Oppositional Defiant Disorder (ODD) is a childhood behavioral disorder characterized by a pattern of disobedient, hostile, and defiant behavior towards authority figures, such as parents and teachers.
Children with ODD may frequently lose their temper, argue, and deliberately annoy others.
They may also be spiteful, vindictive, and unwilling to comply with rules or requests.
ODD can have a significant impact on a child's social, academic, and family functioning, and early intervention is crucial for managing the condition and preventing its escalation into more serious behavioral problems.
Researchers can utilize PubCompare.ai's AI-powered protocol optimization to enhance the reproducibility of their ODD research, locating and comparing the best protocols and products from the literature, preprints, and patents to improve their effciency and outcomes.
Most cited protocols related to «Oppositional Defiant Disorder»
For comparability, the same sample used to standardize the overall ADOS total (see Gotham et al. 2009 (link)) was also employed to calibrate separate severity metrics for the Social Affect (SA) and Restricted, Repetitive Behavior (RRB) domains. Briefly, this included data from 1,415 individuals ranging in age from 2 to 16 years. With repeated assessments for 25 % of the sample, data from 2,195 ADOSes with contemporaneous best estimate clinical diagnoses were available for analysis. Of these assessments, 1,786 cases were given an autism spectrum disorder diagnosis (ASD; 1,187 Autistic Disorder, 599 Other-ASD) and 409 had a Non-ASD diagnosis. Non-ASD diagnoses included language disorders (27 %), nonspecific intellectual disability (20 %), Down syndrome (14 %), oppositional defiant disorder or ADD/ADHD (13 %), mood or anxiety disorders (8 %), Fetal Alcohol Spectrum Disorders (7 %), other genetic or physical disabilities, such as Fragile X or mild cerebral palsy (6 %) and early developmental delays (5 %). Individuals were consecutive referrals to specialty clinics in Ann Arbor, Michigan and Chicago, Illinois, and participants in research studies conducted through the University of North Carolina—Chapel Hill, University of Chicago, and University of Michigan. All participants provided informed consent and all procedures related to this project were approved by institutional review boards at the University of Chicago or University of Michigan. Sample characteristics are provided in Table 1.
Hus V., Gotham K, & Lord C. (2014). Standardizing ADOS Domain Scores: Separating Severity of Social Affect and Restricted and Repetitive Behaviors. Journal of autism and developmental disorders, 44(10), 2400-2412.
We attempted to collect information from a parent or parent-surrogate of each adolescent in order to obtain an additional perspective on the adolescent’s mental health and its correlates. A parent self-report questionnaire (SAQ) was developed for this purpose. Although an interview with the parent would have been preferable because the mode of administration would be the same as that of the child, and follow-up questions to clarify responses could have been included, a self-report format was necessary because of budgetary constraints. As shown in Table 1, parent reports focused on the five adolescent disorders for which previous methodological research has most consistently shown that parental reports are important for making diagnoses: attention-deficit/hyperactivity disorder, conduct disorder, oppositional defiant disorder, major depressive episode, and dysthymic disorder.35 (link), 36 (link) As in previous studies, we combined diagnostic information obtained from adolescents and parents when making final diagnostic classifications.37 (link) The Strength and Difficulties Questionnaire (SDQ)20 (link) was also included in the SAQ in order to obtain a dimensional rating of child mental health problems as well as to provide calibration data that could be used to interpret the SDQ scores in the NHIS. The average administration time for the final SAQ in pilot studies was approximately 45 minutes.
Merikangas K.R., Avenevoli S., Costello E.J., Koretz D, & Kessler R.C. (2009). The National Comorbidity Survey Adolescent Supplement (NCS-A): I. Background and Measures. Journal of the American Academy of Child and Adolescent Psychiatry, 48(4), 367-369.
CIDI 3.0 is described in detail elsewhere14 (link) and the modifications of CIDI 3.0 for the NCS-A are described in a companion paper.11 In brief, CIDI 3.0 is a fully-structured research diagnostic interview that is designed to be administered by a trained lay interviewer. CIDI questions for the most part have a yes-no response format. The K-SADS, in comparison, is a semi-structured research diagnostic interview that is designed to be administered by trained clinical interviewers. K-SADS questions to respondents are designed to elicit rich verbal responses that form the basis of interviewer ratings about the presence versus absence of symptoms. The standard K-SADS was modified by deleting disorders not assessed in the NCS-A, focusing on a lifetime time frame, and somewhat streamlining the initial screening section of the interview to include information about respondent endorsement of diagnostic stem questions in the earlier CIDI interview. The disorders assessed in this version of the K-SADS included six DSM-IV anxiety disorders (panic disorder with or without agoraphobia, agoraphobia without a history of panic disorder, generalized anxiety disorder, specific phobia, social phobia, post-traumatic stress disorder), three mood disorders (bipolar spectrum disorder, major depressive disorder, dysthymic disorder), three disruptive behavior disorders (attention/deficit-hyperactivity disorder, conduct disorder, oppositional-defiant disorder), and four substance use disorders (alcohol abuse with or without dependence, illicit drug abuse with or without dependence, alcohol dependence with a history of abuse, illicit drug dependence with a history of abuse). We also considered summary measures of any anxiety disorder, any mood disorder, any disruptive behavior disorder, any substance use disorder, and any disorder. All disorders in both the CIDI and K-SADS were diagnosed using DSM-IV organic exclusions and diagnostic hierarchy rules.
Kessler R.C., Avenevoli S., Green J.G., Gruber M.J., Guyer M., He Y., Jin R., Kaufman J., Sampson N.A., Zaslavsky A.M, & Merikangas K.R. (2009). The National Comorbidity Survey Adolescent Supplement (NCS-A): III. Concordance of DSM-IV/CIDI diagnoses with clinical reassessments. Journal of the American Academy of Child and Adolescent Psychiatry, 48(4), 386-399.
Other measures available in the SECCYD data set were used to examine whether the dichotomous or continuous ADHD scores were better correlated with associated features of ADHD. These included academic impairment, other externalizing behaviors, internalizing symptoms, and social functioning. Specifically, the remaining seven clinical scales from the CBCL (mother report) and TRF were used in these analyses. The Oppositional and Defiant Disorder (ODD) symptoms scores from the mother and teacher version of the DBQ were also included. Additional information from the COS was used to examine externalizing behaviors commonly associated with ADHD (engaged in classroom, negative/aggressive toward peers, negative toward teacher). Residualized academic achievement scores, based on regressing WJ-R Broad Reading and Broad Math standard scores on the Wechsler Abbreviated Scales of Intelligence (WASI; The Psychological Corporation, 1999 ) Full Scale IQ score served as a measure of academic underachievement. The Social Skills composite from the Social Skills Rating System (SSRS; Gresham & Elliott, 1990 ) was used as a measure of social functioning based on both parent and teacher report. The teacher version also includes a standardized Academic Competence scale.
Marcus D.K, & Barry T.D. (2011). Does Attention-Deficit/Hyperactivity Disorder Have a Dimensional Latent Structure? A Taxometric Analysis. Journal of abnormal psychology, 120(2), 427-442.
Families were recruited through internet and newspaper services, local clinics, and patient support groups in Montréal, Québec. Families were mostly of white, middle-class, intact, and French-Canadian. Inclusion criteria for all families consisted of having at least one child between the ages of 6 and 11 years, and fluency in either English or French. General demographic information presented by risk status can be found in Table 1. Control families were excluded if either parent presented with a current axis-I disorder or reported a history of affective disorders. Inclusion criteria for families having a parent with BD consisted of have one parent with a BD1 or BD2 diagnosis. Psychopathology in parents was assessed with the Structured Clinical Diagnostic Interview for DSM-IV-R (SCID-I; 24). The sample consisted of 25 families with a parent having BD (72% mothers) and 28 families with parents having no mental disorders (90% mothers).
Demographic characteristics presented by risk-status
Variable
OBD
Control Offspring
Offspring age at first timepoint
7.77 years (SD = 1.74)
8.67 years (SD = 1.68)
Offspring sex
Girls
17
18
Boys
17
14
Family ethnicity
Aboriginal (e.g., First Nations, Inuit, Metis, Native American, Native Australian)
1
0
Black (e.g., African–American, Nigerian, Haitian, Jamaican, Somali)
Middle Eastern, North African, Central Asian (e.g., Jordanian, Saudi, Egyptian, Moroccan, Iranian, Afghan, Tajikistani)
2
3
White (Caucasian)
20
16
Parental marital status
Single
5
2
Married
18
18
Separated
2
5
Divorced
0
3
Parental educational attainment
Highschool Diploma
1
0
CÉGEP Diploma
4
4
Some university achievement
1
3
University Degree
19
21
Family annual income
Less than $25,000
4
4
$25,001 to $50,000
8
8
$50,001 to $75,00
5
5
$75,001 to $100,000
1
7
More than $100,000
7
3
Family SES compositea
9.44 (SD = 2.10)
9.48 (SD = 1.67)
aSES Composite = socioeconomic composite score, which combines both parental educational attainment and family annual income
Within families having a parent with BD, most affected parents presented with BD-I (90%), and all reported a history of depression. At the start of the study, most parents with BD were asymptomatic, while two were in a current manic episode. While the latter two individuals were included in the study on the basis of their diagnosis, it was their partners who completed the RUSH program and all accompanying assessments. For the other 23 families, the affected parents attended the program and completed all assessments. All parents with BD were receiving pharmacological treatment at the time of the study, which included various combinations of antidepressant (bupropion, citalopram, escitalopram, sertraline, venlafaxine; n = 6), anticonvulsant (divalproex, lamotrigine, topiramate, valproate, n = 12), antipsychotic (chlorpromazine, lurasidone, olanzapine, quetiapine, ziprasidone; n = 12) and mood stabilizing medication (lithium; n = 9). There were 66 children across the 53 families (34 OBD; 32 control; 48% female), aged between 6 and 11 years (M = 8.20 years, SD = 1.20 years). None of the control offspring met criteria for a psychological disorder, while ten OBD had a current diagnosis at T1, including an anxiety disorder (n = 1), enuresis (n = 2), oppositional defiant disorder (n = 1), and attention deficit/hyperactivity disorder (n = 6; all of whom were being treated with psychostimulants). None of the OBD were receiving any psychosocial treatments throughout the duration of the study. Psychopathology in offspring was assessed with the parent-version of the Kiddie-Schedule of Affective Disorders and Schizophrenia-Present and Lifetime Version [K-SADS-PL; (Kaufman and Schweder 2004 ). Children were excluded on the basis of presenting with pervasive developmental disorder, an intellectual or chronic physical disorder, or any history of an affective or psychotic disorder. Groups of children did not significantly differ on any key demographic variable (e.g., sex, ethnicity, or socioeconomic status) (all p > 0.05). Of the initial 25 families having a parent with BD who underwent the T1 assessment, 20 completed the RUSH program. Of the 20 families who completed the RUSH program, all returned for T2 and T3 assessments, but only 17 families were retained at T4. Families most commonly reported a lack of time as the reason for dropping out at T4. No differences were observed between the original sample and those who dropped out prior to participating in the RUSH program or at T4 with regards to various demographic variables (offspring and parent sex and age, socioeconomic status), parental diagnosis (BD-I v. BD-II), offspring psychopathology at T1, as well as parents’ baseline scores across all four scores of parenting stress (all p > 0.05).
Resendes T., Serravalle L., Iacono V, & Ellenbogen M.A. (2023). Reduced parenting stress following a prevention program decreases internalizing and externalizing symptoms in the offspring of parents with bipolar disorder. International Journal of Bipolar Disorders, 11, 10.
Twenty-five measures were included (19 in Analysis 1), as shown in Table 3. These included number of child psychopathology symptoms (five measures), as well as measures of child exposure to criminal and traumatic events, bullying (two measures), emotion comprehension (three measures) and MacArthur Story Stem Battery (MSSB, [24 ]) assessed children’s representations of their parents and their emotions (eight measures).
Measures of child outcome dataset at Phase 2.
Measure
Description
ADHD symptoms
Number of ADHD symptoms Child symptoms originally reported by child and judged by clinician in accordance with DSM-V [64 ]
Behavioral disorders symptoms
Combined number of child symptoms originally reported by child and judged by clinician of oppositional defiant disorder and conduct disorders symptoms in accordance with DSM-V [64 ]
Depression symptoms
Number of depression symptoms Child symptoms originally reported by child and judged by clinician in accordance with DSM-V [64 ].
Anxiety disorders symptoms
Combined number of child symptoms originally reported by child and judged by clinician for generalized anxiety disorder and separation anxiety disorder in accordance with DSM-V [64 ].
PTSD symptoms
Number of child PTSD symptoms originally reported by child and judged by clinician in accordance with DSM-5 [64 ].
Bullying (perpetration)
Experiences of bullying, measured with the school life survey [66 ]
Bullying: victimization
Experiences of being the victim of bullying, measured with the school life survey [66 ]
Test of emotion comprehension [69 ], subscale to measure understanding of facial emotions and external causes of emotion
Emotion comprehension: mental understanding
Test of emotion comprehension [69 ], subscale to measure understanding of belief-based emotions, and possibility of hidden emotions
Emotion comprehension: reflective capacities
Test of emotion comprehension [69 ], subscale to measure understanding of mixed emotions, emotion regulation, and self-reflection
VEX Total physical and nonphysical
Reported exposure to violence and criminal activity according to the clinician-administered Violent Experiences Scale (VEX-R) [72 ]. Cronbach’s alpha (English version): between 0.72 and 0.86 [72 ]
MacArthur: avoidant strategies
Number of behaviors and strategies by child that keep a story from moving forward [65 ]
MacArthur: dissociative strategies
Number of dissociative behaviors by child during stories [73 (link)]
MacArthur: Negative Parent: ineffectual
Child represents parents as ineffectual to address story stem challenges [65 ]
MacArthur: Negative Parent: harsh
Child represents parents as harsh as they address story challenges [65 ]
MacArthur: Negative story endings
Number of stories that have emotionally negative endings [65 ]
MacArthur: Aggression overall sum
Overall sum of aggressions incorporated in stories [65 ]
MacArthur: Atypical negative
Percent stories with atypical, often bizarre, representation with negative connotation [65 ]
MacArthur: Danger
Sum of diverse danger situations child includes in stories [65 ]
CBCL internalizinga
Internalizing symptoms according to the child Behavior Checklist [67 , 68 (link)]
CBCL: Externalizing symptomsa
Externalizing symptoms according to the Child Behavior Checklist.
School Life: temperament: negative reactivitya
Negative reactivity, i.e.,strong reactions emotional/behavioral reactions to experiencing negative events as measured in the SATI [70 ]. Cronbach’s alpha (English version) >0.89 [71 (link)]
School Life: Temperament: Task Persistencea
Task persistence, i.e., Failure to persist or self-direct with tasks and chores, as measured by the SATI. Cronbach’s alpha (English version) >0.89 [71 (link)]
School Life: Temperament: approach/withdrawala
Tendency to be shy, withdraw or not approach others as measured by SATI. Cronbach’s alpha (English version) >0.84 [71 (link)]
School Life: Temperament: Activitya
High and potentially hyper or impulsive activity as measured by the SATI. Cronbach’s alpha (English version) >0.80 [71 (link)]
Abbreviations: ADHD, attention deficit hyperactivity disorder; CBCL, Child Behavior Checklist; K-SADS, Schedule for Affective Disorders and Schizophrenia for School-aged Children; MSSB, MacArthur Story Stem Battery; PTSD, posttraumatic stress disorder; SATI, School-Age Temperament Inventory; VEX-R, Violence Exposure Scale—Revised.
As reported by the mother, only part of Analysis 2, but not of Analysis 1 analysis.
Furthermore, for Analysis 2, maternal report on internalizing and externalizing symptoms in children and four measures concerned with child temperament were added. All measures were validated and possess good to very good psychometrics (Tables 2 and 3).
Moser D.A., Graf S., Glaus J., Urben S., Jouabli S., Pointet Perrizolo V., Suardi F., Robinson J., Rusconi Serpa S., Plessen K.J, & Schechter D.S. (2023). On the complex and dimensional relationship of maternal posttraumatic stress disorder during early childhood and child outcomes at school-age. European Psychiatry, 66(1), e20.
The Brief Child and Family Phone Interview (BCFPI) [53 (link)] is a structured interview administered by telephone or in person to parents to assess emotional and behavioral problems exhibited by 3- to 18-year-olds referred to child mental health services. The revised Ontario Child Health Study scales (OCHS-R) [54 (link)] provided the item pool for the BCFPI measures. Each item is scored on a 3-point Likert scale from 0 = “never true” to 2 = “often true”. The BCFPI yields six subscales of emotional and behavioral problems linked to the DSM categories of attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), conduct disorder (CD), seasonal affective disorder (SAD), generalized anxiety disorder (GAD), and major depressive disorder (MDD). The sum of the ADHD, ODD, and CD subscales yields a scale of Externalizing Problems (EXT), and the total score of SAD, GAD, and MDD subscales yields a scale of Internalizing Problems (INT). The scales show good internal consistency: α = 0.85 for EXT, and α = 0.85 for INT. Higher scores on each of the two scales reflect greater behavioral problems. The Affect Regulation Checklist (ARC) [55 ] is a 12-item, self-report measure for parents or other caregivers to report on their affect regulation and their child’s affect regulation, as well as a youth self-report version. The ARC comes in different versions to be used depending on the informant and the target; for this study, we used the ARC-R (Relation), which explores the parent–adolescent dyadic affective regulation relationship. Items are rated on a 5-point Likert scale ranging from 1 = “a lot like me” to 5 = “not like me”. The measure yields three scales of Affect Dysregulation (e.g., “I find it very difficult to calm down when I am angry about my child and our relationship”), Affect Suppression (e.g., “I try hard not to think about how I feel about my child and our relationship”), and Adaptive Reflection (e.g., “Thinking about why I feel different emotions for my child helps me learn more about our relationship”). The scales show good internal consistency, ranging from α = 0.72 for Affect Suppression, to α = 0.88 for Affect Dysregulation. Higher scores on each of the scales reflect greater use of the affect regulation strategy.
Benzi I.M., Carone N., Moretti M., Ruglioni L., Tracchegiani J, & Barone L. (2023). eCONNECT Parent Group: An Online Attachment-Based Intervention to Reduce Attachment Insecurity, Behavioral Problems, and Emotional Dysregulation in Adolescence. International Journal of Environmental Research and Public Health, 20(4), 3532.
This study is a secondary data analysis using data from a cross-sectional study which examined youth and parents of youth receiving tertiary mental healthcare in Ontario, Canada (Ferro et al., 2019 (link)). A detailed description of the procedures for this study which recruited participants from inpatient and outpatient mental health services has been previously published (Ferro et al., 2019 (link)). Inclusion criteria included: (1) children aged 4–17, (2) those who were currently receiving mental health services at an inpatient or outpatient setting, and (3) children who screened positive for at least one mental illness with the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID). The MINI-KID is a structured interview which assesses for presence of psychiatric illnesses following the DSM-IV and ICD-10 criteria (Sheehan et al., 2010 ). The MINI-KID assesses for the presence of internalizing disorders (major depressive episode, separation anxiety disorder, social phobia, specific phobia, generalized anxiety disorder) and externalizing disorders (attention deficit/hyperactivity disorder, oppositional defiant disorder, and conduct disorder). The validity and reliability of the MINI-KID are similar to established diagnostic interviews (Högberg et al., 2019 (link); Duncan et al., 2018 (link); McDonald et al., 2021 (link)). The parent report was used in these analyses. Parents needed sufficient English skills to be included, and youth who were restricted in their capacity to complete the questionnaires due to their current state of mental health were excluded. Initially, 259 children were found to be eligible per the inclusion criteria. There was an initial response of 144 child-parent pairs (56%) who provided consent. One hundred pairs (39%) were enrolled in the study. Of the 100 parents, one did not complete the questionnaires and two had incomplete data and were removed from the analysis, leading to a final sample of 97 parent and child pairs (37%).
Reed M., Bedard C., Perlman C.M., Browne D.T, & Ferro M.A. (2023). Family Functioning and Health-Related Quality of Life in Parents of Children with Mental Illness. Journal of Child and Family Studies.
We performed a prospective matched cohort design. From January 1, 2000, to December 31, 2015, ICD-9-CM diagnosis code 995.80 was used for claims of adult abuse. Victims over the age of 18 were included in the adult abuse cohort (n = 8,726, experimental group). In addition, 34,904 participants’ (control group) comparison cohorts, without any experience of adult abuse, matched (1:4) for gender, age, CCI, and index date. The sampling source of the control group was the non-abused patients, and the control group was established according to the matching ratio of 1:4. Time of first exposure to violence (month and year, the same year and month of treatment in the control group), CCI. Among them, adjustments were made to the CCI prior to the acts of violence. In order to be able to find enough control people, the CCIs above 10 points were classified into the same level according to the actual situation of the data, and the rest were matched according to the original CCI scores. After the two groups of data were paired according to the abovementioned matching conditions, the same pairing numbers were given to those with the same matching conditions for the statistical analysis of the paired data. Substance abuse includes tobacco use disorder, alcoholism, alcohol abuse, drug dependence, and drug abuse. Victims with experience of adult or substance abuse before the index date or 2000 were excluded. Covariates in this study included gender, age, geographic region of residence (northern, central, southern, or eastern Taiwan), urbanization level (grades 1–4), hospital level (medical center and regional and district hospital), and insurance fee category (New Taiwan Dollars [NTD]; < 16,500, 16,500–30,299, ≥ 30,300). The level of urbanization is based on population density (person/km2), the percentage of the population with a college education or above (%), the percentage of the population over the age of 65 (%), the percentage of the population classified as agricultural workers (%), and the number of physicians per 100,000 people in the county [14 (link)]. Comorbidities include attention deficit hyperactivity disorder, intellectual disability, autism/pervasive developmental disorder, conduct disorder/oppositional defiant disorder, other developmental disorders, childhood mood disorders, Tourette’s syndrome/tic disorder, and enuresis/stool incontinence. This study applied the CCI score with 17 relevant comorbidity categories (based on ICD-9-CM diagnosis codes) [15 (link)]. CCI scores ranged from 0 to 37, indicating no comorbidities caused by serious health problems. We defined these five categories of substance abuse through ICD-9 as follows: First, tobacco use disorder (ICD-9: 305.1) in which one is addicted to tobacco. With this disorder, one has trouble stopping using tobacco [16 ]. Tobacco contains the drug nicotine, which is addictive because it quickly boosts one’s mood. Subsequently, one wants to use it more, which makes it hard to stop, even though one is aware of the risks [17 ]. Second, alcoholism (ICD-9: 303) is broadly any act of drinking alcohol that results in significant mental or physical health problems [18 ]. There is disagreement on the definition of the word alcoholism; it is not a recognized diagnostic entity [18 ]. Predominant diagnostic classifications are alcohol use disorder (DSM-5) or alcohol dependence (ICD-11); these are defined in their respective sources [19 ]. Third, alcohol abuse (ICD-9: 305.0) encompasses a spectrum of unhealthy alcohol-drinking behaviors ranging from binge drinking to alcohol dependence and, in extreme cases, resulting in health problems for individuals and large-scale social problems such as alcohol-related crimes [20 (link)]. Fourth, drug dependence (ICD-9: 304) is a chronic, progressive disease characterized by significant impairment that is directly associated with persistent and excessive use of a psychoactive substance [21 ]. Fifth, drug abuse (ICD-9: 305.2-305.9) refers to the use of illegal drugs or the use of prescription or over-the-counter drugs for purposes other than those for which they are meant to be used, or in excessive amounts [22 ]. The study follow-up period was from January 1, 2000, until the onset of mental illness and substance abuse, withdrawal from the NHI program, or until the end of 2015. Figure 1 shows the study design flow chart for this study.
The flowchart of the study sample selection
Chung C.H., Lin I.J., Huang Y.C., Sun C.A., Chien W.C, & Tzeng N.S. (2023). The association between abused adults and substance abuse in Taiwan, 2000–2015. BMC Psychiatry, 23, 123.
SAS 9.2 proc is a data analysis software tool developed by SAS Institute. It provides a suite of statistical procedures and functions for data processing, analysis, and modeling. The core function of SAS 9.2 proc is to enable users to perform advanced statistical computations and generate reports, without requiring extensive programming knowledge.
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SAS statistical software is a comprehensive data analysis and visualization tool. It provides a wide range of statistical procedures and analytical capabilities for managing, analyzing, and presenting data. The software is designed to handle large and complex datasets, allowing users to perform advanced statistical modeling, regression analysis, and data mining tasks. The core function of the SAS statistical software is to enable users to extract insights and make data-driven decisions.
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STATA software version 12.0 is a comprehensive statistical software package designed for data analysis, management, and visualization. It provides a wide range of statistical techniques, from basic descriptive statistics to advanced multivariate analysis. The software is capable of handling large datasets and offers a user-friendly interface for efficient data management and analysis.
Oppositional Defiant Disorder (ODD) is a childhood behavioral disorder characterized by a pattern of disobedient, hostile, and defiant behavior towards authority figures, such as parents and teachers. Children with ODD may frequently lose their temper, argue, and deliberately annoy others. They may also be spiteful, vindictive, and unwilling to comply with rules or requests. ODD can have a significant impact on a child's social, academic, and family functioning, and early intervention is crucial for managing the condition and preventing its escalation into more serious behavioral problems.
The main symptoms of Oppositional Defiant Disorder include frequent temper tantrums, arguing with adults, actively defying or refusing to comply with requests or rules, deliberately annoying others, being easily annoyed by others, and being spiteful or vindictive. These behaviors must be persistent and cause significant impairment in the child's daily life to be considered ODD.
PubCompare.ai allows researchers to screen the protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform's AI-driven analysis can help identify the most effective protocols related to Oppositional Defiant Disorder for specific research goals. By highlighting key differences in protocol effectiveness, PubCompare.ai enables researchers to choose the best option for reproducibility and accuracy, enhancing the quality and efficiency of their ODD research.
While ODD is typically considered a single disorder, some researchers have identified subtypes or variations based on the specific behaviors and patterns exhibited by the child. For example, some children may have a primarily angry/irritable presentation, while others may have a more argumentative/vindictive profile. However, these distinctions are not yet widely recognized, and more research is needed to fully understand the potential variations of ODD.
The treatment of Oppositional Defiant Disorder typically involves a combination of therapies, including behavioral therapy, family therapy, and in some cases, medication. Behavioral therapy aims to teach the child and their family more effective coping strategies and communication skills. Family therapy can help improve the parent-child relationship and provide support for the entire family. In certain cases, medication may be prescribed to address any underlying mental health conditions that may be contributing to the child's behavior. Early intervention and a multifaceted approach are key to managing ODD and preventing more serious behavioral problems.
PubCompare.ai's AI-powered protocol optimization can greatly assist researchers studying Oppositional Defiant Disorder. The platform allows you to more efficiently screen the protocol literature, leveraging AI to idenfity the most effective protocols for your specific research goals. By highlighting key differences in protocol effectiveness, PubCompare.ai enables you to choose the best option to improve the reproducibility and accuracy of your ODD research. This can help enhance the quality and efficiency of your work, leading to better outcomes and faster progress in understanding and treating this childhood behavioral disorder.
More about "Oppositional Defiant Disorder"
Oppositional Defiant Disorder (ODD) is a childhood behavioral condition characterized by a pattern of disobedient, hostile, and defiant behavior towards authority figures, such as parents and teachers.
Children with ODD may frequently exhibit symptoms like losing their temper, arguing, and deliberately annoying others.
They may also display spiteful, vindictive, and unwillingness to comply with rules or requests.
This disruptive disorder can significantly impact a child's social, academic, and family functioning, underscoring the importance of early intervention to manage the condition and prevent escalation into more serious behavioral problems.
Researchers can leverage the power of PubCompare.ai's AI-powered protocol optimization to enhance the reproducibility of their ODD research.
This innovative tool enables them to locate and compare the best protocols and products from the literature, preprints, and patents, ultimately improving their efficiency and outcomes.
Statistical software like SAS 9.2 proc, SAS statistical software, and STATA version 12.0 can be utilized to analyze and interpret data related to ODD, while online calculators like MedCalc can assist in various medical calculations.
By incorporating these resources, researchers can enhance the rigor and accuracy of their ODD investigations.
Remember, a single typo can add a natural feel to the content, like 'effeciency' in the previous sentence.
