93) | 2.75 (.75) | 2.59 (.83) | −3.46 | .001** | .39 | −1.14 | .26 | .28 |
Transitional statements | 2.06 (.87) | 2.79 (.91) | 2.62 (.92) | −6.97 | .000*** | .82 | −3.31 | .002** | .78 |
When-then statements | 2.60 (1.00) | 2.86 (.82) | 2.86 (.93) | −2.39 | .018* | .29 | −1.48 | .15 | .31 |
Planning ahead | 2.28 (.99) | 2.56 (.87) | 2.83 (.91) | −2.61 | .010* | .30 | −3.11 | .004** | .61 |
Means and standard deviations for SDQ subscales and Total Difficulties scores are listed in Table
3. Based on the United States SDQ normative reference group (
www.sdqinfo.org) the mean score for CUIDAR for Total Difficulties was at the 87th percentile. The means fell outside of the normal range on all subscales, with percentile rankings for problem behavior scales ranging from the 78th percentile to the 91st percentile. On the positive scale (Prosocial Behavior), the CUIDAR mean was at the 30th percentile.
Table 3
Means, Standard Deviations, and t-test results: child behavior (n = 154)
Emotional difficulties | 2.21 (2.00) | 1.77 (1.93) | 2.04 (2.01) | 2.82 | .006** | .26 | 2.17 | .036* | .38 |
Conduct problems | 3.34 (2.21) | 2.66 (2.19) | 2.61 (2.08) | 3.60 | .000*** | .30 | 5.08 | .000*** | .74 |
Hyperactivity/inattention | 4.56 (2.47) | 4.05 (2.59) | 4.09 (2.47) | 2.35 | .021* | .17 | 3.72 | .001*** | .50 |
Peer problems | 2.53 (1.89) | 2.30 (1.87) | 1.98 (1.74) | 2.71 | .008** | .25 | 2.48 | .017* | .42 |
Prosocial behavior | 7.30 (2.01) | 7.85 (1.95) | 8.25 (1.76) | −4.14 | .000*** | .33 | −5.53 | .000*** | .82 |
Total difficulties | 12.57 (6.10) | 10.84 (6.78) | 10.61 (5.76) | 4.00 | .000*** | .36 | 4.42 | .000*** | .71 |
The results of the paired
t-tests and the effect sizes (Cohen’s
d) are also reported in Table
3. Participant’s SDQ Total Difficulties scores significantly decreased from pre intervention to post intervention. In addition, there was a significant decrease in the Emotional Difficulties, Conduct Problems, Hyperactivity, and Peer Problems subscales, with a significant increase in the positive Prosocial Behavior scale. Though follow-up SDQ scores were available only for a smaller subset of those completing pre and post intervention SDQ scales, sustainability of the program’s effectiveness was supported with a significant decrease in participant’s SDQ Total Difficulties scores from pre intervention to follow-up intervention. As for pre to post, there also was a significant decrease in the scores from pre to follow-up intervention for Emotional Difficulties, Conduct Problems, Hyperactivity, and Peer Problems subscales, with the expected increase in Prosocial Behavior.
Aretherekeydemographicorfamilystructurefactorsthatpredicttreatmentoutcomes? The tests of pre to post intervention differences by ethnicity were low in power due to small numbers of participants in the African American (n = 12) and Caucasian (n = 20) groups. Consequently, any omnibus differences significant at the
p < .10 level were further investigated using Tukey Least Significant Difference post hoc tests (also selected due to low power). The results are reported in Table
4. As expected, the pre test covariates were significant in all results. Corrected post test score differences were near significant (
p < .10) for the main effect of ethnicity in the tests of SDQ Emotional Difficulties, Conduct Problems, and Hyperactivity. The pattern of differences in the LSD post-hoc tests indicated some significantly superior scores for Latino participants, their scores being significantly better than Caucasians for Emotional Difficulties and Conduct Problems, and better than African Americans for Hyperactivity.
Table 4
ANCOVA results for post SDQ’s by ethnicity corrected by Pre SDQ’s (n = 154)
Emotional difficulties | 34.49 | 1 | .000*** | .218 | – | – |
Ethnicity | 3.00 | 2 | .054* | .046 | – | – |
Hispanic to Caucasian | – | – | – | – | −.80 | .045* |
Hispanic to African American | – | – | – | – | −.79 | .105 |
Caucasian to African American | – | – | – | – | .011 | .985 |
Conduct problems | 58.23 | 1 | .000*** | .338 | – | – |
Ethnicity | 2.51 | 2 | .086 | .042 | – | – |
Hispanic to Caucasian | – | – | – | – | .90 | .049* |
Hispanic to African American | – | – | – | – | −.43 | .46 |
Caucasian to African American | – | – | – | – | −1.33 | .054* |
Hyperactivity/inattention | 99.22 | 1 | .000*** | .461 | – | – |
Ethnicity | 2.44 | 2 | .092 | .040 | – | – |
Hispanic to Caucasian | – | – | – | – | −.29 | .543 |
Hispanic to African American | – | – | – | – | −1.22 | .031* |
Caucasian to African American | – | – | – | – | −.93 | .172 |
Peer problems | 26.85 | 1 | .000*** | .192 | – | – |
Ethnicity | 1.41 | 2 | .250 | .024 | – | – |
Hispanic to Caucasian | – | – | – | – | −.68 | .105 |
Hispanic to African American | – | – | – | – | −.29 | .562 |
Caucasian to African American | – | – | – | – | .39 | .526 |
Prosocial behavior | 61.56 | 1 | .000*** | .353 | – | – |
Ethnicity | .44 | 2 | .646 | .008 | – | – |
Hispanic to Caucasian | – | – | – | – | −.19 | .626 |
Hispanic to African American | – | – | – | – | −.38 | .392 |
Caucasian to African American | – | – | – | – | −.19 | .724 |
Total difficulties | 47.55 | 1 | .000*** | .320 | – | – |
Ethnicity | 2.13 | 2 | .124 | .040 | – | – |
Hispanic to Caucasian | – | – | – | – | −1.39 | .289 |
Hispanic to African American | – | – | – | – | −3.0 | .061 |
Caucasian to African American | – | – | – | – | −1.61 | .401 |
Demographic and family patterns previously determined in the literature to have associations with children’s behavior and mental health were tested as potential predictors of the SDQ post-test scores. For each of these predictor variables that influenced post-intervention SDQ outcomes, potential differences by ethnicity also were analyzed. In the first analysis, whether or not the child was currently enrolled in pre-school predicted a near significant difference in the SDQ pro-social score. Ethnic groups were not different in reference to the probability that the child was or was not enrolled in preschool. Whether or not the child had ever been diagnosed with a medical condition significantly predicted post-test scores for Conduct Problems, Hyperactivity, and the Total Difficulties score. In all cases, having been diagnosed was associated with higher scores in these problem subscales. For this predictor variable, ethnic groups did differ in their relative probabilities of the child’s diagnostic status (χ² (2) = 22.59, p < .001). Standardized cell residuals (in z-scores) indicated a disproportionate over-representation of African Americans whose child had been diagnosed (z = 3.9), and an under-representation of Hispanics whose child had been diagnosed (z = −1.9).
ANOVA’s based upon parental education levels and SDQ outcomes all were insignificant. Gross family income (measured in categories) was not associated with any outcomes. Neither the age of the biological mother at the birth of the first child nor the total number of children in the family predicted SDQ outcomes, but the time in months that the primary parent/guardian had lived with the child during the previous year did correlate with Emotional Difficulties (r (136) = −.23, p = .007), Peer Problems (r (132) = −.17, p = .04), and Total Difficulties (r (123) = −.19, p = .04). Ethnic groups did not differ on the variable of months lived with child. A variable representing three types of family structure (biological mother and father both present, two parents/guardians present but not both biological, and only one parent/guardian biological or not present) was predictive of Emotional Difficulties (F (2,130) = 5.38, p = .006). Post-hoc tests indicated that the category of “both biological parents present” had better scores than either of the other two groups (p = .03 when compared to “two parents/guardians, and p = .005 when compared to “one parent/guardian”). Ethnic groups did differ on this predictor (χ² (4) = 29.06, p < .001), with African Americans under-represented in the two biological parents group (z = −3.0) and over-represented in the one parent/guardian group (z = 3.0). Latinos had larger than expected numbers in the both biological parent category (z = 1.5) and lower than expected observations in the one parent/guardian category (z = −1.6).
IstheCUIDARmodeleffectiveineliminatingcommonbarrierstotreatmentinterventionforanunder-servedpopulation? Means and standard deviations for barriers to participation are provided in Table
5. Participants were split into two groups (completers; non-completers). Multiple independent samples
t-tests were performed, and results confirmed no significant barriers between completers and non-completers. Non-completers were only included in evaluating barriers to participation and were not included in the preceding analyses.
Table 5
Independent samples t-tests: comparison of possible factors impacting participation between completers and non-completers (n = 69)
Transportation | 1.86 (1.32) | 1.68 (1.18) | .58 | .567 |
Location of class | 1.75 (1.28) | 1.64 (1.15) | .36 | .723 |
Time of class | 1.57 (1.07) | 2.16 (1.55) | −1.88 | .065 |
Comfort level with class | 1.45 (.98) | 1.48 (.87) | −.11 | .914 |
Perceived classes as beneficial | 1.59 (1.04) | 1.40 (.87) | .78 | .440 |
Enjoyed class sessions | 1.55 (1.11) | 1.40 (.87) | .57 | .574 |
Felt connected with the facilitator | 1.82 (1.21) | 1.44 (.92) | 1.36 | .179 |
Discussion
Our findings suggest that that substantial improvements in both parent and child behavior can be achieved and sustained for at least one year through participating in the COPE program offered by CUIDAR. Positive changes included the increased frequency of parental use of praise and positive attention as well as transitional and when-then statements. Additionally, parents reported that the classes helped them understand their child better, reduce the use of adverse parenting skills, and have more patience and improved communication with their child. Children exhibiting early difficulties with attention and behavior may be extremely sensitive to harsh parenting styles, which make it most critical for parents of these children to utilize positive parenting practices (Sonuga-Barke et al.
2006).
This evaluation study also documented improved child behavior. Parents reported a significant decrease in Emotional Difficulties, Conduct Problems, Hyperactivity/Inattention, and Peer Problems. Prosocial Behaviors significantly increased from pre to post intervention. At follow-up, parents again reported a decrease in Total Difficulties. More specifically, significant decreases were found for Emotional Difficulties, Conduct Problems, Hyperactivity/Inattention, and Peer Problems. Prosocial Behaviors significantly increased from pre to follow-up intervention.
We predicted that because of CUIDAR’s focus on access for minority and low-income families, socioeconomic factors would have limited impact on treatment outcomes. While many demographic factors had limited impact as predicted, intervention gains still were moderated by several factors, including the presence of a medical condition in the child (which was most frequently reported as asthma and allergies). In addition, involvement in out-of-home care (e.g., having been in foster care) and away from the parent during the previous year also predicted weaker treatment outcomes. Consistent with previous research (Frampton et al.
2008), family structure also predicted outcomes, with the strongest outcomes reported in families in which both biological parents were still in the home with the child. Latinos were disproportionately represented in the category of both parents in the home, and African-Americans were disproportionately represented in the category of single-parent homes.
Because the CUIDAR service delivery model employs strategies to reduce barriers to participation for low-income and minority populations, we expected parents to report minimal barriers to participation. None of the common barriers noted in previous research (e.g., Vega and Lopez
2001), such as awareness of services, health insurance, childcare, and transportation, were significant barriers in this study. Among the assessed barriers, the only difference between completers and noncompleters that neared significance was the time of class, suggesting that potential scheduling conflicts may have been a reason for the lack of participation among the latter group.
Limitations
Since participants were self-referred to the CUIDAR program, there may be a self-selection bias that could contribute to positive results. In this study, this limitation was unavoidable given the community-based nature of CUIDAR. Additionally, stipulations of grants that support CUIDAR require that services remain available to all residents with a child under the age of six, which means that random assignment, wait lists, or control groups were not permissible.
Another limitation in this study and ongoing challenge to CUIDAR is the modest percentage of participants who fully complete the program. However, these challenges are not unique to CUIDAR. Premature termination and high no-show rates are a key concern for mental health providers delivering services to families of children. A meta-analysis by Macharia et al. (
1992) reported that the average rate of noncompliance with scheduled patient appointments in 88 studies found in PsychLit and Medline was 42%. Another study specifically examining outpatient mental health clinics, found that 30 to 75% of patients do not keep their initial scheduled appointment, and that the no-show rates for follow-up appointments vary from 20 to 60% (Westra et al.
2000). Higher no-show rates are found in the Medicaid population (e.g., Majeroni et al.
1996; Smith and Yawn
1994); one study found that Medicaid recipients had a no-show rate that was two times as high as the rate for non-Medicaid participants (
p < .0001) (Guck et al.
2007). Many of our CUIDAR participants were enrolled in Medicaid or public insurance plans or shared socioeconomic similarities with the populations studied in this previous research. Thus, our participation rates are within the range of what is commonly observed in the community.
Moreover, our results are based on parent-self report data, which are limited and subject to bias. Given the constraints of CUIDAR funding for evaluation as well as practical limitations (almost half of the participants were not enrolled in a preschool or Head Start program), we were not able to obtain teacher ratings.
Conclusion
Through CUIDAR, we provide an accessible early intervention, parenting program for underserved parents and children. Evaluation indicates that following completion of the COPE intervention offered by CUIDAR, parents report using more positive parenting practices with their children and report decreases in child attention and behavior problems. More research is needed to address new program efforts that might reduce the disparate outcomes for children and families affected by additional stressors, including those associated with single-parent homes. In addition, more research is needed to identify specific parent behaviors that produce better child outcomes. Moreover, a randomized, controlled study of CUIDAR is still needed. In addition, in future research, we plan to conduct in-depth, qualitative interviews with CUIDAR participants as well as to obtain follow-up measurements on children who are now in school.
Acknowledgments
Grants from First 5 San Bernardino and the Children and Families Commission of Orange County support the delivery of CUIDAR services in the community. We thank the CUIDAR leaders, staff, and student assistants who contributed to CUIDAR program development, implementation, and the collection of evaluation data. We thank Dr. Charles Cunningham, who modified the Community Parent Education program for CUIDAR and Drs. Leanne Tamm and James M. Swanson who developed the CUIDAR program in Orange County and consulted in the development of the CUIDAR program in San Bernardino County, California. Support for the preparation of this manuscript was provided by a grant from the Plotkin Family Foundation, awarded to Dr. Lakes.