Language Disorders And Problem Behaviors: A Meta-analysis
Language Disorders and ProblemBehaviors: A Meta-analysisPhilip R. Curtis, MA, a Jennifer R. Frey, PhD, b Cristina D. Watson, Ed.M, b Lauren H. Hampton, PhD, a Megan Y. Roberts, PhDaCONTEXT: A large number of studies have shown a relationship between language disordersand problem behaviors; however, methodological differences have made it difficult to drawconclusions from this literature.abstractOBJECTIVE: To determine the overall impact of language disorders on problem behaviors inchildren and adolescents between the ages of birth and 18 years and to investigate the roleof informant type, age, and type of problem behavior on this relationship.DATA SOURCES: We searched PubMed, EBSCO, and ProQuest.STUDY SELECTION: Studies were included when a group of children with language disorderswas compared with a group of typically developing children by using at least 1 measure ofproblem behavior.DATA EXTRACTION: Effect sizes were derived from all included measures of problem behaviorsfrom each study.RESULTS: We included 47 articles (63 153 participants). Meta-analysis of these studies revealeda difference in ratings of problem behaviors between children with language disordersand typically developing children of moderate size (g 0.43; 95% confidence interval 0.34to 0.53; P .001). Age was entered as a moderator variable, and results showed that thedifference in problem behavior ratings increases with child age (increase in g for eachadditional year in age 0.06; 95% confidence interval 0.02 to 0.11; P .004).LIMITATIONS: There was considerable heterogeneity in the measures of problem behaviors usedacross studies.CONCLUSIONS: Children with language disorders display greater rates of problem behaviorscompared with their typically developing peers, and this difference is more pronounced inolder children.aRoxelynand Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois; and bGraduate School of Education and HumanDevelopment, Department of Special Education and Disability Studies, George Washington University, Washington, District of ColumbiaDrs Roberts and Frey conceptualized the study, supervised data collection, and revised the manuscript; Mr Curtis conceptualized the study, designed the datacollection instrument, collected data, performed the initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Ms Watson collecteddata and reviewed the initial manuscript; Dr Hampton critically reviewed the manuscript and aided in statistical analyses; and all authors approved the finalmanuscript as submitted.DOI: https:// doi. org/ 10. 1542/ peds. 2017- 3551Accepted for publication May 15, 2018Address correspondence to Philip R. Curtis, MA, Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, 2240Campus Dr, Evanston, IL 60208. E-mail: p-curtis@northwestern.eduPEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).To cite: Curtis PR, Frey JR, Watson CD, et al. Language Disorders and Problem Behaviors: A Meta-analysis. Pediatrics. 2018;142(2):e20173551Downloaded from www.aappublications.org/news by guest on January 17, 2019PEDIATRICS Volume 142, number 2, August 2018:e20173551REVIEW ARTICLE
Between 13.4% and 19.1% oftoddlers experience delayed languagedevelopment, 1 and between 6%and 8% of kindergartners have adevelopmental language disorder. 2These groups of children are definedas having delayed or disorderedlanguage development but intactnonverbal cognitive abilities, in theabsence of other known genetic orneurodevelopmental disorders. 3Disorders in language developmenthave been associated with anumber of difficulties in academicand psychosocial development,including increased rates of problembehaviors. 4–6 Although many studieshave revealed the associationbetween language disordersand problem behaviors acrossdevelopment, there is considerablemethodological heterogeneitybetween studies. This heterogeneityreflects differences in how languageskills are assessed and the criteriaused for diagnosis of languagedelay or disorder, the informanttype used to measure problembehaviors (ie, parents, teachers, orresearcher-coded observations ofchild behaviors), the age of childrenincluded in the study, as well as thetypes of problem behaviors that wereassessed.Measures of problem behaviors areoften used to classify symptomsas either internalizing behaviorsor externalizing behaviors. 7Internalizing behaviors includesymptoms commonly associatedwith depression and anxiety,whereas externalizing behaviorsinclude disruptive, hyperactive, andaggressive behaviors. 8 Although thisis only 1 system of classification,a majority of the behavioral andemotional assessments used in theexisting literature investigatingthe relation between languagedisorders and problem behaviors usescales that reflect these dimensions(eg, the Child Behavior Checklist[CBCL], 9 the Infant Toddler SocialEmotional Assessment [ITSEA], 102the Social Competence andBehavior Evaluation, 11 etc), so thisclassification system was used in thecurrent study.To quantitatively assess theassociations between language delaysand problem behaviors found in theliterature, while taking into accountthe issues noted above, we conducteda meta-analysis used to address thefollowing 3 questions:1. Do children with languagedisorders display higher rates ofproblem behaviors compared withtheir typically developing peers?2. Does informant type and/or agemoderate the relation betweenlanguage disorder status andproblem behaviors?3. Is language disorder statusmore strongly associated witheither internalizing behaviors orexternalizing behaviors?METHODSIdentification of StudiesSearches of PubMed, EBSCO, andProQuest were performed for alldates until July 2017. The followingsearch terms were used, restrictedto the titles and/or abstractswithin each database: “disruptivebehavior*, ” “behavior problems, ”“problem behavior*, ” “challengingbehavior*, ” “externalizing behavior*, ”“internalizing behavior*, ” “agress*behavior*, ” or “behave*, ” and“communication, ” “language, ”“vocabulary, ” “semantics, ” “syntax, ”or “grammar” and “delay, ” “disorder, ”“impairment, ” “disability, ” or “latetalkers.” In total, this search yielded3128 unique abstracts. Additionally,reference lists of included studieswere searched to identify additionalstudies that may have fit ourinclusion criteria, and known authorsof relevant unpublished data setswere contacted, resulting in anadditional 43 abstracts.During the first screening phase,abstracts were screened for inclusionon the basis of the following a prioricriteria: a cross-sectional designother than single-subject designor case studies is used, is writtenin English, includes 1 group ofchildren with language disordersand 1 control group, average age ofparticipants is 18 years, languagedisordered group is not solelycomposed of children with autismspectrum disorder, includes 10participants, and includes a measureof externalizing, internalizing, ortotal problem behaviors. Articlesthat failed to meet any of the listedinclusion criteria were excluded. Inthe case of longitudinal studies orfollow-up studies of a previouslystudied sample, only the firsttime point was used. During thisfirst screening phase, the numberof included studies decreasedfrom 3171 to 76. During the dataextraction process, an additional 29articles were excluded from analyses.Reasons for exclusion of thesearticles are available in SupplementalTable 5.Data ExtractionAfter digital or hard copies of eachincluded study were obtained, datawere extracted from each articleby using a detailed coding protocol(this protocol can be obtained bycontacting the first author). To testfor bias within studies, a “qualityof language assessment” variablewas created to rate the rigor of thediagnostic methods used to classifychildren as typically developing orlanguage disordered in each study.A 5-point scale was developed, anda code was assigned to every article(see Table 1 for a full explanation ofthis code). All articles were doublecoded by 2 independent reviewers,and discrepancies were resolvedthrough consensus.Included in many studies wereseparate language-disorderedgroups. For instance, authors ofDownloaded from www.aappublications.org/news by guest on January 17, 2019CURTIS et al
Downloaded from www.aappublications.org/news by guest on January 17, 2019PEDIATRICS Volume 142, number 2, August 20183ControlGroup, 91152218Source, yAsbell 12 n.d.Beitchman et al 13 1989Black 14 1989Bretherton et al 15 2014Carson et al 16 2007Carson et al 17Caulfield et al 18 1989Curtis et al 19 2017Fernell et al 20 2002Fujiki et al 21 1996Fujiki et al 22 2002Fujiki et al 23 2001Fujiki et al 24 1999Fujiki et al 25 2004Goudsmit 26 n.d.Guralnick et al 27 1996Henrichs et al 28 2012Herzel 29 n.d.Holmes 30 n.d.Horwitz et al 31 2003Lemanek et al 32 1993Lindholm et al 33 1979Malay 34 1995McCabe 5 2005McCabe et al 35 31978228LanguageDelayedGroup, 9.067.376.382.492.142.282.424.55.54.5NRMean Age ofParticipantsTABLE 1 Characteristics of Individual Included e4211111211422212411111144Quality WWNRNRWNRRaceof NRMidhighNRNRNRNRMixedMidhighMixedNRNRNRMixedNRSES of ludedNENENENENENEExcludedExcludedChildrenWith EExcludedNENENENENEExcludedChildrenWith IQ er specific case historyformCBCL 4–16Personality Inventory for ChildrenKohn Problem ChecklistStrengths and DifficultiesQuestionnaireTemperament and AtypicalBehavior ScaleCBCL 2–3Observation of parent and childStructured parental interviewEyberg Child Behavior InventoryCBCL 1.5–5ITSEAConners’ 10-Item TestConners’ Rating Scale–RevisedSocial Skills Rating SystemEmotion Regulation ChecklistObservation of playgroundinteractionTeacher Behavior Rating ScaleTeacher Behavior Rating ScaleEmotion Regulation ChecklistCBCL 4–18CBCL 4–16CBCL 1.5–5CBCL 4–16CBCL 4–18ITSEAObservation Coding SystemBehavior Problem Checklist(Quay)CBCL 2–3Eyberg Child Behavior InventoryMalay’s Observational CodingParent-Child Rating Scale 3.0Teacher-Child Rating Scale 2.1Social Competence BehaviorEvaluation ScaleParent-Child Rating Scale 3.0Teacher-Child Rating Scale 2.1Behavior Measure
4Downloaded from www.aappublications.org/news by guest on January 17, 2019CURTIS et 2Raffa 42 1990Redmond 43 2011Redmond and Rice 441998Roberts et al 45submittedRoth 46 1994Roy et al 47 2014Stanton-Chapman etal 48 2007Tallal et al 49 1989Tam 50 1996Timler 51 2008Tomblin et al 52 2000Van Agt et al 53 2005Whitehouse et al 54 2011Willinger et al 55 2003Zubrick 56 19842521439441316412817521203228Qi and Kaiser 41 20043427014171436LanguageDelayedGroup, N331179323611435ControlGroup, NPaul et al 39 1990Prior et al 40 2011Nes et al 37 2015Oram 38 n.d.McCabe et al 36 2004Source, yTABLE 1 355.9810.787.844.482.54.143.59.584.83Mean Age 1514Quality NRWRaceof xedNRMixedNRNRNRNRMixedNRNRSES of dChildrenWith EExcludedNENEExcludedNEExcludedChildrenWith IQ Social Skills Rating System–PreschoolSocial Skills Rating System–PreschoolParent questionnaireConners’ Parent Rating Scale–RevisedConners’ Teacher Rating Scale–RevisedChild Personality ScaleStrengths and DifficultiesQuestionnaireCaregiver-Teacher Report Form2–5Observational Coding SystemPersonality Inventory for ChildrenCBCL 6–18Conners’ Parent Rating Scale–RevisedCBCL 6–18Teacher Report Form (1991)Multidimensional Assessment ofDisruptive BehaviorSocial Skills Rating System–PreschoolStrengths and DifficultiesQuestionnaireCBCL 1.5–5Social Skills Rating System–PreschoolCBCL 4–16CBCL 4–16Conners’ Teacher Rating ScaleSocial Skills Rating System–ElementarySocial Skills Rating System–ElementaryCBCL 4–18Social Skills Rating System–ElementaryTAPQOLCBCL 2–3CBCL 4–18Parent questionnaireBehavior Measure
CBCL 2–3ParentNENEMixedW12.9922.12381528Zubrick et al 1 2007A, mostly African American; ASD, autism spectrum disorder; H, mostly Hispanic; n.d., no date; NE, not excluded or not reported; NR, not reported; SES, socioeconomic status; SLP, speech-language pathologist; TAPQOL, Netherlands Organization forApplied Scientific Research Academic Medical Center Preschool Children Quality of Life; W, mostly white.a Scores of the quality of language assessment were categorized as follows: (1) The researcher administered standardized assessments with clearly stated inclusion criteria (includes parent-report measure with normative data); (2) An SLP orpsychologist had made a diagnosis previously with specific inclusion criteria; (3) An SLP or psychologist had made a diagnosis previously using specific measures but without specific inclusion criteria; (4) An SLP or psychologist had made a previousdiagnosis, but no explicit reference was made to specific measures (ie, “recruited from an SLP’s caseload”); (5) Identified by parent report using a measure without normative data (ie, “parents indicated their children were not yet combiningwords”).b Mixed: no race 50%.Behavior MeasureInformantExcludedChildrenWith IQ 70ExcludedChildrenWith ASDSES of theSampleRaceof theSamplebQuality ofLanguageAssessmentaMaximumAgeMinimumAgeMean Age ofParticipantsLanguageDelayedGroup, NControlGroup, NSource, yTABLE 1 Continuedsome studies divided children intoreceptive-expressive, expressiveonly, and articulation-disorderedgroups. Because authors of studiesvaried considerably in how theydefined language disorder subgroups,and no consistently defined groupscould be extracted across studies, forthe purposes of the current analyses,all language-disordered groups werecombined to form a single languagedisordered group for each study.Groups comprising only participantswith articulation disorder, whenreported separately, were excluded.Additionally, groups comprisingonly participants with pragmaticlanguage impairment were excluded.Pragmatic language impairment,also referred to as social (pragmatic)communication disorder in theDiagnostic and Statistical Manual ofMental Disorders, Fifth Edition, 57is characterized by difficulties inthe social use of language that isnot better explained by deficits ingrammar or word structure. Althoughthese difficulties with pragmaticlanguage may be associated withproblem behaviors, the underlyingmechanisms of that association maybe different than the mechanismslinking deficits in language contentand structure to problem behaviors.For this reason, we feel that articlesin which authors investigatepragmatic language difficultiesspecifically warrant a separate studyand so have been excluded in thecurrent analyses.Behavioral Measure CharacteristicsIn the studies that were includedin this meta-analysis, authors useda number of different measuresof problem behaviors, includingpublished standardized measures,researcher-created interviews orquestionnaires, and coding of directobservations of children’s behaviorsby researchers. Questionnairesand interviews were completed byparents, teachers, or both.One complexity in measuringproblem behaviors arises fromthe factor structures used whencreating measures. Many measures,such as the CBCL, group itemsinto lower-order “narrow-band”factors (ie, “aggression, ” “anxiousand/or depressed, ” etc), as wellas higher-order factors, typicallylabeled as “internalizing problems, ”“externalizing problems, ” or“total problem behavior” factor, inwhich all behaviors are combined.When measures that used suchfactor structures were included instudies, there was a great deal ofheterogeneity in what scores authorsreported. Authors of some studiesreported only higher-order factors,such as “internalizing composite” ortotal problem behaviors, whereasother authors reported onlysubscales. In the current analyses,we were interested in the following2 broad domains: total problembehaviors and a comparison ofinternalizing and externalizingproblems. For this reason, allreported effect sizes were captured.When it was known that an authorhad not reported a certain scale (forinstance, he or she reported the CBCLinternalizing composite but not theexternalizing composite), attemptswere made to contact the authorand obtain these data. Out of 7 datarequests sent, 3 authors were able toprovide us with missing data.Data SynthesisStudy authors reported the includedeffect sizes as differences in means,percentages of each group meetinga “clinical cutoff” for problembehaviors, reported t test results, and2-group analyses of variance. Effectsizes and effect size variances werecomputed in the ComprehensiveMeta-Analysis (version 3.3.070)software. Because some studies hadsmall sample sizes, all effect sizeswere converted to Hedges’ g, whichis used to correct for small samplesize. 58Downloaded from www.aappublications.org/news by guest on January 17, 2019PEDIATRICS Volume 142, number 2, August 20185
In some studies, measures wereavailable for only subsamples of thestudy participants, either because ofmeasurement issues (eg, Malay 1995)or because of missing data. Whendata were available, participant agesand sex ratios were calculated foreach measure individually; whenthese data were not available, theoverall ages and participant sexes forthe whole study were used.Effect sizes were classified asrepresenting either internalizingor externalizing behaviors.Measures that were notclassified by the measure itself(eg, “CBCL 1.5–5 InternalizingComposite”), the authors of thestudy or other publications wereindependently classified by 2 ofthe authors with 91.8% agreement.Discrepancies were resolved byconsensus.Data AnalysisConventional meta-analytic methodsrequire that each study is usedto contribute only 1 independenteffect size. Because many of thestudy authors included in thismeta-analysis reported 1 effectsize that need to be included in thesame analysis, these traditionalmeta-analytic methods are notappropriate for the currentstudy. When multiple effectsizes are derived from the sameparticipants, these effect sizes arenot independent but are insteadcorrelated. It is possible to createsynthetic effect sizes for each studyby averaging effect sizes from thesame study; however, the syntheticeffect size’s SEs are dependent onthe covariance structure betweenthe individual effect sizes fromwhich they are computed, makingthis approach problematic. 59To more accurately model thesemultiple, dependent effect sizesacross studies, we employed therobust variance estimation methodcreated by Hedges et al. 59 Thisnovel method of meta-analysis does6not require the explicit covariancestructure between effect sizesreported from the same study(which are rarely available) butinstead uses the observed residualsto estimate the meta-regressioncoefficient estimates. 60 A correctionfor small sample sizes was employedin the current analyses. 60 Theseanalyses allowed us to includemultiple effect sizes from the samestudy (eg, Malay 34), avoiding boththe problems of excluding validestimates of problem behaviorsas well as biasing our effect sizeestimates.Moderator AnalysesIn addition to these strengths, robustvariance estimation also allowsresearchers to include additionalvariables as a means of modelingobserved heterogeneity across effectsizes, what is frequently called amoderator analysis. 61 These analysesfunction much like typical linearregression analyses, with the studyderived effect sizes as the dependentvariables and study-level covariates,such as average age of participants orinformant type, as the independentmoderator variables. Full details aregiven in Hedges et al 59. The methodof ordinary least squares is used tosolve the linear equation predictingindividual effect sizes, modeled withan intercept (the average effect sizeacross studies and measures) and anymoderators the researcher choosesto include. Each regression coefficientwithin the meta-regression can beinterpreted as in a typical linearregression (for a 1-unit increase inthe moderator variable, what is theexpected change in the observedeffect size?). SEs, significance levels,and confidence intervals (CIs)are provided for each parameterestimate to aid in interpretation.Statistically significant moderatorvariables suggest that the differencesin effect sizes across studies areassociated with differences in thatparticular moderator variable inthe meta-regression. It is importantto note that moderator variablesentered into these meta-regressionsare used to predict the effect sizesfrom each study. That is, moderatorssuch as age, informant type, or typeof problem behavior are used topredict the standardized differencein problem behavior scores betweenchildren with typical developmentand children with language delays ordisorders.RESULTSThe first set of analyses wereused to deal with total problembehaviors, the most broad andinclusive category of problembehaviors. These scores are derivedby pooling all problem behaviorsassessed within a given measure.However, some study authors failedto report a composite score forthe total problem behaviors. Forinstance, Carson et al 26 reportedan internalizing composite scoreand externalizing composite scorefor the CBCL 2 to 3 but not a totalproblem behavior score. To ensurethat all studies contributed atleast 1 effect size for this analysis,preference was given in thefollowing order: (1) total problembehavior composite scores werereported; (2) if a total problembehavior composite score wasnot reported, an internalizingand/or externalizing compositescore was reported; and (3) if nocomposite scores were reported,individual subscale scores werereported. No overlapping effectsizes were included (ie, if a totalproblem behavior composite scorewas reported, externalizing andinternalizing composite scores werenot also included, because thesescales draw from the same itemsas total problem behaviors scores).This system was used to ensurethat studies in which authors didnot report total problem behaviorcomposite scores were still includedin these analyses.Downloaded from www.aappublications.org/news by guest on January 17, 2019CURTIS et al
Research Question 1: What Is theDifference in Rates of ProblemBehaviors Between Children WithLanguage Delays and Their TypicallyDeveloping Peers?To address this question, we createdan intercept-only model. Resultsare reported in Table 2, and a forestplot is available in the SupplementalInformation. For this model, therewere 47 studies included with a totalof 128 effect sizes (minimum 1;mean 2.7; maximum 18), for τ2 0.05. The intercept was significant(0.43; 95% CI 0.34 to 0.53; P .001),indicating that, on the whole, childrenwith language delays have problembehavior ratings 0.43 SDs higher thantheir typically developing peers. See Fig 1 for a forest plot of effect sizesincluded in this analysis.Research Question 2a: Do EffectSizes Differ on the Basis ofInformant?It is possible that ratings of problembehaviors may vary across settings(ie, home, school, or researchlaboratories 62) or that differentinformants may rate children’sproblem behaviors differently. 63Estimates of effect sizes are given in Table 2 for each type of informantindividually. Average effect sizesfrom teacher report were higherthan both those derived from parentreport, as well as from researcherobservational coding (0.63 versus0.37 and 0.43, respectively). Totest whether these differenceswere statistically significant, amoderator analysis was run by usinga “teacher report” dummy code. Thisvariable was coded as 0 for parentor researcher observations and 1for teacher reports. Because therewere comparatively few effect sizesderived from researcher observation(5 studies, 14 effect sizes), and theeffect sizes derived from parentreports and researcher observationswere similar, no variable was enteredto differentiate between parent andresearcher observations. Resultsfrom this model are given in Table 2.The intercept, representing theaverage standardized differencein problem behaviors betweenchildren with typical developmentand children with language delaysor disorders, remained significant,indicating that children withlanguage delays are rated by theirparents and researcher observationsas having significantly more problembehaviors than typically developingchildren. The unstandardizedcoefficient of the dummy code forteacher reports was statisticallysignificant, indicating that, withinthe studies included in this metaanalysis, on average, teachersidentified a larger difference betweengroups than do parents or researchobservations.Research Question 2b: Does theAssociation Between LanguageDisorders and Problem BehaviorsVary on the Basis of Children’s Age?To test whether the relation betweenlanguage disorders and problembehaviors varies by children’s age,an additional analysis was run withthe average child age from each studyentered as a moderator variable.Again, the dependent variable inthese models is individual effectsizes, representing the standardizeddifference between children withlanguage delays or disorders andchildren with typical languagedevelopment. The age variable wascentered at the age of the youngestparticipants (Henrichs et al 28 meanage 1.5 years), so that the interceptwould represent the average effectsize for children 1.5 years of age,and the unstandardized regressioncoefficient on mean age wouldrepresent the increase in effect sizepredicted by a 1-year increase inchildren’s average age. Results fromthis model are given in Table 3.Results revealed that even forchildren as young as 1.5 years ofage, language disorder status wasassociated with higher rates ofproblem behaviors (unstandardizedcoefficient 0.19; 95% CI 0.07 to0.31; P .004). The unstandardizedcoefficient for the mean age variablewas also statistically significant(0.07; 95% CI 0.03 to 0.11; P .001), meaning that the associationbetween language disorder statusand problem behaviors is largerin older children than in youngerchildren.It could be argued that age andnumber of effect sizes derivedby teacher report may in factbe collinear with one another,confounding the relation betweenage and problem behaviors andbetween informant type andproblem behaviors. Indeed,within the current sample ofstudies, the average age forteacher-reported outcomes wassignificantly older than theaverage age of parent-reportedoutcomes (mean parent orobserver-rated reported age 5.02 years; mean teacher reportedage 6.61; t(34.18) 2.88; P .007). When both mean age and theteacher report dummy code wereincluded in the same model, theunstandardized coefficient for thedummy variable for teacher reportno longer approached significance(0.19; 95% CI 0.12 to 0.49; P .21).More importantly, the estimate of thedifference between parent-reportedor observer-rated effect sizes andteacher-reported effect sizes droppedfrom 0.35 to 0.19, after controllingfor mean age. This suggests that theoverall higher ratings of problembehaviors by teachers within thissample are strongly related tochildren’s age. Within this model,the unstandardized coefficient ofmean age was again significant(0.06; 95% CI 0.01 to 0.10; P .01),indicating that after controlling forinformant type, each additional yearin age was associated with a 0.06 SDincrease in the difference in problembehavior scores between childrenwith language disorders and theirtypically developing peers.Downloaded from www.aappublications.org/news by guest on January 17, 2019PEDIATRICS Volume 142, number 2, August 20187
TABLE 2 Average Standardized Differences Between Typically Developing Children and Children WithLanguage Delays or Disorders by Informant TypeParameterAll informantsInterceptStudies: 47Parent onlyInterceptStudies: 40Observation coding onlyInterceptStudies: 5Teacher onlyInterceptStudies: 14All informants, controllingfor teacher reportInterceptTeacher reportStudies: 47Estimatea (SE)P95% CI0.43 (0.05)Effect sizes: 128 .001I2 78.10.34 to 0.53τ2 0.050.37 (0.04)Effect sizes: 90 .001I2 75.40.29 to 0.46τ2 0.040.43 (0.13)Effect sizes: 14.03I2 37.30.05 to 0.80τ2 0.070.63 (0.15)Effect sizes: 24.001I2 81.90.30 to 0.96τ2 0.250.38 (0.05)0.35 (0.13)Effect sizes: 128 .001.02I2 76.240.28 to 0.47(0.06 to 0.63)τ2 0.04In all analyses, ρ 0.8.a Estimates are unstandardized regression coefficients.Research Question 3: Is LanguageDisorder Status More StronglyAssociated With Either InternalizingBehaviors or ExternalizingBehaviors?Several researchers have suggestedthat language more strongly impacts1 type of behavior (internalizingversus externalizing) compared withthe other. 44, 64 To test
Parent Conners ' 10-Item Test . Parent Parent-Child Rating Scale 3.0 Teacher Teacher-Child Rating Scale 2.1 Teacher Social Competence Behavior Evaluation Scale McCabe et a 35 2006 18 30 4.2 3.17 5.42 4 W Mixed Excluded NE Parent Parent-Child Rating Scale 3.0 Teacher Teacher-Child Rating Scale 2.1
Mar 04, 2014 · 2. Substance-induced disorders -- intoxication, withdrawal, and other substance/medication-induced mental disorders (psychotic disorders, bipolar and related disorders, depressive disorders, anxiety disorders, obsessive-compulsive and related disorders, sleep disorders, sexual dysfunctions,
1. Neurodevelopmental Disorders 2. Schizophrenia Spectrum and other Psychotic Disorders 3. Bipolar and Related Disorders 4. Depressive Disorders 5. Anxiety Disorders 6. Obsessive-Compulsive and Related Disorders 7. Trauma-and Stressor-Related Disorders 8. Dissociative Disorders 9. Somatic Symptoms and Rela
A-Disorders of nitrogen-containing compounds: 6-6-Disorders of glutathione metabolism 11-Disorders of phenylalanine 12-Disorders of tyrosine metabolism 13-Disorders of sulfur amino acid and sulfide metab. 14-Disorders of branched-chain amino acid metab. 15-Disorders of lysine metabolism 16-Disorders of proline and ornithine metabolism 18 .
6. Detection of Eating Disorders 63 7. Diagnosis of Eating Disorders 73 8. Interventions at the Different Levels of Care in the Management of Eating Disorders 81 9. Treatment of Eating Disorders 91 10. Assessment of Eating Disorders 179 11. Prognosis of Eating Disorders 191 12. Legal Aspects Concerning Individuals with Eating Disorders in Spain 195
The Co-Occurring Center for Excellence (COCE), funded through the Substance Abuse and Mental Health Services Administration (SAMHSA), is a leading national resource for the field of co-occurring mental health and substance use . Eating disorders Sleep disorders Impulse-control disorders Adjustment disorders ersonality disorders P Disorders .
Chapter 13 Anxiety and Stress-Related Illness Chapter 14 Schizophrenia Chapter 15 Mood Disorders Chapter 16 Personality Disorders Chapter 17 Substance Abuse Chapter 18 Eating Disorders Chapter 19 Somatoform Disorders Chapter 20 Child and Adolescent Disorders Chapter 21 Cognitive Disorders 10458-13_UT4-CH13.qxd 7/12/07 11:18 AM Page 239
NMSP to increase screening from 7 to 28 metabolic disorders, with the addition of 9 fatty acid oxidation disorders (FAODs) and 12 more amino acid disorders. 2.3 Disorders in the Newborn Metabolic Screening Programme The 28 metabolic disorders currently screened for by the NMSP are: Amino acid disorders (14 disorders, including PKU and MSUD)
Brief Contents PART ONE MEDICAL-SURGICAL CASES, 1 Chapter 1 Cardiovascular Disorders, 1 Chapter 2 Respiratory Disorders, 83 Chapter 3 Musculoskeletal Disorders, 149 Chapter 4 Gastrointestinal Disorders, 189 Chapter 5 Genitourinary Disorders, 235 Chapter 6 Neurologic Disorders, 273 Chapter 7 Endocrine Disorders, 341 Chapt
