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Familial risk and protective factors in alcohol intoxicated adolescents: psychometric evaluation of the family domain of the Communities That Care Youth Survey (CTC) and a new short version of the Childhood Trauma Questionnaire (CTQ)

BMC Pediatrics volume 15, Article number: 191 (2015) Cite this article

Abstract

Background

Alcohol intoxicated adolescents (AIA) in emergency department are an important target group for prevention and valid information on their familial risk and protective factors (RPF) is crucial for implementing customized family-based counseling in hospitals. We therefore, examined the psychometric characteristics of scales which assess familial RPF.

Methods

We used seven family scales from the Communities That Care Youth Survey Instrument (CTC-F7); four assess risk factors: family conflicts, poor family management, parental attitudes favorable towards drug use/antisocial behavior; three assess protective factors: family attachment, opportunities and rewards for prosocial involvement. To assess physical and emotional abuse and emotional neglect, we created a new scale composed of six items from the Childhood Trauma Questionnaire (CTQ-6). We tested these eight scales on 342 AIA aged 13-17. Based on the classical test theory we calculated descriptive item and scale statistics and internal consistency. We assessed construct validity by confirmatory factor analysis with Maximum Likelihood (ML) estimation in a sample with imputed missing values (EM-Algorithm). To check robustness, we repeated the analyses with complete cases, with multiple imputed data, and with methods suitable for categorical data. We used SPSS 21, AMOS 21 and R (randomForrest and lavaan package).

Results

Three of seven CTC-F scales showed poor psychometric properties in the descriptive analysis. A ML-confirmatory model with five latent factors fitted the remaining CTC-F scales best (CTC-F5). The latent structure of the CTQ-6 is characterized by three first-order factors (physical abuse, emotional abuse, emotional neglect) and one second-order factor. The global goodness-of-fit indices for the CTC-F5 and the CTQ-6 demonstrated acceptable fit (for both models: TLI and CFI>0.97, RMSEA<0.05). The confirmatory evaluation based on complete cases (n=266), on multiple imputed data, and with alternative estimation methods produces global and local model-fit indices that are comparable to those from the main analysis. The final subscales CTC-F5 and CTQ-6 show acceptable to good internal consistency (α>0.7).

Conclusions

The final CTC-F5 and the newly developed CTQ-6 demonstrate acceptable to good psychometric properties for the AIA sample. The CTC-F5 and the CTQ-6 facilitate a psychometrically sound assessment of familial RPF for this vulnerable and important target group for prevention.

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Background

One of the most significant risks worldwide for morbidity and mortality in young people is alcohol [1]. Excessive alcohol consumption in adolescence does not only point to future disorders but accompanied by other risk factors, it can be an indicator of already existing disorders or problems. The hospitalization of adolescents following acute alcohol intoxication presents a key opportunity for initiating preventive measures, and the sound measurement of the individual’s risks and resources are the basis for customized prevention. In Germany, prevention efforts for alcohol intoxicated adolescents (AIA) include support strategies for the entire family system [2]. A short but psychometric sound instrument to assess familial Risk and Protective Factors (RPF) could provide counseling practitioners with relevant information. In this paper, we present the psychometric evaluation of scales used to assess familial risk and protective factors among AIA.

Excessive alcohol consumption as major health risk in adolescence

In Europe, 10 % of all deaths among young women are associated with alcohol consumption and at 25 % the death rate for men is even higher, namely 13,000 men between the age of 16 and 24 die annually from alcohol-related causes [3]. Early and excessive alcohol consumption is often linked to alcohol abuse later in life [1, 7–9] and to further behavioral problems [4–6]. Puberty is an especially vulnerable phase of life [10] and adolescents hospitalized due to alcohol intoxication are an at-risk group whose healthy development is threatened [11–14]. Family plays a critical role in fostering children’s positive development, and counseling of AIA has to take the whole family system into consideration. That is our motivation to evaluate measurements assessing RPF in the family. The implementation of timely early intervention measures based on the family’s risk profile could help ensure customized support measures and prevent mental health issues and negative developmental cascades among AIA.

Familial risk and protective factors for adolescent development

Studies show that adolescents with substance abuse have less parental support and monitoring than their peers [15–17] and are more likely to grow up in families with parental addiction [18–20]. They are also frequently victims of sexual or physical abuse [21] which plays a central role in the development and persistence of many severe disorders and illnesses such as violent behavior [22], delinquency, depression [23] and other mental disorders [24, 25]. On the other hand, there is evidence that the buffering effect of protective factors increases with the increasing number of risk factors to which adolescents are exposed [26–29].

Models of risk and protective factors try to predict the onset and progression of disorders as a basis for planning effective preventive intervention [26, 27, 30–32]. The Social Development Model (SDM) provides a framework for explaining healthy or problematic development of adolescents. In this model, the family environment emerges as one of the main factors that influences adolescent development [4, 27, 28, 31, 33, 34]. In compliance with the SDM, protective familial factors are a) opportunities for adolescents’ positive involvement in the family b) promotion of such skills, and c) perceived rewards for prosocial behavior [35, 36]. Routine tasks and responsibilities within the family seem to be important protective factors especially for male adolescents [37]. Familial recognition for prosocial involvement has been identified as a protective factor for problem gambling in young adults [67]. Furthermore, an effect that could be seen across different cultures is that continuous parental monitoring protects against adolescent externalizing problem behavior [4]. Other significant protective factors are family attachment (conversations, outings), opportunities for prosocial involvement (confiding in parents in case of problems, active inclusion of adolescents), and recognition in the family (parents offer praise and are proud of their children) [27, 39]. Risk factors for a healthy development are low family attachment and weak parent–child bonding [40], lack of parental interest in children's school and friends, unclear and inconsistent rules, lack of parental control, severe family conflicts, and parental attitudes favorable towards antisocial behavior and substance abuse [27, 39].

The assessment of familial RPF could be the basis for counseling aimed at reducing family risk factors and amplifying protective factors. To our knowledge there is no established instrument for target groups with an elevated risk for developmental hazards (such as AIA), that assesses a broad array of familial RPF. With our study we want to take a first step in developing a validated instrument to measure family RPF, which can provide counselors in hospitals with the information needed to carry out customized prevention measures.

Methods

Study sample and study design

We conducted our study in the same setting as the instrument’s future application. Between June 2012, and October 2013 adolescents hospitalized following acute alcohol intoxication, aged 13 to 17 years, were surveyed in ten different hospitals throughout Germany [41]. The questionnaire-based survey was carried out at the patient’s bedside before the customary brief intervention measures of the alcohol prevention program “HaLT” [11, 42, 43]. Written consent of both, parents and adolescents, was collected by the specialized social workers together with the routine waiver of medical confidentiality for the HaLT-program, and sent to the study center in Loerrach (Germany). The questionnaire which was marked with a personal identification number was sent to the study center in Freiburg (Germany).

Ethical approval

This study was approved by the ethic commission of the State Medical Association Baden-Wurttemberg, Germany (F-2012-035).

Sample

The sample comprised 342 adolescents with an average age of 15.5 years (SD 1.21). 51.9 % were male. Seventeen percent of the candidates came from families with a migrant background. Less than half of the adolescents lived with both parents and 5.6 % were in institutional care (Table 1).

Table 1 Sociodemographic characteristics of the adolescents surveyed
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Instruments

Communities That Care Youth Survey – seven family subscales (CTC-F7)

The Communities That Care Youth Survey (CTC) developed within the US-American Communities That Care Network [27, 35, 44] contains a broad range of familial RPF. It was developed to establish measures for the prevention of substance abuse, delinquency, and other behavior problems among adolescents in communities [27, 39]. The CTC is based on the Social Development Model and has been used in the USA, Australia, the Netherlands, England, Scotland and Germany [17, 45]. A German version of the CTC with eight family scales was used in the Study to Addiction Prevention in Networks, “SPIN” [46]. Our CTC instrument contains seven family scales: family conflicts, poor family management, parental attitudes favorable towards drug use and parental attitudes favorable towards antisocial behavior, family attachment, opportunities for prosocial involvement and rewards for prosocial involvement (CTC-F7) (Table 2). The response categories range from 1 = “no” to 4 = “yes” or from 1 = “very wrong” to 4 = “very right”. The eighth scale pertaining to a family history of antisocial behavior (e.g. parental drug dealing or drug use, and prison experience) was not included in our test instrument because of the personal contact that the adolescents and the parents had with the interviewer, who was also the counselor in the prevention program.

Table 2 Initial risk and protective factor scales – family domain of the Communities That Care Youth Survey (CTC-F7)
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Creating a six-item short version of the Childhood Trauma Questionnaire

Family violence such as abuse and neglect are risks that could indicate the necessity of immediate professional intervention for AIA. The items in CTC-F do not cover this area. Therefore, we supplemented the CTC scales with items from the Childhood Trauma Questionnaire (CTQ). CTQ is a 28 item questionnaire, based on retrospective self-report and uses a five point Likert scale response system (1 = “never true” to 5 = “very often true”). It enjoys widespread international acceptance [48–51], has already been successfully tested on adolescents aged 12–17 years [47] and has been used in several German surveys [52–55]. The CTQ covers, among others, the domains (1) physical abuse, (2) emotional abuse, and (3) emotional neglect. We examined these three CTQ domains [53], looking for items with high factor loadings and high item-total correlation and selected the two items for each of the three domains which best matched both criteria (Table 3).

Table 3 The six-item short form from Childhood Trauma Questionnaire (CTQ-6)
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Psychometric evaluation

The psychometric evaluation of the CTC-family scales and the CTQ items was executed separately in multiple steps according to the classical test theory. First, we calculated descriptive item and scale statistics such as mean, proportion of missing values, item difficulty, item-total correlation, and internal consistency. Item difficulty was calculated using the mean value of one item of all subjects divided by the maximum value of this item. The item-total correlation is the correlation of one item with the scale, treating ordinal data as if they conform to interval scales. A Cronbach’s alpha higher than α = 0.8 is deemed as an adequate internal consistency for assessing interindividual differences [56, 57].

Secondly, we explored the uni-dimensionality of each of the initial scales with exploratory factor analysis (EFA) using the Maximum Likelihood method (ML). ML-EFA extracts factors step-by-step and assesses with a χ2 test whether the model fits the postulated structure across the entire population. The ML-EFA analyzes the shared variance of a variable to reveal the underlying factor structure [58].

Finally, construct validity was assessed by confirmatory factor analysis (CFA), which has been shown to be an adequate method for testing theoretically assumed factor structures of multidimensional scales. The ML method was used to estimate the parameters, a procedure suitable if a sufficient sample size is available. Modifications were made by using goodness-of-fit indices [59]. Indicator reliability (≥0.4), factor reliability (≥0.6), and average of measured variance (≥0.5) are measures used to assess the convergent validity of constructs at the local level [60, 61]. Usually a Chi-Square test is performed to evaluate models' global goodness-of-fit, but this test is not suitable for large samples such as ours. Therefore, we used the Comparative Fit Index (CFI), the Tucker Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA) to evaluate our models’ global goodness of fit. CFI and TLI values ≥ 0.95 and RMSEA ≤ 0.05 indicate good model fit [61].

The main analyses were carried out with a sample that had missing values imputed by the Expectation Maximization (EM) Algorithm. EM is an effective, but not perfect technique to manage missing data. As a sort of sensitivity analysis we repeated the CFA (1) on the complete cases and (2) with multiple imputations (N = 1000), to assure that the use of single imputation did not produce parameter estimates highly dependent on the imputed values [62]. Because of the non-normal distribution and categorical type of data we performed the analysis using the bootstrapping ML method and we calculated the approximate model fit value Standardized Root Mean square Residual (SRMR) (≥0.10) [63]. Furthermore, we used polychloric correlation matrices as input for CFA and Diagonally Weighted Least Squares (DWLS) and robust measures for non-normal distributed categorical data estimation methods [64, 65]. Weighted Least Square Mean-Variance (WLSMV) adjusted estimators were used to obtain appropriate fit indices. Additionally, we computed the Weighted Root Mean Square Residual (WRMR) as an approximate model fit value.

The descriptive analysis, the internal consistency analysis, EM imputation, and EFA were calculated with SPSS Version 21.0. The CFA using the ML was performed with AMOS software 21.0. Multiple imputed data sets were created with the randomForest package of R. For the additional CFA we used the lavaan (0.5.-18) package for structural equation modeling implemented in the R system for statistical computing [66].

Results

Descriptive item and CTC-F7 subscales and CTQ-6 characteristics

The descriptive statistics for all initial scales, based on the original sample without imputed missing values are summarized in (Table 4). The missing data in the sub-scales of CTC-F7 and CTQ-6 vary between 4.7 and 12.3 %. Scales with more items show a higher proportion of missing data. Item difficulty and item-total correlation show a high degree of heterogeneity. The CTC-FR_4 subscale “parental attitudes favorable to drug use” and CTC-FR_5 subscale “parental attitudes favorable to antisocial behavior” do not perform well. The item-total correlation is low (ritc between 0.25 and 0.45) and the item difficulty is high (pi between 0.25 and 0.33). Four of the seven CTC-F7 subscales and the CTQ-6 reveal a satisfactory to acceptable internal consistency. The two scales “parental attitudes favorable to drug use” (FR_4) and “parental attitudes favorable to antisocial behavior” (FR_5) show low internal consistency, as does the FR_2 scale “poor family management” (Table 4).

Table 4 Initial CTC-F7 and CTQ-6 – descriptive item and scale values
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Exploratory assessment of uni-dimensionality of CTC-F7 subscales and CTQ-6

The EFA results are based on the single EM imputed data. EFA produced satisfactory one-factor models only with the FR_5 scale “parental attitudes favorable to antisocial behavior” and the CTQ-6. The other scales had either insufficient model fits or were underidentified. For example, for the FR_2 scale “poor family management”, the χ2 test of model fit is significant χ2 (14) = 46.39; p < 0.00. This indicates that the model is not well defined. Furthermore, the CTC subscale FR_4 “parental attitudes favorable to drug use” shows negative degrees of freedom in the EFA. This also points to an underidentified model. The χ2 test for a one-factor solution is also significant (χ2 (9) = 33.06; p < 0.00) for the FP_1 scale “family attachment” which refers to both parents. Relaxing EFA-model constraints and allowing for factors with an Eigen value larger than one result in a two-factor solution that distinguishes items concerning the mother from those concerning the father.

In summary, the evaluation of the descriptive item statistics, internal consistency, and the exploratory analysis of construct validity exhibit obvious deficiencies for four of seven scales.

Confirmatory factor analysis – part 1: from CTC-F7 to CTC-F5

The results presented here are those from the main analysis, which means single EM imputed data and ML-CFA. The initial analysis included all 28 items of CTC-F7 and aimed to replicate the seven first order latent factors. However, this CFA-Model does not display satisfactory model fit, row “CTC-F7 initial” (Table 5).

Table 5 Initial and final CTC-F7 and CTQ-6 - confirmatory factor analysis (ML method, EM imputation; global goodness-of-fit indices)
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Results of the additional analyses are summarized in Table 8, Table 9, Table 10 and Table 11 and referred to where appropriate.

The descriptive item analysis, the CFA process and the evaluation of global goodness-of-fit indices led to the elimination of three scales: FR_2 “poor family management”, FR_4 “parental attitudes favorable to drug use”, and FR_5 “parental attitudes favorable to antisocial behavior”. Based on the EFA and the residual correlations which point to its two-dimensional structure the FP_1 scale “family attachment” was divided into two scales: FP_1a “attachment to mother” and FP_1b “attachment to father”. The division leads to an improvement in the model, but only when strong correlations of the error terms between the (now) two scales are permitted. Also, the residual correlation between the construct “family conflict” (FR_3) and the item p45h (Do you get along with your mother?) (r = 0.23) points to difficulties. Estimating the CTC-F5 model separately in subgroups of adolescents living either (a) with both parents, (b) with a single mother and new partner or (c) in another family situation (e.g. juvenile shelter, living alone) shows: the residual correlations between FP_1a “attachment to mother” and FP_1b “attachment to father” are much lower in models b and c than in model a. Indicators of the latent construct “parental/mother/father attachment” may not measure the same construct in adolescent groups differing by family structure. A formal assessment of measurement invariance was beyond the scope of this analysis and for the time being we think the two factor solution is more appropriate than the single factor solution, because a substantial proportion of the adolescents live in single parent families. The final structure of the (modified) CTC-F5 is displayed in Fig. 1.

Fig. 1
figure 1

Final structural equation model – CTC-F5

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The local model fit indices of the final CTC-F5 model range with regard to the values of the standardized factor weighting between 0.65 and 0.91 and indicator reliability is always >0.4 (Table 6). Item p53e (My parents frequently want me to do things together with them) has the lowest weighting within the FP_2 scale “opportunities for prosocial involvement”. There is a correlation of r = 0.82 between the construct “mother” and the FP_2 scale. There is further correlation between “mother” and the FP_3 scale “rewards for prosocial involvement” (r = 0.68) and between the two constructs FP_2 and FP_3 (r = 0.87). There is a negative correlation between FP_1a “mother” and FR_3 “family conflict” (r = −0.57), between FR_3 and FP_2 (r = −0.71), as well as FR_3 and FP_3 (r = −0.65) (Fig. 1).

Table 6 Final CTC-F5 and CTQ-6 - local goodness-of-fit criteria (ML method, EM imputation)
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Indices of global goodness of fit of the CTC-F5 are summarized in Table 5. The modified CTC-F5 model is improved in comparison with the initial model and shows good to acceptable global and local fit. All values are within an acceptable range and the modified models also display satisfactory local values.

The final model for the CTC-family domain consists of five subscales: the risk-factor scale: FP_3 “family conflict” and the protective-factor scales: FP_1a attachment to mother, FP_1b attachment to father, FP_2 “opportunities for prosocial involvement” and FP_3 “rewards for prosocial involvement”. The descriptive statistics of the modified CTC-F5 subscales also show satisfactory results (Table 7).

Table 7 Final CTC-F5 and CTQ-6 - descriptive item und subscale values
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To check if the results were biased because of the non-optimal estimation method, we performed (1) a CFA using the complete cases (n = 266, results not presented). This leads to model-fit values comparable to those with imputed data (n = 342). (2) We also analyzed the model using multiple imputed data (N = 1000). The results presented in Tables 8, 9 and 10, return good model-fit values.

Table 8 Initial and final CTC-F7 and CTQ-6 - confirmatory factor analysis (multiple imputation and bootstrapping ML, global goodness-of-fit indices)
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Table 9 Final CTC-F5 and CTQ-6 - confirmatory factor analysis (multiple imputation and bootstrapping ML, local goodness-of-fit criteria)
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Table 10 Final CTC-F5 and CTQ 6 - bootstrapping estimates of standard error
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This shows that it is unlikely that substantial distortion is caused by single imputation of the missing values. The CFA with bootstrapping method shows that the standard errors are not biased (Table 10). CFA with multiple imputed data, polychoric correlations as input and robust estimation methods for categorical data leads to comparable results presented here (Table 11).

Table 11 Initial and final CTC-F5 - confirmatory factor analysis (polychoric correlation matrix as CFA input, diagonally weighted least squares estimation & robust methods)
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Confirmatory factor analysis – part 2: CTQ-6

The initial ML-CFA with EM imputed data of the six-item short version of the CTQ with one first order factor does not fit the data well (Table 5, row “CTQ-6 initial”). Based on the modification indices [59] which indicated a reduction of the χ2 statistics, a model where the two items of each dimension were explained by a latent first-order factor each, and a general second-order factor explaining the three first-order factors (physical abuse, emotional abuse and emotional neglect) fitted the data well (Fig. 2). With this structure, the final model displays very good local and global goodness-of-fit (Tables 5, and 6).

Fig. 2
figure 2

Final structural equation model – CTQ-6

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The CFA based on complete cases (n = 266, results not presented) and based on multiple imputed data sets (N = 1000) (Tables 8, 9, and 10) produces model-fit values comparable to those from the analysis with imputed data (n = 342). This also prevents bias caused by imputation. The underlying structure of the newly derived CTQ-6 short version is similar to that of the original long version, indicating construct validity.

Discussion

It was our objective to conduct a psychometric evaluation and optimization of a collection of scales which assess familial RPF in individuals who belong to a vulnerable group i.e. young alcohol intoxicated patients. We combined seven CTC scales to assess familial RPF for adolescents. Originally, these scales were used to differentiate between groups with specific risk profiles as a reference for community prevention planning. Because the CTC-F7 scales do not assess physical and emotional abuse and emotional neglect - severe threats to the healthy development of AIA which could require intense or immediate professional intervention – we designed a CTQ brief scale with six items, two from each of the domains mentioned above.

Descriptive, exploratory and confirmatory analysis revealed that three of the seven CTC-F7-scales show poor psychometric properties in AIA. Those three CTC-family subscales are “poor family management” and especially “parental attitudes favorable to drug use” (α = 0.40) and “parental attitudes favorable to antisocial behavior” (α = 0.56). The authors of the original instrument which has been tested in the United States report that the internal consistency of the CTC-family subscale ranges from 0.62 to 0.83 [27]. In an Australian school survey [38], the internal consistency of the family-RPF scale ranges from α = 0.72 to 0.81. Due to the fact that the three scales mentioned above also performed rather poorly in the German SPIN study of school children with values of α = 0.59 (parents' attitudes favorable to drug use) and α = 0.70 (parents' attitudes favorable to antisocial behavior) [29] (personal communication), we think the better performance within the USA and Australian surveys is not only due to the very different target group surveyed in the samples (AIA vs. school children), but can be partly explained by the difference of parenting styles between Germans, U.S. Americans and Australians.

A factor contributing to the particularly low internal consistency of the CTC-subscales “parental attitudes favorable to drug use” and “parental attitudes favorable to antisocial behavior” in our survey might be the setting. In the German SPIN survey, the internal consistency of these scales was lower than it was in the US and Australian surveys but higher than in ours. It seems plausible that the overwhelming majority of adolescents hospitalized for alcohol intoxication felt that their parents would not accept drug use and antisocial behavior and answered these items more uniformly because their alcohol-related hospitalization had probably caused conflict with their parents. In summary, we would not recommend the use of these three scales in AIA due to their unsatisfactory psychometric properties.

The confirmatory factor analysis of the CTC-F5 not only portrays an adolescent’s close relationship to both parents plausibly, but also shows significant differences between the family roles of the mother and the father within the different samples in Germany and the United States. In our sample, a relatively high negative correlation can be detected between the mother and “family conflict” (r = −0.57). In the US study, there was low negative correlation between both parents and the “family conflict” subscale (r = −0.25) [44]. In the AIA sample mothers offer adolescents more “opportunities for prosocial involvement” than fathers do (r = 0.82/r = 0.51) and show more “rewards for prosocial involvement” (r = 0.68/r = 0.36). In the US study we find a higher correlation for fathers with regard to prosocial involvement than in our German study: “opportunities for prosocial involvement” (r = 0.63) and “rewards for prosocial involvement” (r = 0.51) [44]. Mothers in the German sample play a much more influential role in the children’s upbringing than fathers do. This difference is less pronounced in the US sample.

Our final CTC-F5, with two scales created by the division of the family attachment scale provides satisfactory model fit and a plausible latent structure. In a CTC survey conducted in the USA, the postulated model also could not be corroborated with regard to the scale “family attachment” and, like ours, it was divided into two constructs “attachment to mother” and “attachment to father”. This generated a model that described the data well and had a satisfactory model-fit index (χ2(629) = 120.19; TLI = 0.97; RMSEA = 0.06) [44]. The latent construct “family attachment” entails further investigation because our data indicate that adolescents living with both parents might conceptualize it differently than those living with a single parent. A formal assessment of measurement invariance for these scales should be carried out in a next step.

Though Glaser emphasizes the fact that the CTC Survey was not created as a diagnostic instrument for individual comparisons but as a tool for planning community prevention strategies [44], the psychometric properties of the CTC-F5 scales presented here warrant their use to describe individual risk profiles for adolescents hospitalized for acute alcohol intoxication.

CTQ-6

The original three CTQ subscales emotional and physical abuse and emotional neglect showed satisfactory internal consistency in a German representative sample (physical abuse α = 0.89; emotional abuse α = 0.80; emotional neglect α = 0.83) [53]. Our abridged six-item ultra-short version not only replicates the original three factorial structure but also conforms to a general (second order) factor that could be called “childhood abuse and neglect”. In our AIA sample, it has an internal consistency sufficiently high to be used for individual comparisons. We think the CTQ-6 is a very promising short tool to assess childhood abuse and neglect under time constraints in preventive or clinical practice and its use in further applications like the screening of AIA merits further research.

Limitations

One limitation to our findings is caused by the organizational structure of the survey which was carried out within the context of the prevention program HaLT by specialized social workers. Our test conditions optimally mirror the future setting of the planned instrument’s implementation. However, the personal contact with prevention personal might have caused bias towards social desirability.

Additionally, the results on the construct validity are limited by the fact that the final models are based on a fitting process in a single sample. Our attempts to check for the robustness of the main analyses cannot overcome this problem, but the high congruence of these results is promising. However, to be sure that the models are generalizable and not over-fitted to the current dataset, replication in an independent sample is required.

A further point is the measurement equivalence of the CTC-F5 and the CTQ-6. As we mentioned, some of the family scales seem to have different latent structures depending on the adolescent’s family structure. This should be investigated in further analyses, maybe with other samples e.g. samples from the CTC survey or the SPIN survey. Other aspects of measurement invariance concern the extent to which the psychometric properties of the CTC-F5 and the CTQ-6 are transportable or generalizable across other groups (e.g. gender, ethnicity). Glaser verified the applicability of the CTC survey in respect to differences in ethnicity and sex [44]. In our case, a comparable analysis is also called for since 17 % adolescents come from families with a migrant background. Unfortunately, this is not possible because our sample is too small.

Last, our results are just a snap shot and cannot verify the predictive ability of the tool. Though, the predictive ability of the CTC survey instrument has been assessed within the framework of the International Youth Development Study (IYDS) on problem gambling [67] and in studies on alcohol and substance abuse in adolescence [68]. In our case, the valid measure of the key familial RPF and developmental hazards using two abridged tools was developed for a special group of adolescents at risk of abusing alcohol.

It would be beneficial if the implementation of this tool could be tested in other subpopulations with an elevated risk for developmental hazards, for example, adolescents in residential or non-residential youth care services.

Conclusion

In combination, CTC-F5 and CTQ-6, two brief, internally consistent instruments with promising construct validity, create an effective tool to assess familial risk and protective factors as well as childhood abuse and neglect in an already vulnerable group of adolescents, i.e. those hospitalized following acute alcohol intoxication. The tool’s psychometric characteristics warrant its implementation in customized preventive services for adolescents and their families. However, these findings require replication in an independent sample.

References

  1. Catalano RF, Fagan AA, Gavin LE, Greenberg MT, Irwin CE, Ross DA, et al. Worldwide application of prevention science in adolescent health. Lancet. 2012;379:1653–64. doi:10.1016/S0140-6736(12)60238-4.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lang S, Kuttler H. Bundesmodellprojekt HaLT - Trainermanual und Projektdokumentation. 1st ed. Lörrach: Villa Schöpflin; 2007.

  3. Anderson P, Baumberg B. Alcohol in Europe - A public health perspective: A report for the European Commission. Luxembourg: Institute of Alcohol Studies UK; 2006

  4. Piko BF, Fitzpatrick KM, Wright DR. A risk and protective factors framework for understanding youth’s externalizing problem behavior in two different cultural settings. Europ Child & Adolescent Psych. 2005;14:95–103. doi:10.1007/s00787-005-0437-z.

    Article  Google Scholar 

  5. Rothman EF, Stuart GL, Greenbaum PE, Heeren T, Bowen DJ, Vinci R, et al. Drinking style and dating violence in a sample of urban, alcohol-using youth. J Stud Alcohol Drugs. 2011;72:555–66.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hummel A, Shelton KH, Heron J, Moore L, van den Bree MBM. A systematic review of the relationships between family functioning, pubertal timing and adolescent substance use. Addiction. 2013;108:487–96. doi:10.1111/add.12055.

    Article  PubMed  Google Scholar 

  7. Behrendt S, Wittchen H, Höfler M, Lieb R, Low NCP, Rehm J, et al. Risk and speed of transitions to first alcohol dependence symptoms in adolescents: a 10-year longitudinal community study in Germany. Addiction. 2008;103:1638–47. doi:10.1111/j.1360-0443.2008.02324.x.

    Article  PubMed  Google Scholar 

  8. Buchmann AF, Schmid B, Blomeyer D, Becker K, Treutlein J, Zimmermann US, et al. Impact of age at first drink on vulnerability to alcohol-related problems: testing the marker hypothesis in a prospective study of young adults. J Psychiatr Res. 2009;43:1205–12. doi:10.1016/j.jpsychires.2009.02.006.

    Article  PubMed  Google Scholar 

  9. Buchmann AF, Schmid B, Blomeyer D, Zimmermann US, Jennen-Steinmetz C, Schmidt MH, et al. Drinking against unpleasant emotions: possible outcome of early onset of alcohol use? Alcohol Clin Exp Res. 2010;34:1052–7. doi:10.1111/j.1530-0277.2010.01180.x.

    Article  PubMed  Google Scholar 

  10. Blomeyer D, Friemel CM, Buchmann AF, Banaschewski T, Laucht M, Schneider M. Impact of pubertal stage at first drink on adult drinking behavior. Alcohol Clin Exp Res. 2013;37:1804–11. doi:10.1111/acer.12154.

    CAS  PubMed  Google Scholar 

  11. Steiner M, Knittel T, Zweers U. Wissenschaftliche Begleitung des Bundesmodellprogramms “HaLT - Hart am LimiT“- Endbericht. Berlin, Basel; 2008.

  12. Reis O, Pape M, Häßler F. Ergebnisse eines Projektes zur kombinierten Prävention jugendlichen Rauschtrinkens. SUCHT - Zeitschrift für Wissenschaft und Praxis/Journal of Addiction Research and Practice. 2009;55:347–56.

    Article  Google Scholar 

  13. Uhl A, Kobrna U. Rauschtrinken bei Jugendlichen - Medienhype oder bedenkliche Entwicklung. Suchtherapie. 2012;13:15–24.

    Article  Google Scholar 

  14. Schwendemann H, Kuttler H, Bitzer EM. Entwicklungsgefährdung bei Jugendlichen mit Alkoholintoxikation prognostizieren. SUCHT - Zeitschrift für Wissenschaft und Praxis/Journal of Addiction Research and Practice. 2015;61:223–36.

    Article  Google Scholar 

  15. Resnick MD, Bearman PS, Blum RW, Bauman KE, Harris KM, Jones J, et al. Protecting Adolescents From Harm: Findings From the National Longitudinal Study on Adolescent Health. J Am Med Assoc. 1997;278:823–32. doi:10.1001/jama.1997.03550100049038.

    Article  CAS  Google Scholar 

  16. Spirito A, Barnett N, Lewander W, Colby S, Rohsenow D, Eaton C, et al. Risks associated with alcoho-positive status among adolescents in the emergency department: A matched case–control study. J Pediatr. 2001;139/5:396–402.

    Google Scholar 

  17. Hemphill SA, Kotevski A, Herrenkohl TI, Bond L, Kim MJ, Toumbourou JW, et al. Longitudinal consequences of adolescent bullying perpetration and victimisation: a study of students in Victoria, Australia. Crim Behav Ment Health. 2011;21:107–16. doi:10.1002/cbm.802.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Werner EE, Johnson JL. The Role of Caring Adults in the Lives of Children of Alcoholics. Subst Use Misuse. 2004;39:699–720. doi:10.1081/JA-120034012.

    Article  PubMed  Google Scholar 

  19. Yoon G, Westermeyer J, Kuskowski MA, Nesheim L. Impact of the Number of Parents With Alcohol Use Disorder on Alcohol Use Disorder in Offspring. J Clin Psychiatry. 2013;74:795–801. doi:10.4088/JCP.13m08350.

    Article  PubMed  Google Scholar 

  20. Langlois KA, Garner R. Trajectories of psychological distress among Canadian adults who experienced parental addiction in childhood. Health Rep. 2013;24:14–21.

    PubMed  Google Scholar 

  21. Begle A, Hanson R, Danielson C, McCart M, Ruggiero K, Amstadter A, et al. Longitudinal pathways of victimization, substance use, and delinquency: findings from the National Survey of Adolescents. Addict Behav. 2011;36:682–9. doi:10.1016/j.addbeh.2010.12.026.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lansford JE, Miller-Johnson S, Berlin LJ, Dodge KA, Bates JE, Pettit GS. Early Physical Abuse and Later Violent Delinquency: A Prospective Longitudinal Study. Child Maltreat. 2007;12:233–45. doi:10.1177/1077559507301841.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Stuewig J. The Relation of Child Maltreatment to Shame and Guilt Among Adolescents: Psychological Routes to Depression and Delinquency. Child Maltreat. 2005;10:324–36. doi:10.1177/1077559505279308.

    Article  PubMed  Google Scholar 

  24. Goodwin RD, Fergusson DM, Horwood JL. Childhood abuse and familial violence and the risk of panic attacks and panic disorder in young adulthood. Psychol Med. 2005;35:881–90. doi:10.1017/S0033291704003265.

    Article  PubMed  Google Scholar 

  25. Bader K, Hänny C, Schäfer V, Neuckel A, Kuhl C. Childhood Trauma Questionnaire – Psychometrische Eigenschaften einer deutschsprachigen Version. Z Klin Psychol Psychother. 2009;38:223–30. doi:10.1026/1616-3443.38.4.223.

    Article  Google Scholar 

  26. Bond L, Thomas L, Toumbourou J, Patton G, Catalano R. Improving the Lives of Young Victorians in Our Community: a survey of risk and protective factors. Melbourne: Community Care Division, Dept. of Human Services; 2000.

  27. Arthur MW, Hawkins JD, Pollard JA, Catalano RF, Baglioni Jr AJ. Measuring Risk And Protective Factors For Substance Use, Delinquency, And Other Adolescent Problem Behaviors: The Communities That Care Youth Survey. Eval Rev. 2002;26:575–601. doi:10.1177/019384102237850.

    PubMed  Google Scholar 

  28. Ostaszewski K, Zimmerman MA. The effects of cumulative risks and promotive factors on urban adolescent alcohol and other drug use: a longitudinal study of resiliency. Am J Community Psychol. 2006;38:237–49. doi:10.1007/s10464-006-9076-x.

    Article  PubMed  Google Scholar 

  29. Groeger-Roth F, Schubert H, editors. “Das kommt aus Amerika, das geht hier nicht…“ - Erfahrungen mit CTC in Niedersachsen: Sicher leben in Stadt und Land. Godesberg: Forum Verlag; 2012.

    Google Scholar 

  30. Sawyer SM, Afifi RA, Bearinger LH, Blakemore S, Dick B, Ezeh AC, et al. Adolescence: a foundation for future health. Lancet. 2012;379:1630–40. doi:10.1016/S0140-6736(12)60072-5.

    Article  PubMed  Google Scholar 

  31. Viner RM, Ozer EM, Denny S, Marmot M, Resnick M, Fatusi A, et al. Adolescence and the social determinants of health. Lancet. 2012;379:1641–52. doi:10.1016/S0140-6736(12)60149-4.

    Article  PubMed  Google Scholar 

  32. Bengel J, Meinders-Lücking F, Rottmann N. Schutzfaktoren bei Kindern und Jugendlichen - Stand der Forschung zu psychosozialen Schutzfaktoren für Gesundheit. Köln: Bundeszentrale für Gesundheitliche Aufklärung; 2009.

    Google Scholar 

  33. Masten AS. Global perspectives on resilience in children and youth. Child Dev. 2014;85:6–20. doi:10.1111/cdev.12205.

    Article  PubMed  Google Scholar 

  34. Catalano RF, Kosterman R, Hawkins JD, Newcomb MD, Abbott RD. Modeling the Etiology of Adolescent Substance Use: A Test of the Social Development Model. J Drug Issues. 1996;26:429–55.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hawkins JD, Weis JG. The Social Development Model: An Integrated Approach to Delinquency Prevention. J Prim Prev. 1985;6(2):73–97.

    Article  CAS  PubMed  Google Scholar 

  36. Schubert H, Veil K, Spieckermann H, Abels S. Evaluation des Modellprojektes “Communities That Care” in Niedersachsen. Köln: Sozial - Raum - Management; 2013.

    Google Scholar 

  37. Petermann U, Petermann F. Risiko- und Schutzfaktoren in der kindlichen Entwicklung. Familienpolitik und soziale Sicherung: Festschrift für Heinz Lampert;mit 5 Tabellen. Springer: Berlin,Heidelberg; 2005:39–55.

  38. Scholes-Balog KE, Hemphill SA, Kremer P, Toumbourou JW. A longitudinal study of the reciprocal effects of alcohol use and interpersonal violence among Australian young people. J Youth Adolesc. 2013;42:1811–23. doi:10.1007/s10964-013-9910-z.

    Article  PubMed  Google Scholar 

  39. Hawkins JD, Catalano RF, Arthur MW, Egan E, Brown EC, Abbott RD, et al. Testing Communities That Care: The Rationale, Design and Behavioral Baseline Equivalence of the Community Youth Development Study. Prev Sci. 2008;9:178–90. doi:10.1007/s11121-008-0092-y.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Parra GR, DuBois DL, Sher KJ. Investigation of profiles of risk factors for adolescent psychopathology: a person-centered approach. J Clin Child Adolesc Psychol. 2006;35:386–402. doi:10.1207/s15374424jccp3503_4.

    Article  PubMed  Google Scholar 

  41. Kuttler H, Reis O, Pradel H, Gross C, Zimmermann US, Kraus L, et al. Entwicklung eines Kurzfragebogens zum Erkennen und Prognostizieren von Risiko- und Schutzfaktoren bei Alkoholintoxikationen im Jugendalter (RiScA). Forum für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie in Deutschland e.V. (BKJPP), Forum-Verlag Aachen 2013:15–34.

  42. Kuttler H. Teenager mit Alkoholintoxikation im Krankenhaus - Kooperation von Medizin und Sozialpädagogik als Schlüssel zur Suchtprävention. Psychosomatik und Konsiliarmedizin. 2008;2(1):50–2.

    Article  Google Scholar 

  43. Wolstein J, Wurdak M, Stürmer M. Evaluation des Alkoholpräventionsprojektes HaLT - Hart am Limit in Bayern. München: Bayerische Akademie für Suchtfragen; 2011.

  44. Glaser RR, Van Horn ML, Arthur MW, Hawkins JD, Catalano RF. Measurement properties of the Communities That Care Youth Survey across demographic groups. J Quant Criminol. 2005;21:73–102.

    Article  Google Scholar 

  45. Schubert H, Veil K. Erster Zwischenbericht der SPIN Evaluation, Literaturanalyse Communities That Care: Arbeitspapier 35. Cologne: Forschungsschwerpunkt Sozial - Raum - Management (SRM), Fachhochschule Köln; 10/2009.

  46. Groeger-Roth F. Lokale Risiko- und Schutzfaktoren für Jugendkriminalität: Die Rolle von sozialräumlichen Unterschieden für die kommunale Prävention, Sozialräumliches Arbeiten in der Straffälligenhilfe : dahin gehen, wo es weh tut. Freiburg im Breisgau: Lambertus; 2012. p. 73–89.

    Google Scholar 

  47. Bernstein DP, Stein JA, Newcomb MD, Walker E, Pogge D, Ahluvalia T, et al. Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse Negl. 2003;27:169–90.

    Article  PubMed  Google Scholar 

  48. Mugisha J, Muyinda H, Wandiembe P, Kinyanda E. Prevalence and factors associated with Posttraumatic Stress Disorder seven years after the conflict in three districts in northern Uganda (The Wayo-Nero Study). BMC Psychiatry. 2015;15:170. doi:10.1186/s12888-015-0551-5.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Grassi-Oliveira R, Cogo-Moreira H, Salum GA, Brietzke E, Viola TW, Manfro GG, et al. Childhood Trauma Questionnaire (CTQ) in Brazilian samples of different age groups: findings from confirmatory factor analysis. PLoS One. 2014;9:e87118. doi:10.1371/journal.pone.0087118.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Saddichha S, Werker GR, Schuetz C, Krausz MR. Stimulants and Cannabis Use Among a Marginalized Population in British Columbia, Canada: Role of Trauma and Incarceration. Int J Offender Ther Comp Criminol 2014. doi:10.1177/0306624X14541661.

  51. Larsson S, Aas M, Klungsøyr O, Agartz I, Mork E, Steen NE, et al. Patterns of childhood adverse events are associated with clinical characteristics of bipolar disorder. BMC Psychiatry. 2013;13:97. doi:10.1186/1471-244X-13-97.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Wingenfeld K, Spitzer C, Mensebach C, Grabe H, Hill A, Gast U, et al. Die deutsche Version des Childhood Trauma Questionnaire (CTQ): Erste Befunde zu den psychometrischen Kennwerten. Psychother Psychosom Med Psychol. 2010;60:442–50. doi:10.1055/s-0030-1247564.

    Article  PubMed  Google Scholar 

  53. Klinitzke G, Romppel M, Häuser W, Brähler E, Glaesmer H. Die deutsche Version des Childhood Trauma Questionnaire (CTQ) - psychometrische Eigenschaften in einer bevölkerungsrepräsentativen Stichprobe. Psychother Psychosom Med Psychol. 2012;62:47–51. doi:10.1055/s-0031-1295495.

    Article  PubMed  Google Scholar 

  54. Häuser W, Schmutzer G, Brähler E, Glaesmer H. Misshandlungen in Kindheit und Jugend: Ergebnisse einer Umfrage in einer repräsentativen Stichprobe der deutschen Bevölkerung. Deutsches Ärzteblatt. 2011;108:287–94.

    Google Scholar 

  55. Iffland B, Brähler E, Neuner F, Häuser W, Glaesmer H. Frequency of child maltreatment in a representative sample of the German population. BMC Public Health. 2013;13:980. doi:10.1186/1471-2458-13-980.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bühner M. Einführung in die Test- und Fragebogenkonstruktion. 2nd ed. München, Don Mills: Pearson Studium; 2006.

    Google Scholar 

  57. Streiner DL, Norman GR. Health measurement scales: A practical guide to their development and use. 4th ed. Oxford, New York: Oxford University Press; 2008.

    Book  Google Scholar 

  58. Costello AB, Osborne JW. Best Practices in Exploratory Factor Analysis: Four Recommendations For Getting the most from Your Analysis. 2005. http://pareonline.net/getvn.asp?v=10&n=7. Accessed 15 Sep 2015.

  59. Jöreskog KG, Sörbom D. LISREL 8 user’s reference guide. 2nd ed. Chicago, IL: Scientific Software International; 1996.

    Google Scholar 

  60. Hair JF. Multivariate data analysis. 6th ed. Upper Saddle River, N.J: Pearson Prentice Hall; 2006.

    Google Scholar 

  61. Kline RB. Principles and practice of structural equation modeling. 2nd ed. New York NY u.a: Guilford Press; 2005.

    Google Scholar 

  62. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7:147–77.

    Article  PubMed  Google Scholar 

  63. Backhaus K, Erichson B, Weiber R. Fortgeschrittene multivariate Analysemethoden: Eine anwendungsorientierte Einführung. Berlin: Springer; 2011.

    Google Scholar 

  64. Forero CG, Maydeu-Olivares A, Gallardo-Pujol D. Factor Analysis with Ordinal Indicators: A Monte Carlo Study Comparing DWLS and ULS Estimation. Structural Equitation Modeling 2009:625–41

  65. Jöreskog KG. On the estimation of polychoric correlations and their asymptotic covariance matrix. Psychometrika. 1994;59:381–9. doi:10.1007/BF02296131.

    Article  Google Scholar 

  66. Rosseel Y. lavaan: an R Package for structural equation modeling. J Stat Softw. 2012;28:1–36.

    Google Scholar 

  67. Scholes-Balog KE, Hemphill SA, Dowling NA, Toumbourou JW. A prospective study of adolescent risk and protective factors for problem gambling among young adults. J Adolesc. 2014;37:215–24. doi:10.1016/j.adolescence.2013.12.006.

    Article  PubMed  Google Scholar 

  68. Boden JM, Fergusson DM, Horwood LJ. Risk factors for conduct disorder and oppositional/defiant disorder: evidence from a New Zealand birth cohort. J Am Acad Child Adolesc Psychiatry. 2010;49:1125–33. doi:10.1016/j.jaac.2010.08.005.

    PubMed  Google Scholar 

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Acknowledgements

The authors thank their colleagues from the RiScA Group: Dr. Ulrich S. Zimmermann and Cornelius Groß, Dresden; Prof. Dr. Olaf Reis and Stefanie Bumke, Rostock; Prof. Dr. Ludwig Kraus and Dr. Daniela Piontek, Munich.

The authors thank Felix Groeger-Roth from Landespraeventionsrat Niedersachsen (Federal Prevention Council of Lower Saxony) and Prof. Dr. Renate Soellner, Hildesheim University for providing the German version of the Communities That Care questionnaire and survey data.

We thank the Federal Centre for Health Education (Bundeszentrale fuer gesundheitliche Aufklaerung, Köln), for supplying us with USB flash drives, which we used as incentives for the participating adolescents.

We thank Dr. Heinz-Werner Priess, AGENON, Berlin, for his unlimited, invaluable and very instructive statistical advice.

Funding source

The study was funded by the German Ministry of Health (Grant ID: IIA5-2511DSM220). The Ministry played no role in design, in the collection, analysis, and interpretation of data, in the writing of the manuscript; or in the decision to submit the manuscript for publication.

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  1. Public Health & Health Education, Freiburg University of Education, Kunzenweg 21, 79117, Freiburg, Germany

    Heidi Kuttler, Hanna Schwendemann & Eva Maria Bitzer

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  1. Heidi Kuttler
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  3. Eva Maria Bitzer
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Correspondence to Heidi Kuttler.

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The authors declare that they have no competing interests.

Authors’ contributions

HK conceived the study, participated in its design, coordination and data acquisition and significantly contributed to the interpretation of the data and to the drafting of the manuscript. HS planned and performed the statistical analysis, participated in drafting the manuscript, and significantly contributed to the interpretation of the results. EMB provided substantial input to the study design, statistical analysis, and interpretation of the data. She revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

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Kuttler, H., Schwendemann, H. & Bitzer, E.M. Familial risk and protective factors in alcohol intoxicated adolescents: psychometric evaluation of the family domain of the Communities That Care Youth Survey (CTC) and a new short version of the Childhood Trauma Questionnaire (CTQ). BMC Pediatr 15, 191 (2015). https://doi.org/10.1186/s12887-015-0471-z

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BMC Pediatrics

ISSN: 1471-2431

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