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The Brief Symptom Inventory-9 (BSI-9): Development and validation in a German general population sample

BMC Psychology volume 12, Article number: 413 (2024) Cite this article

Abstract

Background

The Brief Symptom Inventory-18 (BSI-18) is a self-report questionnaire with three subscales, somatisation, anxiety, and depression, based on longer measures of distress. The present study proposes a shorter, nine-item version (BSI-9) of the BSI-18 as a brief screening tool for distress.

Methods

Confirmatory factor analyses and reliability and validity analyses were carried out using a representative sample of the German general population. Confirmatory factor analysis demonstrates a good model fit for the three-dimensional BSI-9.

Results

The total scale was found to have strong internal consistency (αCronbach = 0.87 for the global severity index). The internal consistency coefficients of the three-item subscales reflect the brevity of these scales (somatisation αCronbach = 0.72, depression α Cronbach = 0.79, anxiety αCronbach = 0.68). The subscales were found to be significantly related with subscales of the Patient Health Questionnaire-4 and Hopkins Symptom Checklist-25.

Limitations

The present study used a limited number of distress measures, and a more recent dataset would be useful to provide a more current picture of the general population’s distress levels.

Conclusions

The BSI-9 provides a short, valid, and reliable screener for distress in the general population. Future work should examine its utility in clinical settings and different cultural contexts.

Peer Review reports

The Brief Symptom Inventory – 18 items (BSI-18; [1]) is a self-report questionnaire used to assess distress in both clinical and general populations [2,3,4,5]. It is the shortest version of a lineage of distress measures that began with the Symptom Checklist-90-Revised (SCL-90-R; [6]), a 90-item self-report measure of distress. The SCL-90-R assesses nine-symptom dimensions, (i.e. somatisation, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism, and a global score) and has been validated for use in both clinical and community samples [7]. The SCL-90-R was shortened into the Brief Symptom Inventory (BSI; [6]), which includes 53 items, maintaining the nine symptom dimensions of the SCL-90-R. The BSI-18 was developed as an even briefer measure of distress and is commonly modeled using a three-factor structure, i.e., anxiety, depression, somatisation, a one-factor structure, i.e., the global severity index [8, 9], or a four-factor structure in which the anxiety facet is divided into panic and agitation [1, 10]. Reducing the number of factors from nine to three improved the structural validity of the scale, as the three remaining factors were more homogenously related to distress [10], required less time for administration [11], allowed for a reduced clinical burden on both clinicians and patients [11, 12], and provided the most current assessment of distress symptoms [13].

The BSI-18 has been validated for use in several clinical populations, including patients diagnosed with traumatic brain injury, Parkinson’s disease patients, organ transplantations, and cancer patients [3, 9, 14, 15], as well as the general population [2, 16]. The BSI-18 has been translated into multiple languages and validated in numerous cultural and linguistic settings [9, 16, 17].

Researchers continue to examine and refine the measurement of psychological distress, including shortening measures to improve utility and efficiency in clinical practice [12]. Reducing the number of items in a measure, while retaining strong psychometric properties, facilitates more efficient and effective research, decreases the burden on patients as well as clinicians by reducing the time required to complete, score, and interpret these measures.

Study objective and hypotheses

The aims of the current study are: (1) to develop a brief, nine-item German version of the BSIand (2) to assess the psychometric properties of the new BSI-9. We predicted that the BSI-9 would show positive correlations with both the Patient Health Questionnaire-4 and the Hopkins Symptom Checklist-25, other screening measures of distress. Specifically, we expected large correlations between corresponding subscales (i.e. between anxiety subscales and between depression subscales) of the BSI-9 and of the other measures of distress, compared to smaller correlations between non-corresponding subscales (e.g., between the somatisation subscale and subscales capturing anxiety or depression).

Method

Participants and Procedures

Data from a representative sample of the German population (N = 2520) were collected in 2009 by a demography consulting company from Germany (USUMA, Berlin). Representativeness was ensured by age, gender, and education distribution, according to the Federal Statistical Office (Franke et al., 2017). Participants were selected using a random-route procedure, which randomly selects households and household members. The current study reports on participants who provided complete responses for the target measure, the BSI-18, resulting in a sample size of N = 2482.

Measures

The Brief Symptom Inventory 18 (BSI-18)

The BSI-18 is an 18-item self-report measure of distress [18]comprised of three scales, somatization, anxiety, and depression. Participants are asked to rate how often each item distressed them in the past seven days, with Likert-type responses ranging from 1 to 5, where 1 = not at all; 2 = a little bit; 3 = moderately; 4 = quite a bit; 5 = extremely. The German version used in this study has been validated for use in the German general population [16]. Factorial studies of the German BSI-18 support the use of three subscales as well as the use of a global index, i.e., the sum score across all items [16]. Internal consistency reliability (Cronbach’s alpha) was 0.84 for anxiety, 0.87 for depression, 0.82 for the somatization subscales and 0.93 for the global severity index [16]. In terms of convergent validity of the German version, corresponding anxiety and depression scales of the BSI-18 and Patient Health Questionnaire-4 [19], i.e., anxiety and depression subscales, were more strongly correlated than non-corresponding subscales, i.e., the somatisation subscale of the BSI-18 [16]. Other language versions of the BSI-18 have been shown to have good convergent validity, sensitivity, and specificity [20,21,22].

The Patient Health Questionnaire-4 (PHQ-4)

The PHQ-4 is a 4-item self-report questionnaire that consists of a two-item anxiety scale (GAD-2) and a two-item depression scale (PHQ-2) with a Likert-type response scale assessing symptom severity. It has been validated for use in the general population [19, 23]. Participants are asked to rate their symptoms during the last two weeks as being present 0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day. The internal consistency was found to be 0.78 for the depression subscale and 0.85 for the anxiety subscale, while the internal consistency for the total scale was 0.82 [23].

The Hopkins Symptom Checklist-25 (HSCL-25)

The HSCL-25 is a 25-item self-report measure that consists of two subscales measuring anxious and depressive symptoms. The response options range from 1 to 4, with 1 = not at all, 2 = a little, 3 = quite a bit, and 4 = extremely. Mean sum scores can be calculated for the anxiety (10 items) and depression (15 items) subscales. A global score can be derived by computing the mean across all 25 items. The German version of the HSCL-25 was validated by [24] in the German general population. The internal consistency was 0.84 for the anxiety subscale, 0.92 for the depression subscale, and 0.94 for the total score [24].

Statistical analyses

The total sample (N = 2482) was split randomly into two datasets: A (n = 1255) and B (n = 1227). Descriptive statistics (%, mean, standard deviation, range) of demographic characteristics (Table 1) as well as means and standard deviations for all measures (Table 2) were computed for both subsamples using SPSS for Mac OSX v24. Dataset A was used to examine the factor structure of the German BSI-18, previously reported in [16] and to select the items for the briefer German BSI-9. Dataset B was used to investigate the factorial structure and other psychometric properties of the German BSI-9.

Table 1 Sample demographic information separated by statistical analysis group
Full size table
Table 2 Pearson correlation coefficients (subsample B)
Full size table

Dataset A: Confirmatory Factor Analysis (CFA) with BSI-18

Based on previous reports on the factor structure of the German BSI-18 (Franke et al., 2017), two models were fitted using CFA in MPlus v.8 [25]: a one-factor model, including all 18 items, and a three-factor model corresponding to the three subscales of the BSI-18: anxiety, depression, somatization. Item responses were ordinal Likert data and were therefore modeled using the weighted least squares (WLSMV) estimator. In line with recommended guidelines for assessing model fit [26], in addition to the chi-square test, which is highly sensitive to sample size and can lead to inaccurate rejection of the model fit [27], we used a combination of the following model fit indices: the Comparative Fit Index (CFI; [28]), the Tucker-Lewis Index (TLI; [29]), the Root Mean Square Error of Approximation (RMSEA; [30]), and the Standardized Root Mean Square Residual (SRMR; [31]). The model fit was considered acceptable if \(\:\chi\:\)2p > 0.05, TLI \(\:\ge\:\)0.95; CFI \(\:\ge\:\) 0.90, RMSEA <0.08; and SRMR <0.08, AVE<0.5 [32].

Dataset A: item selection for the BSI-9

The nine items for the BSI-9 were selected based on examination of the factor loadings for the BSI-18 items, examination of modification indices, and theoretical support for symptoms belonging to each subscale.

Dataset B: CFA with BSI-9

Once relevant items were selected for the BSI-9, the same two CFA models tested with the BSI-18 (but fewer items for the three-factor model) were also tested with the BSI-9 using the same modelling methods.

Dataset B: reliability and validity of the BSI-9

Cronbach’s alpha was computed for the BSI-9 total and subscales. Convergent and divergent validity of the BSI-9 were assessed through Pearson correlations with the corresponding subscales of the PHQ-4 and HSCL-25. Cohen’s effect sizes were used to assess the magnitude of the bivariate correlations, with r ≤ .10 indicating small, r = .30 indicating moderate, and r = .50 indicating large correlations [33].

Results

Sample demographic information

The demographic characteristics for both datasets are presented in Table 1.

Development of the BSI-9

CFA with the BSI-18

A one-factor CFA with the BSI-18 showed an acceptable fit, \(\:\chi\:\)2(df = 135, n = 1,255) = 1133.39, p < .001; RMSEA = 0.08, 95% Confidence Interval (CI) [0.07-0.08]; TLI = 0.94; CFI = 0.95; SRMR = 0.06. A three-factor model also showed an acceptable fit \(\:\chi\:\)2(df = 132, n = 1,255) = 576.26, p < .001; RMSEA = 0.05, 95% CI [0.05-0.06]; TLI = 0.97; CFI = 0.98; SRMR = 0.04).

Selection of the items for the short version of BSI, the BSI-9

First, the item pool was narrowed to 12 items, four items per subscale, based on the items with the highest factor loadings (Table 3). Next, theoretical background (i.e., construct definitions) and modification indices were considered to shorten the scale to nine items, three items per subscale. Specifically, modification indices were used to narrow an initial set of items that correlated most strongly with theory. Items with larger modification indices were retained for the final scale. The minimum number of items (i.e., three) was selected for each subscale was chosen to prioritize the brevity of the scale [34].

Table 3 Descriptive statistics and factor loadings for the BSI-9
Full size table

CFA with the BSI-9

A one-factor CFA in subsample B showed an acceptable model fit (\(\:\chi\:\)2(df = 28, n = 1227) = 512.41, p < .001; RMSEA = 0.12, 95% CI [0.11-0.13]; TLI = 0.92; CFI = 0.94; SRMR = 0.06). A three-factor CFA showed an adequate fit (\(\:{\chi\:}\)2(24) = 80.78, p < .001; RMSEA = 0.04 [0.03-0.06]; TLI = 0.99; CFI = 0.99; SRMR = 0.02). Factor loadings and 95% Confidence Intervals for the BSI-9 items can be found in Table 3.

Reliability and convergent validity of the BSI-9

McDonald’s omega was 0.73 for the somatisation subscale, 0.80 for the depression subscale, 0.69 for the anxiety subscale, and 0.84 for the global severity index. The correlations between the subscales of the BSI-9 and, the PHQ-4 and HSCL-25 were moderate to large in magnitude and significant at the 0.01 level (Table 2).

Discussion

To date, the BSI-9 is the shortest version of the Brief Symptom Inventory, a widely used measure of distress in both clinical and general populations. In the current analyses, the one and three factor structures of the BSI-9 were found to be of comparable model fit to that of the BSI-18, with the CFI, TLI, SRMR, and RMSEA falling within acceptable values when the items were modeled categorically rather than being treated as if they were continuous variables (Table 4). The chi-square value was significant. However, large sample size has been reported to skew this statistic [27] and the current study sample was of exceptionally magnitude with > 1000 participants per subsample. In sum, the statistical indices reflect a good model fit for the three-factor structure of the BSI-9 and an acceptable fit of the one-factor model of the BSI-9. Clinical judgment can be used to determine which model (one factor versus three factors) would be most useful for the purpose of the assessment.

Table 4 Confirmatory factor analysis models of the BSI-18 and BSI-9
Full size table

Of note, the model fit for the BSI-18 reported in the present study is different from the fit reported for the development of the BSI-18 by [16], which used the same data set. The difference can be attributed to how the BSI data were modeled: current analyses fit the items as categorical data while Franke used continuous data modelling.

In both the one- and three-factor models, each item loaded strongly onto the global score and its corresponding subscale (Table 3) and had good reliability. Although Cronbach’s alpha was lower, albeitacceptable, for the subscales, this may be considered a strength rather than a limitation. Higher alpha levels for a very short subscale, such as in the BSI-9, could in fact be considered a limitation, as it may indicate too much similarity between items and therefore only measure one facet of a larger construct. We aimed to capture different indicators of each of the subscale to better encompass the construct, which led to the slightly lower alpha values. In terms of convergent validity, the correlations of anxiety, depression, and somatisation subscales and total BSI scores with anxiety, depression, and total scales of both the PHQ-4 and HSCL-25 (Table 2) were moderate to large, with somatisation showing smaller correlations with the other measures. This is not surprising, given that neither validation measure (i.e., the PHQ-4 and HSCL-25) contains a somatisation subscale, which would be expected to correlate strongly with the BSI-9 somatisation subscale. Correlations with specific subscales of the PHQ-4 and HSCL-25 were slightly smaller than expected, which may be attributed to slight measurement differences captured by the wording of scale items (i.e., different facets of the constructs are measured on different scales).

Limitations

Although the size and representative nature of the sample used in the current study reflect a methodological strength, several limitations should be considered. First, the sample used to develop the BSI-9 was the same sample used by [16] to validate the German version of the BSI-18; ideally this would have been a separate sample. Secondly, a lack of longitudinal data prevents the evaluation of predictive validity. An additional, more recent dataset could be useful to compare the changes in distress in the German population over time and provide a more current picture of the general population’s distress levels. Third, the present study used a limited number of distress measures for convergent and divergent validity. Using additional measures, especially a scale capturing somatization symptoms would provide further insight into the validity of the BSI-9. Relatedly, the smaller correlation between subscales of measures used for convergent validity (i.e., the HSCL-25 and PHQ-4) may also capture a more clear distinction between anxiety and depression subscales compared to that measured by the BSI-9.

Implications & future directions

The BSI-9 is a short distress screening tool for research applications and that clinicians may use with their patients. Clinicians can use their clinical judgement to determine if they would like to use the three factor or one factor structure, depending on the information they are seeking. Future investigations of the BSI-9 should examine its utility in clinical populations, as well as in other linguistic and cultural settings. Further, since psychological distress may be influenced by a variety of factors, such as age, education level, relationship satisfaction, worry about loved ones, and environmental stress and has implications for mental health, quality of life, and general well-being [35, 36], future studies should examine how differences in these demographic characteristics are related to BSI-9 scores.

Given the broad nature of distress, the BSI-9 provides a fast screening tool for clinicians to screen their patients’ distress levels. It is a valid and reliable measure that has the potential to be used in both research and clinical settings.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

References

  1. Derogatis LR. BSI-18: administration, scoring and procedures manual. Minneapolis, MN: National Computer Systems; 2000.

    Google Scholar 

  2. Houghton F, Keane N, Murphy N, Houghton S, Dunne C, Lewis C, Breslin M. The Brief Symptom Inventory-18 (BSI-18): norms for an Irish third-level college sample. Ir J Psychol. 2012;33:43–62.

    Article  Google Scholar 

  3. Meachen SJ, Hanks RA, Millis SR, Rapport LJ. The reliability and validity of the Brief Symptom Inventory–18 in persons with traumatic brain injury. Arch Phys Med Rehabil. 2008;89(5):958–965. https://doi.org/10.1016/j.apmr.2007.12.028

  4. Torres L, Miller MJ, Moore KM. Factorial invariance of the Brief Symptom Inventory-18 (BSI-18) for adults of Mexican descent across nativity status, language format, and gender. Psychol Assess. 2013;25(1):300–5.

    Article  PubMed  Google Scholar 

  5. Wang J, Kelly BC, Booth BM, Falck RS, Leukefeld C, Carlson RG. Examining factorial structure and measurement invariance of the Brief Symptom Inventory (BSI)-18 among drug users. Addict Behav. 2010;35(1):23–9.

    Article  PubMed  Google Scholar 

  6. Derogatis LR. SCL-90-R: Administration, scoring & procedures manual-II, for the R (Revised) version and other instruments of the psychopathology rating scale series. 2nd Edition, Clinical Psychometric Research, Inc., Towson; 1992.

  7. Derogatis LR, Unger R. Symptom checklist-90‐revised. The Corsini encyclopedia of psychology. eds I.B. Weiner and W.E. Craighead; 2010.

  8. Durá E, Andreu Y, Galdon MJ, et al. Psychological assessment of patients with temporomandibular disorders: confirmatory analysis of the dimensional structure of the brief symptoms Inventory 18. J Psychosom Res. 2006;60:365–70.

    Article  PubMed  Google Scholar 

  9. Galdón MJ, Durá E, Andreu Y, Ferrando M, Murgui S, Pérez S, Ibañez E. Psychometric properties of the Brief Symptom Inventory-18 in a Spanish breast cancer sample. J Psychosom Res. 2008;65:533–9.

    Article  PubMed  Google Scholar 

  10. Recklitis CJ, Parsons SK, Shih MC, Mertens A, Robison LL, Zeltzer L. Factor structure of the Brief Symptom Inventory–18 in adult survivors of childhood cancer: results from the Childhood Cancer Survivor Study. Psychol Assess. 2006;18:122–32.

    Article  Google Scholar 

  11. Derogatis LR. BSI, 18, Brief Symptom Inventory 18: administration, scoring and procedures manual. Minneapolis, MN: NCS Pearson; 2001.

    Google Scholar 

  12. Loutsiou-Ladd A, Panayiotou G, Kokkinos CM. A review of the factorial structure of the Brief Symptom Inventory (BSI): Greek evidence. Int J Test. 2008;8(1):90–110.

    Article  Google Scholar 

  13. Berwick DM, Murphy JM, Goldman PA, Ware JE, Barsky AJ, Weinstein MC. Performance of a five-item mental health screening test. Med Care. 1991;29(2):169–76.

    Article  PubMed  Google Scholar 

  14. Carrozzino D, Siri C, Bech P. The prevalence of psychological distress in Parkinson’s disease patients: the Brief Symptom Inventory (BSI-18) versus the Hopkins symptom checklist (SCL-90-R). Prog Neuropsychopharmacol Biol Psychiatry. 2019;88:96–101.

    Article  PubMed  Google Scholar 

  15. Nijhoff MF, Hovens J, Huisman SD, Ringers J, Rabelink T, de Fijter H, van der Boog P, de Koning E. Psychological symptoms and quality of life after simultaneous kidney and pancreas transplantation. Transplantation Direct. 2020;6(5):e552.

  16. Franke GH, Jaeger S, Glaesmer H, Barkmann C, Petrowski K, Braehler E. Psychometric analysis of the Brief Symptom Inventory 18 (BSI-18) in a representative German sample. BMC Med Res Methodol. 2017;17(1).

  17. Mohammadkhani P, Dobson KS, Amiri M, Ghafari FH. Psychometric properties of the Brief Symptom Inventory in a sample of recovered Iranian depressed patients. Int J Clin Health Psychol. 2010;10(3):541–51.

    Google Scholar 

  18. Derogatis LR, Melisaratos N. The Brief Symptom Inventory: an introductory report. Psychol Med. 1983;13(3):595–605.

    Article  PubMed  Google Scholar 

  19. Kroenke K, Spitzer RL, Williams JBW, Löwe B. An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics. 2009;50:613–21.

    PubMed  Google Scholar 

  20. Petkus AJ, Gum AM, Small B, Malcarne VL, Stein MB, Wetherell JL. Evaluation of the factor structure and psychometric properties of the Brief Symptom Inventory-18 with homebound older adults. Int J Geriatr Psychiatry. 2010;25(6):578–87.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Spitzer C, Hammer S, Löwe B, Grabe HJ, Barnow S, Rose M, Wingenfeld K, Freyberger HJ, Franke GH. Die kurzform des Brief Symptom Inventory (BSI – 18): erste Befunde zu den psychometrischen Kennwerten Der Deutschen Version [The short version of the Brief Symptom Inventory (BSI – 18): preliminary psychometric properties of the German translation]. Fortschr Neurol Psychiatr. 2011;79(9):517–23. German.

    Article  PubMed  Google Scholar 

  22. Zabora J, BrintzenhofeSzoc K, Jacobsen P, Curbow B, Piantadosi S, Hooker C, Owens A, Derogatis L. A new psychosocial screening instrument for use with cancer patients. Psychosomatics. 2001;42(3):241–6.

  23. Löwe B, Wahl I, Rose M, Spitzer C, Glaesmer H, Wingenfeld K, Schneider A, Brähler E. A 4-item measure of depression and anxiety: validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J Affect Disord. 2010;122(1–2):86–95.

    Article  PubMed  Google Scholar 

  24. Glaesmer H, Braehler E, Grande G, Hinz A, Petermann F, Romppel M. The German version of the Hopkins Symptoms Checklist-25 (HSCL-25)—Factorial structure, psychometric properties, and population-based norms. Compr Psychiatr. 2014;55:2396–403.

    Article  Google Scholar 

  25. Muthén LK, Muthén BO. Mplus user’s guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén. 1998–2017.

  26. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Modeling: Multidisciplinary J. 1999;6(1).

  27. Reise SP, Widman KF, Pugh RH. Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance. Psychol Bull. 1993;114(3):552–66.

    Article  PubMed  Google Scholar 

  28. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107(2):238–46.

    Article  PubMed  Google Scholar 

  29. Tucker LR, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika. 1973;38:11–10.

    Article  Google Scholar 

  30. Steiger JH. Structural model evaluation and modification: an interval estimation approach. Multivar Behav Res. 1990;25:2173–180.

    Article  Google Scholar 

  31. Hu L, Bentler PM. Cutoff criteria for fit indexes in Covariance structure analysis: conventional criteria Versus New Alternatives. Struct Equ Model. 1999;6:1–55.

    Article  Google Scholar 

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

    Google Scholar 

  33. Cohen J. Statistical power analysis for the behavioural sciences. 2nd ed. Hillsdale: Lawrence Erlbaum Associates; 1988.

    Google Scholar 

  34. Marsh HW, Hau KT, Balla JR, Grayson D. Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivar Behav Res. 1998;33(2):181–220.

    Article  Google Scholar 

  35. Miller DL, Manne SL, Taylor K, Keates J, Dougherty J. Psychological distress and well-being in advanced cancer: the effects of optimism and coping. J Clin Psychol Med Settings. 1996;3(2):115–30.

    Article  PubMed  Google Scholar 

  36. Payton AR. Mental health, mental illness, and psychological distress: same continuum or distinct phenomena? J Health Social Behav. 2009;50:2213–227.

    Article  Google Scholar 

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Author notes

  1. E. Braehler and A. Körner contributed equally to this work.

Authors and Affiliations

  1. McGill University, Montreal, QC, Canada

    C. MacDonald, K. Brophy & A. Körner

  2. Nicotine Dependence Service, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada

    A. Coroiu

  3. Department of Medical Psychology and Medical Sociology, Medical Center of the University of Leipzig & Department of Psychosomatic Medicine and Psychotherapy, Medical Center of the University of Mainz, Leipzig, Germany

    Elmar Braehler

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Contributions

C.M. wrote the main manuscript text. K.B. prepared Tables 1, 2, 3 and 4. A.C. provided support on statistical analyses. A.K., K.B., and A.C. contributed supervision and feedback on the final manuscript. E.B. collected the data for this manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Elmar Braehler.

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All methods in this study were carried out in accordance with relevant guidelines and regulations. Participation was voluntary, informed consent was obtained from participants or their legal guardians, as applicable. Participants also received a data protection declaration which was in agreement with the Helsinki Declaration. The present study was approved according to the ethical guidelines of the “German Professional Institutions for Social Research” (Arbeitskreis Deutscher Markt- und Sozialforschungsinstitute, Arbeitsgemeinschaft Sozialwissenschaftlicher Institute, Berufsverband Deutscher Markt- und Sozialforscher) and has the approval number: 072-11-07032011 from the ethics board of the University of Leipzig, Germany.

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MacDonald, C., Brophy, K., Coroiu, A. et al. The Brief Symptom Inventory-9 (BSI-9): Development and validation in a German general population sample. BMC Psychol 12, 413 (2024). https://doi.org/10.1186/s40359-024-01890-8

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