March 27, 2006

Aggression and the Family

Ovid: MILLER: J Am Acad Child Adolesc Psychiatry, Volume 45(3).March 2006.355-363

Family and Cognitive Factors: Modeling Risk for Aggression in Children With ADHD

Drs. Carlin Miller and Halperin are with the Department of Psychology, Queens College, City University of New York, Flushing; Drs. Scott Miller, Newcorn, and Halperin are with the Division of Child and Adolescent Psychiatry, Mount Sinai School of Medicine, New York; Dr. Halperin and Mr. Trampush are with the Neuropsychology Doctoral Program, Graduate School and University Center, City University of New York, New York; and Dr. McKay is with the Court Assessment Program, Westchester Jewish Community Services, Hartsdale, NY.
Accepted October 11, 2005.
This research was supported by National Institute of Mental Health grants RO1MH46448 and RO1MH60698, and The William T. Grant Foundation’s Faculty Scholar’s Award Program.
Correspondence to Dr. Carlin J. Miller, Department of Psychology, Queens College, City University of New York, 65-30 Kissena Boulevard, Flushing, NY 11367; e-mail:
Disclosure: Dr. Newcorn has had consulting/advisory relationships with Eli Lilly, McNeil, Novartis, Shire, UCB, Bristol-Myers Squibb, Cephalon, and Sanofi-Aventis; has received research support from Eli Lilly, McNeil, and Shire; and serves on speaker boards for Eli Lilly, McNeil, and Novartis.Dr. Halperin has consulted for Shire. The other authors have no financial relationships to disclose.

Objective: To explore the relationships of family and cognitive factors to aggression as reported by parents and teachers.

Method: Data regarding different types of aggressive behavior were collected from parents and teachers of 165 school-age (7-11 years old) children referred to a study of attention-deficit/hyperactivity disorder and disruptive behavior. Structural equation modeling was used to examine the differential relationships of family and cognitive factors to aggression in the home and school settings.

Results: Family risk factors influenced aggression reported at home and at school, whereas cognitive risk factors may exert their influence on aggression at school. Significant covariance between parent and teacher report of aggressive behavior was observed.

Conclusions: Intervention at the family level may serve to reduce aggressive behaviors in both home and school environments. J. Am. Acad. Child Adolesc. Psychiatry, 2006;45(3):355-363.

Aggressive behavior is among the most stable personality traits across the life span, such that aggression in childhood often predicts similar and perhaps escalating behavior over the life course (Farrington, 1991; Haapasalo and Tremblay, 1994; Loeber and Dishion, 1983; Loeber and Hay, 1997), resulting in peer rejection (Bagwell et al., 2001; Loeber et al., 1993), antisocial personality disorder (Cadoret and Cain, 1981; Stewart and Leone, 1978), domestic abuse (Fuller et al., 2003; Kolvin et al., 1988), and incarceration (Olweus, 1979; Schaeffer et al., 2003). Nevertheless, many aggressive children do not become aggressive adults.

A confluence of environmental and genetic factors is likely to affect the manifestation of aggression in children and the degree to which it persists/escalates with development. Severity of aggression appears to be the most important behavioral predictor of outcome. However, whether the behavior is situation specific or pervasive also has prognostic value. Thus, children who are aggressive at home or at school are likely to have a better outcome than those who are aggressive in both settings (Loeber and Hay, 1997; Loeber et al., 1993).

Considerable data indicate that aggression runs in families, and that both genetic and environmental factors play a role in the emergence and persistence of this maladaptive behavior in children. Twin studies consistently report higher concordance rates for aggression among monozygotic compared with dizygotic twins (Button et al., 2004; Hudziak et al., 2003), with heritability estimates for aggression ranging from 0.28 to 0.72. Yet, genes account for only a portion of the variance in aggression. A host of adverse environmental factors, including poverty, low socioeconomic status, marital discord, harsh parenting, poor supervision, parental psychopathology, and crowded living conditions are linked to the emergence of aggressive behavior in children and adolescents (Cadoret and Cain, 1981; Fergusson et al., 1996; Haapasalo and Tremblay, 1994; Vaden-Kiernan et al., 1995). It has been hypothesized that having limited financial and human resources increases risk because children are less likely to be closely supervised and the household is less structured by rules (Griffin et al., 2000; Haapasalo and Tremblay, 1994). Nevertheless, in naturalistic studies involving intact family units, it is difficult to parse out genetic and environmental effects. For example, studies of aggressive boys consistently indicate similar behaviors in their fathers (Pfiffner et al., 1999; Stewart and deBlois, 1983). Although these aggressive behaviors may represent the impact of genetic factors, it is likely that modeling and evocative effects are also involved. This hypothesis is supported by evidence indicating that the longer an aggressive or personality-disordered father is not in the home, the less effect his behavior has on the behavior of children in the home (Stewart and deBlois, 1983). Furthermore, when the genetic contribution of a father’s antisocial behavior is controlled, the antisocial parent’s behavior in the environment still affects the child’s aggressive behaviors (Jaffee et al., 2003). These findings are consistent with emerging data indicating interactive effects of genes and environment on the emergence of aggression in youths. Both adoption (Riggins-Caspers et al., 2003) and molecular genetic (Caspi et al., 2002) studies indicate greater susceptibility to adverse environmental factors in genetically vulnerable individuals.

In addition to home and family factors, a substantial literature indicates that groups of aggressive children (Coy et al., 2001; Vance et al., 2002) and adults (Brownlie et al., 2004; Huesmann et al., 2002) are characterized by cognitive deficits, primarily in the verbal domain. Thus, it is possible that childhood cognitive and learning problems lead to aggressive behaviors; however, findings have not been conclusive. Patterson and colleagues (1989) hypothesized that academic failure and rejection by normal peers in the presence of cognitive problems leads to a deviant peer group and further delinquency. The presence of inattentive behaviors, as seen in youth with attention-deficit/hyperactivitydisorder (ADHD), also increases the likelihood of aggressive behaviors, especially when inattention occurs together with low cognitive ability (Bellanti et al., 2000; Schaeffer et al., 2003). The specific relationship between learning problems and the development of aggression is also unclear. One study found that low intelligence and attention problems but not learning problems predicted delinquency (Manguin and Loeber, 1996). However, other studies have indicated that learning problems, particularly reading problems, increase the risk of aggressive behavior, especially in individuals with aggression problems that predate learning issues (Cornwall and Bawden, 1992; Vance et al., 2002).

Despite the fact that aggression reported by parents and teachers is different, there is little information regarding differential correlates of parent and teacher reports. Most studies examining both parent and teacher data have focused primarily on ADHD and/or oppositional-defiant behavior, rather than aggression. Furthermore, it is well known that aggression is multifactorial in nature, and it is not known how this may translate into reports from different informants. One approach to managing this problem is by defining and examining the correlates of latent factors that can better account for multiple components of complex constructs. The present study uses structural equation modeling of latent traits to examine the extent to which demographic factors and cognitive problems differentially correlate with aggression at home and school. It was hypothesized that family risk factors would predict aggression as reported by the parent and the teacher. In contrast, cognitive risk factors were hypothesized to predict aggression at school, where they are likely to engender greater distress, but not at home (Fig. 1).Consistent with past findings from Halperin and colleagues (2002, 2003), the correlation between home and school aggression was predicted to be statistically significant, but relatively modest in magnitude.

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[Email Jumpstart To Image] Fig. 1 Model of hypothesized data relationships.

Participants were 163 boys (n = 141) and girls (n = 22), ages 7-11 years with ADHD and/or other disruptive behavior disorders, who participated in a research protocol investigating aggressive behaviors in individuals with ADHD. The mean age of the sample was 9.06 years (SD = 1.29 years), and all had a Verbal or Performance IQ score of at least 70. The sample was ethnically diverse: 40.9% were Latino, 29.6% were African American, 18.9% were white, and 10.7% were of mixed or other ancestry. All participants and their parents were English speaking. The majority lived in an urban environment within a major metropolitan area. Most were from lower middle-class homes (mean = 30.84; SD = 13.89), although there was a range of socioeconomic status in the sample; Hollingshead Index (Hollingshead,1975) scores ranged from 11 to 66. Referral was through local schools and medical care providers. Subjects were not paid for their participation and did not receive any intervention as part of the protocol. Approval from the medical center’s institutional review board was attained before the study began and informed consent was gathered from parents before participants were enrolled.

Participants were diagnosed for Axis I disorders using the Diagnostic Interview Schedule for Children (DISC), version 2.1 (Shaffer et al., 1989) or 2.3 (Shaffer, 1997) in concert with other measures. Trained graduate students administered the DISC to parents. Additional information was gathered through parent and teacher rating scales as well as clinical observations. Overall, 152 participants were diagnosed with ADHD. The remaining 13 had significant subsyndromal symptoms associated with ADHD. In addition to ADHD, 27.9% of the children met criteria for oppositional defiant disorder, 19.4% met criteria for conduct disorder, 12.6% met criteria for mood disorders, and 35.1% met criteria for anxiety disorders. Also, 18.8% of participants met criteria for two or more comorbid disorders in addition to ADHD.
Family Demographics Information.

Parents, most often mothers, were interviewed by trained graduate studentsabout the family environment and family risk factors. For the family risk factors latent construct, parents were asked about the number of siblings in the home (SIBS), number of parenting adults in the home (HOME), and whether the biological parents of the target child had a history of aggressive behavior (FHX). For the SIBS and the HOME variables, the reported number was entered in the database. The number of adults was typically one or two, but on occasion a third relative (e.g., grandparent) also served in this capacity. For the FHX variable, the number of biological parents with a history of aggression for each target child was entered.

The interaction variable (RISK) combined the biological parent’s history for aggressive behavior with whether the parent was living at home. In accordance with previous research (Stewart and deBlois, 1983; Stewart and Leone, 1978), having aggressive parents living in the home with the child was hypothesized as the highest risk and having nonaggressive parents in the home was seen as the lowest risk of child aggression. For the RISK variable, possible values ranged from 1 to 4 as follows: children with both parents living in the home, neither of whom had a history of aggression = 1; children with only one parent at home, neither having a history of aggression = 2; children with either one or two parents with a history of aggression but the aggressive parent was not living in the home = 3; children with either one or two parents with a history of aggression and an aggressive parent living in the home = 4.
WISC, Revised or Third Edition (WISC-R/WISC-III).

The Full Scale IQ score from the WISC-R (Wechsler,1974) or WISC-III (Wechsler,1992) was used as an indicator of general cognitive functioning/impairment and was included in the cognitive risk factors latent construct. The version of the WISC that was administered to the participants was determined by when they entered the study. The WISC series of tests are frequently used intelligence tests with acceptable psychometric properties. As would be expected by the Flynn effect (Flynn, 1999), scores from later published tests are often lower than those generated by previous versions. In light of the fact that Full Scale IQ scores generated by the WISC-III are on average 5.9 points lower than those from the older WISC-R (Wechsler, 1992), all WISC-III scores were adjusted upward 5.9 points, as suggested by the WISC-III manual.
Reading and Math Achievement.

The reading and math subtests from Wide Range Achievement Test, Revised Edition (Jastak and Wilkinson, 1984) or the Wechsler Individual Achievement Test (Wechsler,1992) were administered to each participant. The Wide Range Achievement Test and Wechsler Individual Achievement Test are frequently used in clinical settings, with considerable evidence of their reliability and validity. The standard score for word reading was used as an indicator of general reading achievement (READ) and the standard score for math calculation was used as an indicator of general mathematical achievement (MATH). Both MATH and READ were included in the cognitive factors latent construct. Administration of the particular achievement test was determined by when the participant entered the research protocol. In contrast to the IQ scores, there were no significant differences (p > .10) in academic achievement scores as a function of whether the child was administered the Wide Range Achievement Test or Wechsler Individual Achievement Test.
Children’s Aggression Scale-Parent and Teacher Versions (CAS-P, CAS-T).

The CAS-P and CAS-T, respectively, are scales representing five domains of aggressive behavior, four of which were used in this study: verbal aggression (VERB), aggression against objects and animals (OBJ), provoked physical aggression (PROV), and unprovoked/initiated physical aggression (INIT). The use of weapons subscale was eliminated from the data analyses because it demonstrated limited discriminability effectiveness in this age range (Halperin et al., 2002, 2003). Psychometric data for the parent and teacher versions of the CAS have been published elsewhere and indicate good reliability and validity. Compared to other “aggression” rating scales, scores are less confounded by factors related to ADHD and oppositional-defiant behaviors (Halperin et al., 2002, 2003). Information from the CAS-P was entered into the model to indicate aggression in the home and nearby environments. CAS-T scores were used to indicate aggression displayed in the classroom and other school environments. Previous studies of the CAS-P and CAS-T (Halperin et al., 2002, 2003) suggest statistically significant but modest intercorrelations.
Statistical Methodology

Descriptive statistics and correlations for all indicator variables were calculated using SPSS 10.0 (SPSS Inc., 1999). LISREL 8.54 (Joreskog and Sorbom, 2003) was used to test the main hypotheses. This version of LISREL contains the full information maximum likelihood feature (FiML), which allows the use of all available data to estimate model parameters. Monte Carlo studies using FiML have found reliable parameter estimates when as much as 25% of the data is missing (Enders and Bandalos, 2001). The percentage of missing values in the present study was 7.28%. Two goodness-of-fit statistics were used in the present study: a FiML [chi]2 value and the root mean square error of approximation (RMSEA). The [chi]2 statistic tests the closeness of fit between the hypothesized model and the model implied by the data. The RMSEA represents the discrepancy per degree of freedom between the population data and the proposed model (Byrne, 1998). RMSEA values up to 0.06 (Hu and Bentler, 1999) have been used to indicate adequate fit.

A common structural equation modeling strategy for assessing the relative goodness of fit of competing models is the use of nested models. This approach begins with testing the fit of a baseline model, which provides a baseline [chi]2 value to which the [chi]2 values of subsequent models are compared. In the present study, the baseline model freely estimated all hypothesized paths with high levels of family risk factors predicting high levels of parent-reported aggression at home. Low levels of cognitive resources were hypothesized to predict high levels of aggression as reported by teachers. The path from family factors to school aggression was also hypothesized to be significant. The path from cognitive factors to home aggression was estimated but was not hypothesized to be significant. The covariance of home and school aggression, expected to be significant, was also estimated.

Descriptive statistics for all indicator variables are summarized in Table 1. Slightly more than 30% of participants lived in homes with one adult. Slightly more than 50% lived in homes with two adults, although in many cases, adults in the home were not necessarily the biological parents. Most participants had at least one sibling living with them at home, and nearly 14% had three or more siblings in the home with them. More than 35% of the participants had at least one biological parent with a history of aggressive behavior and nearly 13% had two biological parents with a history of aggressive behavior. Slightly more than 13% of participants were living with a parent who had a history of aggressive behavior, and slightly more than 25% living with biological parents with no history of aggressive behavior.

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[Email Jumpstart To Image] TABLE 1 Descriptive Information for Indicator Variables

The mean level of intellectual functioning was in the low average range. Approximately one fourth of participants were functioning significantly below the average range of intellectual ability (i.e., Full Scale IQ .10) relative to the baseline model indicated that modeling cognitive factors as equally predictive of home and school aggression showed only slightly poorer fit to the data. Because the [chi]2 increase was not significant, the alternate hypothesis of cognitive factors predicting both home and school aggression ([beta] = -.11, t = -1.91, p


Assessing the effectiveness of treatment

Understanding the Effect Size of ADHD Medications

Evidence-based Practice

Filed under: Uncategorized — mental1 @ 6:19 pm

Ovid: HAMILTON: J Am Acad Child Adolesc Psychiatry, Volume 45(3).March 2006.364-370

Evidence-Based Practice for Outpatient Clinical Teams

Dr. Hamilton is with The Permanente Medical Group of California, Inc., Sacramento, CA, and Kellogg College, University of Oxford.
Accepted September 1, 2005.
Robin Weersing, Ph.D., John Lyons, Ph.D., and Michael Jellinek, M.D., were helpful in conceptualizing and drafting these ideas.
Correspondence to Dr. John D. Hamilton, 2025 Morse Avenue, Sacramento, CA. 95825; e-mail:
Disclosure: The author has no financial relationships to disclose.

This column focuses on evidence-based practice (EBP) within multidisciplinary outpatient settings, but, first, some definitions. Besides EBP (Burns and Hoagwood, 2005; Guyatt and Rennie, 2002), there are also evidence-based medicine (EBM; March et al., 2005), evidence-based service (EBS; Chorpita et al., 2002), and evidence-based treatment (EBT; Kazdin, 2005). The term empirically supported treatment (EST) is often used interchangeably with EBT (Chorpita, 2003).

The common denominator in these terms is a commitment to using the evidence of empirical results to guide care in a clinically sensitive way. To oversimplify, there are three groups. The first group (EBM) is associated with medication issues and child and adolescent psychiatrists. The second (EBT) is associated with psychologists, psychosocial treatments, and the American Psychological Association. The third group (EBS) is associated with systems trying to make better use of empirical results to improve outcomes. In this column, when we refer to EBP, we are using it as an umbrella term to include EBM, EBT, and EBS, and valuing patient preference and clinical expertise as well. While accommodating both medication and psychosocial interventions, EBP is neutral in choosing between them. This is a major advantage in choosing an approach acceptable to a multidisciplinary team.

EBM focuses on the practitioner, EBT focuses on the therapy, and EBS focuses on a large system. Yet it is often the multidisciplinary team serving outpatients that is the natural unit of organization in delivering services. Consider, therefore, two central questions.

First, how does a child and adolescent outpatient team function if it is committed to a sophisticated EBP that values clinical expertise and patient values? Which processes in EBP differ from functioning “as usual” and in what way?

Second, why bother? Without definitive evidence that efforts to adopt EBPs in real-world settings will generate better outcomes than proceeding as usual, this is an important question without a definitive answer (Dulcan, 2005; Weisz et al., 2004). Yet there are reasons to bother. First, real-world practice settings have minimal to no effect compared with a control group for child and adolescent psychotherapy (Bickman et al., 1999; Weisz and Jensen, 1999). Second, demonstration projects to improve care by simply offering more care have also shown minimal effect (Bickman et al., 1999). Third, in Hawaii’s system of care for youths, the rate of improvement (reduced symptoms and improved functioning) as reported by teacher, parent, and clinician all improved significantly between 2002 and 2004, the time period following the introduction of EBP (Daleiden, 2004; Daleiden and Chorpita, 2005). These initial reports indicate that systemwide efforts to introduce EBPs have made a difference in Hawaii.

Table 1 lists a chain of clinical processes fundamental to EBP and highlights differences between the EBP approach and usual practice in defining the clinical population, in choosing an intervention, and in evaluating its effects relative to a comparison or control group. This order is the familiar PICO format derived from epidemiology: population, intervention (or exposure), control (or comparison), and outcome. Implementation of such an ambitious list of processes has only barely begun at my own workplace.

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[Email Jumpstart To Image] TABLE 1 Functions and Processes: Treatment as Usual Versus EBP
Diagnostic Assessment of the Youth’s Psychopathology and Functioning Is Sufficient to Hold Up in the Court of Scientific Opinion Yet Balanced With Clinical Sensitivity

At least one component of the diagnostic assessment needs to generate results that even a skeptic would acknowledge define subsets of youths represented in the psychiatric treatment literature. A diagnostic process so arbitrary that its results are truly idiosyncratic to the agency and its interviewers is a disadvantage. For example, many agencies have their own intake forms, and clinicians routinely assign youths DSM-IV-TR diagnoses based on a single unstructured interview, a process with significant limitations (Jensen and Weisz, 2002): it becomes more difficult to link treatment planning to the literature and to compare results with known benchmarks. DSM-IV-TR, however, does not have a special reserved place within EBP, and this is an advantage on a multidisciplinary team because DSM is associated with a single professional association. Continuous scales empirically derived using factor analysis such as the Achenbach System of Empirically Based Assessment (ASEBA), for example, can compete with DSM as legitimate broad measures of psychopathology and functioning.

A clinical interview and formulation can complement a feasible, broad-based instrument with known reliability and concurrent and predictive validity (Jensen, 2005; Jellinek and McDermott, 2004). First, inventories of broad arrays of symptoms free up the interviewer’s limited time. He or she can use clinical expertise to focus on whatever appears most significant in the encounter with the youth, knowing that the inventory, delivered as a questionnaire or via computer, will search broadly for symptoms. The interviewer is free to build rapport and refine initial information gathered from the inventory as well as address discrepancies among reporters (parent, youth, teacher). A useful analogy is how a pediatrician connects to an asthmatic child with a personal interview, but uses a peak flow meter as a measure of functioning.

A structured, broad assessment and a clinical interview complement each other because only the latter can provide the highly case-specific information often useful in clinical treatment, as well as a context for understanding the results of the structured assessment. For example, the divorced parents of Evan, an adolescent with severe attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder on the parent-based Diagnostic Interview Schedule for Children, gave a detailed history of their current multiple, intense, and unpleasant arguments related to their marriage’s dissolution. This information provided a current context for Evan’s oppositional behavior as rated on the structured instrument.

Both broadbased and highly specific diagnostic instruments complement the therapist’s efforts to establish a strong therapeutic alliance with a clinical interview. There indeed may be an optimal mix in combining well-defined instruments with more spontaneous information-gathering approaches such as a play therapy session with a school-age child and a clinical interview with an adolescent focusing on the developmental tasks and dynamics of adolescence. Values, ideals, and aspirations, for example, as well as models for identification, sexual experiences, and strengths and accomplishments may all be more adequately discussed in a clinical interview.

Although defined instruments in a time-pressured setting may invite overreliance on their results, pushing an assembly-line atmosphere and undervaluing clinical sensitivity, wisely used technological innovations like the computerized Diagnostic Interview Schedule for Children (Version 4.0) and Achenbach’s checklist system can capture data efficiently and liberate clinicians to develop a solid alliance with youths and parents. The goal is an evaluation summary that includes both a rich and sophisticated formulation, a good alliance, and results from established diagnostic instruments.
Diagnostic Assessment Includes Instruments Able to Function as Outcome Measures

The heart of EBP is empirical results, both from the site itself and from the literature. Hence, the need to begin collecting data at intake with feasible instruments that can be used to measure outcome. For example, the Screen for Child Anxiety Related Emotional Disorders, or SCARED, is helpful in defining the level of anxiety symptoms in clinical youths (Birmaher et al., 1999). It is feasible because it is a brief self-report measure for parents and youths in the public domain. Another feasible instrument in tracking outcome results for ADHD is the SNAP-ADHD rating scale, composed of 18 items that closely parallel the DSM-IV-TR listing of ADHD symptoms (Gaub and Carlson, 1997; Swanson, 1992; Swanson et al., 1999). It also has been widely used in treatment studies (e.g., Wigal et al., 2004). Measures such as the SCARED and the SNAP-ADHD scale, administered at intake, can help in developing outcome databases for individual youths, for individual clinics, or for a whole system. Many other feasible instruments sensitive to change and in the public domain exist for specific indications, including the Child Stress Disorders Checklist for observer-report of acute stress disorder and posttraumatic stress disorder symptoms (Saxe et al., 2003).
Pyramid of Evidence Is Central to the Team’s Functioning

In the team committed to EBP, practitioners are familiar with the pyramid of evidence (Guyatt et al., 2002) and use it to offer feasible-to-implement treatments showing the most benefit and the least harm in the most credible studies. In the State of Hawaii’s mental health division, for example, a unifying task for the clinical staff is to biannually review the evidence base for treating the most common disorders. They post their conclusions on the Internet as an agency-wide “menu”. Psychosocial treatments are rated according to the American Psychological Association’s rating system, and medications are given a letter grade based on similar principles (Chorpita and Viesselman, 2005).

The pyramid of evidence is a neutral and useful referee in debates centered on medication versus psychosocial interventions, debates easily fueled by the irrational. The pyramid of evidence then becomes a central unifying element of the team’s functioning, linking providers, youths, and their parents to both psychosocial and medication treatment options. Active discussion of the best evidence with families decreases the gap between what clinicians know about treatment and what youths and parents know, an information gap that handicaps consumers (Lyons, 2004).

Finally, the pyramid needs to be built on a human scale rather than as a marble monument. The clinician’s own expertise and patient preferences temper and soften the pyramid without dulling its crisp edges. An overly precise approach discarding subtle, astute clinical observations becomes artificial and only discourages EBP.
Issues Related to Efficacy Versus Effectiveness Are Taken Seriously

A psychotherapy shown to be efficacious in a controlled study in a delineated population is often referred to as an EBT, especially in the psychological literature. The problems exporting an EBT from university centers using specialized clinicians with small caseloads to real-world settings are well known. How much can real-world psychotherapists adapt an EBT to better suit their own site without destroying key essential components and affecting outcome? How much supervision or live monitoring is necessary to ensure adequate treatment fidelity? Because clinicians control EBP implementation for their clients, what motivational and learning strategies go into changing individual therapists’ behaviors? These serious questions are unresolved at present with a large impact on the “bottom line” of improved youth outcomes (Riemer et al., 2005). A team committed to EBP takes interest in these issues.

Exporting medication therapies from highly specialized research teams into real-world settings is also complex. In the Multimodal Treatment of ADHD Study, for example, 56% of the youths with ADHD receiving a carefully crafted intervention of psychostimulants achieved a good outcome (defined as SNAP-IVPT

March 11, 2006

Patient perspective on bipolar disorder

Filed under: Drugs & Alcohol,Psychotic disorders — mental1 @ 8:04 pm

Bad Mother: I’m tempted to take up mountain climbing.

That K2 stat is worth remembering. Another number of dubious worth to be filed away in my head. Well written post.

Cannabis: Just who is likely to go mad?

Filed under: Drugs & Alcohol,Psychotic disorders — mental1 @ 7:54 pm

Reefer madness by Veronika Meduna | New Zealand Listener
Drug induced psychosis is a huge issue in psychiatry these days and this article is a very readable summary of the research that is happening around the COMT gene as part of the Queen Mary cohort research.
Maybe one day we’ll be able to sort out patients who are at increased risk. I’d love to know what the dominant phenotype is in our local populations. The sheer numbers make me wonder whether we have a large recessive population of valine-valine COMT people.

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