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Abstract

This study evaluated the psychometric properties of the modified brief Personality Inventory for DSM-5 (PID5BF + M) in primary care (PC) using data from n = 1,030 German patients. Furthermore, differences in maladaptive personality traits between PC patients and the general population were explored. Confirmatory factor analysis supported factorial validity (CFI = 0.949, TLI = 0.942, RMSEA = 0.044, and SRMR = 0.058). Reliability was adequate across domain scales (ω_H: 0.75–0.85). PID5BF + M total and domain scale scores, particularly negative affectivity, correlated significantly positively with depression (PHQ-9), anxiety (GAD-7), and somatic symptoms (PHQ-15), indicating convergent validity. Regression analyses showed PC to be associated with lower levels of maladaptive personality traits, compared with a representative German population sample (n = 4,172). These findings highlight the PID5BF + M as a valid and reliable tool for assessing personality pathology and maladaptive traits in PC, enabling general practitioners to screen for transdiagnostic indicators of mental health conditions.

Keywords

PID-5 primary care general practitioner personality disorders psychopathology transdiagnostic approach validation

Introduction

Mental Health Conditions in Primary Care: Limitations of Categorical Diagnostic Taxonomies

The two leading diagnostic taxonomies currently in use, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) section II (American Psychiatric Association, 2013) and the International Classification of Diseases, 10th Revision (ICD-10; World Health Organisation, 1993), classify mental disorders as distinct entities based on the binary occurrence (i.e., present/not present) of a certain set of symptoms. This approach has several shortcomings: First, it has been suggested that established diagnostic thresholds are largely arbitrary in nature, following that strict cut-offs defining when a specific diagnosis is given may be questionable (Clark et al., 2017). Second, great inter-individual heterogeneity exists within diagnoses (Newson et al., 2021). For instance, with only five out of nine possible symptoms being required for a borderline personality disorder diagnosis, two affected individuals could match on only one symptom (Woods et al., 2020). The lack of empirical equivalence between single symptoms within an associated mental disorder is also of concern (Boschloo et al., 2015). Third, assigning a clear diagnosis to an individual can be difficult due to the frequent overlap of symptoms between diagnoses, indicating low validity of diagnostic constructs (Rodriguez-Seijas et al., 2015). For example, core symptoms of major depressive disorder are shared with 35 other diagnoses (Forbes et al., 2023). This heterogeneity is further reflected by individuals’ tendency to shift between mental disorders across their lifespan (Caspi et al., 2020; Menzies et al., 2024), implying an overly rigid categorization of symptoms into specific disorder clusters (Dalgleish et al., 2020).

Particularly in primary care (PC), the diagnosis of mental illness according to standard classification systems may be inadequate (Hanel et al., 2009; World Health Organisation, 2008). Usually being the first point of contact for individuals with symptoms, PC serves as the interface between the general population and more specialized healthcare settings (Green et al., 2001). Compared to the general population, PC patients can be distinguished by a higher disease burden (Mokraoui et al., 2016), with the early identification of mental health conditions as a key function of PC (Alexander et al., 2010; Gaebel et al., 2013; World Health Organisation, 2018). However, assessing a large number of specific mental disorder criteria has been deemed unfeasible for general practitioners (GPs; Brown & Wissow, 2012; Hanel et al., 2009). This approach seems not only impractical in terms of the time available per patient (Irving et al., 2017; Talen et al., 2013) but also of limited clinical utility. As a gateway to psychiatric care, PC is characterized by patients who frequently present with mild to moderate, nonspecific psychological distress. Symptoms often exist along a continuum and may overlap with multiple mental disorders (Gili et al., 2010; Linden, 2004). Given these distinct characteristics of PC, screening tools are needed that support rather than burden GPs in their preliminary assessment of psychologically distressed patients (Neulinger et al., 2024; Talen et al., 2013).

Personality Disorders and the Shift to a Dimensional Approach to Psychopathology

Instead of assessing psychopathology through distinct, disorder-specific categories, a continuous understanding has been proposed (Dalgleish et al., 2020; Haslam et al., 2020; Kotov et al., 2017; Widiger & Samuel, 2005). Given the aforementioned non-specificity of psychological symptoms in PC (Brown & Wissow, 2012; Hanel et al., 2009; Linden, 2004), this approach may be promising for the diagnostic practice of GPs. A prime example of a shift toward dimensional models is represented by personality disorders. In response to accumulating evidence on the dimensional nature of personality (Hopwood et al., 2018), an alternative model of personality disorders (AMPD) was implemented in section III of the DSM-5 (American Psychiatric Association, 2013), and a similar model was adopted in the ICD-11 (World Health Organisation, 2019/2021). In both models, personality pathology is defined in terms of a general level of personality dysfunction and specific personality domains. For the latter, the DSM-5 and ICD-11 each define a total of five maladaptive personality traits. While four traits are highly similar (i.e., negative affectivity, detachment, antagonism/dissociality, and disinhibition), both models differ regarding the fifth domain, which refers to psychoticism in the DSM-5 and anankastia in the ICD-11.

Transdiagnostic Mental Health Screening in PC: The Potential of the PID5BF + M

Two questionnaires that are widely used to measure the personality traits specified by each classification system are the Personality Inventory for DSM-5 (PID-5) (Krueger et al., 2012) and the Personality Inventory for ICD-11 (PiCD; Oltmanns & Widiger, 2018). Recently, a brief modified version of the PID-5 was developed. Originally comprising 34 items (PID5BF+) (Kerber et al., 2022), it has been further refined with 36 items (PID5BF + M) (Bach et al., 2020). Due to its extension by the domain of anankastia, the PID5BF + M allows the maladaptive personality traits of both classification systems to be assessed using trait-specific sum scores. Furthermore, the total score can be used to indicate the overall severity of personality pathology (Pires et al., 2023; Zimmermann et al., 2020).

The PID-5 in its brief form has been proposed as a valuable screener for PC (Porcerelli et al., 2019). In addition to assessing personality pathology –25% of PC patients show signs of a personality disorder [Tyrer et al., 2015]) – the PID5BF + M may be particularly useful in PC, as psychologically distressed patients without a personality disorder still present with elevated levels of maladaptive personality traits (Pires et al., 2021). Although defined separately, maladaptive personality traits and psychological symptoms show some functional relationship (Abdi & Pak, 2019; Hong & Tan, 2021; Perkins et al., 2019; Sauer-Zavala et al., 2022; Stanton et al., 2016). Accordingly, maladaptive personality traits were suggested as higher-order constructs, operating as transdiagnostic indicators for a wide range of lower-order symptoms of mental illness (Fowler et al., 2022; Kerber et al., 2022; Kotov et al., 2010; Porcerelli et al., 2019; Rek et al., 2021; Subica et al., 2016; Wendt et al., 2024; Wright et al., 2015). Negative affectivity (i.e., neuroticism) appears to play a particular role (Kotov et al., 2010; Rek et al., 2021; Stanton et al., 2016) as it is considered to be an important marker of “emotion-based disorders” (Barlow et al., 2021; Tyrer et al., 2016). Characterized by a heightened experience of (negative) emotions and subsequent avoidance-based coping mechanisms (Bullis et al., 2019), common mental disorders in PC, such as depression, anxiety, and somatic symptom disorders (Hanel et al., 2009; Roca et al., 2009), can be subsumed under this umbrella term. Routinely screening for maladaptive personality traits – especially increased neuroticism/negative affectivity – may enable GPs to provisionally identify patients who are at risk of or affected by mental illness (Böhnke et al., 2014; Lahey, 2009; Neulinger et al., 2024; Rek et al., 2021; Widiger & Oltmanns, 2017). The PID5BF + M could therefore be a more feasible, but no less informative, addition to the diagnostic process of GPs than disorder-specific screening (Böhnke et al., 2014; Porcerelli et al., 2019).

Research Gap: The Psychometric Properties and Clinical Utility of the PID5BF + M in PC

A recent systematic review on screening questionnaires in PC recommended evaluating personality questionnaires as tools for the transdiagnostic assessment of mental illness (Neulinger et al., 2024). While research on the psychometric properties of the PID5BF + M has been conducted in inpatient and community samples (Bach et al., 2020; Riegel et al., 2021; Zinchuk et al., 2023), its evaluation in PC remains limited. To date, only Porcerelli et al. (2019) have examined the discriminant and convergent validity of the PID5BF – another abbreviated version of the PID-5 – using a small sample of 100 patients. The authors explicitly called for replication studies and stressed the importance of evaluating the utility of personality screening in relation to commonly used depression- and anxiety-focused screeners. As a result, key gaps remain in understanding the psychometric properties and clinical utility of the PID5BF + M in PC. To address these gaps, this study aimed to (1) assess the factorial validity and reliability of the PID5BF + M in a German PC sample; (2) evaluate its potential as an early indicator of mental illness in PC by examining convergent validity with established measures of depressive, anxiety, and somatic symptom severity; and (3) analyze the association between the PC setting and maladaptive personality traits, using a representative sample of the German general population for comparison.

Methods

Contextual Background on the German Healthcare System

The German healthcare system operates on a dual model, comprising statutory health insurance and private health insurance. The statutory health insurance is based on the principle of solidarity and is primarily funded through wage-related contributions of about 14% of income, with employees and employers each paying half. This structure ensures universal health coverage for all residents, as health insurance is mandatory in Germany (Busse et al., 2017). Primary healthcare is predominantly delivered by GPs. Particularly for patients with mental health conditions, GPs are often the sole treatment providers (Häfner & Petzold, 2007). Notably, patients have the autonomy to choose and change their GP of choice at any time and to consult specialists directly without a GP referral (Blümel et al., 2020).

Procedure and Participants

Cross-sectional data collection took place between December 2022 and May 2023 in 10 PC practices in Munich, Germany. PC patients were informed about the study by members of the research team or medical assistants in the waiting room of their PC practice. If willing to participate, and after giving oral informed consent, they completed a self-report questionnaire assessing sociodemographic and health-related data.

Considering only PC patients with complete data (n = 1,030), 59.6% identified as women, the mean age was 46.5 years (SD = 16.61), and 90.6% stated to have a German citizenship. 66.8% participants were in a relationship or married (single: 20.7%; annulled: 7.8%; widowed: 4.7%). 46.5% had a high-school diploma (no school-leaving certificate: 0.9%; lower secondary school diploma: 24.1%; secondary school diploma: 28.4%). A vocational mastership, technical college, or university degree was held by 35.7% (no vocational training: 6.0%; vocational training: 58.3%). The employment rate was 70.1%. Among participants not working, 11.6% were housewives/housemen, 63.9% were retired, 17.7% were students, 6.8% were unemployed (see Appendix 1 for a comparison of sociodemographic and mental health symptom data between included and excluded PC patients).

The reference sample of the German general population was derived from the standard edition of the GESIS panel (Leibniz Institute for the Social Sciences, 2023). Funded by federal and state governments and administered by the Leibniz Institute for the Social Sciences, the GESIS panel holds data of a representative sample (n = approximately 5,200 panelists) of the adult German-speaking population (18–70 years of age). Initiated in 2013, today, a total of four cohorts are part of the GESIS panel who complete questionnaires four times per year (i.e., in 4 waves) on different topics, among others, also concerning maladaptive personality traits (Bosnjak et al., 2017; Leibniz Institute for the Social Sciences, 2023). Specifically, for this study, the wave “Ge” was selected (Rek et al., 2021). The identifier “Ge” refers to data collection conducted between October and November 2019, including participants of the first three cohorts who completed the PID5BF + (difference to the PID5BF + M is described below). Later GESIS data were not included to avoid the influence of the COVID-19 pandemic (Wendt et al., 2024). The extracted data comprised 4,172 participants (50.4% female) with a mean age of 58.9 years (SD = 14.5). 65.1% of participants were in a relationship or married (single: 19.8%; annulled: 9.4%; widowed: 5.8%). A high-school diploma was held by 49.1% (no school-leaving certificate: 0.9%; lower secondary school diploma: 16.9%; secondary school diploma: 33.2%), while 40.0% had a vocational mastership, technical college, or university degree (no vocational training: 8.4%; vocational training: 51.6%).

Measures

Modified Personality Inventory for DSM-5 Brief Form

The PID5BF + M (Bach et al., 2020) assesses the five DSM-5 personality traits of psychoticism, disinhibition, negative affectivity, detachment, and antagonism, and is supplemented by the ICD-11 personality domain of anankastia. With a total of 36 items, each domain (e.g., negative affectivity) is divided into three facets (e.g., emotional lability, anxiousness, and separation insecurity) and two items per facet rated on a 4-point Likert scale (0: “does not apply at all”–3: “applies exactly”). A total score and domain-specific scores can be formed, indicating the overall severity of personality pathology and the levels of trait-specific manifestations, respectively. Higher values suggest greater impairment.

In the GESIS panel, the PID5BF+ (Kerber et al., 2022) was employed. Compared to the PID5BF + M, it covers the five DSM-5 personality traits with the same six items each. However, the ICD-11 trait anankastia is measured with only four items, two of which are also included in the PID5BF + M, while the other two differ in content.

For copyright reasons, the exact wording of the PID5BF + /M items was not provided, but is available on the website of the American Psychiatric Association (2025).

Patient Health Questionnaire-9

Depressive symptoms were examined using the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001), a nine-item measure evaluating symptom severity over the past 2 weeks on a 4-point Likert scale (0: “not at all present”–3: “present nearly every day”). The total score can range from 0 (= no depression) to 27. An acceptable internal consistency was observed in the PC sample (ω = 0.88).

Generalized Anxiety Disorder Screener-7

General anxiety symptoms were measured using the Generalized Anxiety Disorder Screener-7 (GAD-7; Löwe et al., 2008). With seven items rated on a 4-point Likert scale (0: “not at all present”–3: “present nearly every day”), symptom severity over the past 2 weeks is assessed. The sum score can vary between 0 (=no anxiety) and 21. McDonald’s Omega (ω = 0.90) suggested an acceptable internal consistency in the PC sample.

Patient Health Questionnaire-15

Somatic symptom severity was evaluated using the Patient Health Questionnaire-15 (PHQ-15; Kroenke et al., 2002), with 15 items each assessing the experience of a specific somatic symptom in the past 4 weeks on a 3-point Likert scale (0: “not bothered at all”–2: “bothered a lot”). The total score can lie between 0 (=no somatic symptoms) and 30. An acceptable internal consistency was found in the PC sample (ω = 0.84).

Data Analysis

Statistical analysis was conducted with R (R Core Team, 2021), using the following packages: “readxl” (Wickham & Bryan, 2023), “lavaan” (Rosseel et al., 2022), “semTools” (Jorgensen et al., 2022), “psych” (Revelle, 2024), “dplyr” (Wickham et al., 2023), “correlation” (Makowski et al., 2022), “car“ (Fox & Weisberg, 2019), “officer” (Gohel et al., 2025), “flextable” (Gohel & Skintzos, 2024), and “polycor” (Fox, 2022).

For all PID5BF + M items and domain scales, descriptive statistics were calculated, including the mean, standard deviation (SD), skewness, and kurtosis. The reliability of the PHQ-9, GAD-7, and PHQ-15 was estimated using categorical McDonald’s Omega total (ω), whereas the reliability of the PID5BF + M domain scales was examined with McDonald’s Omega hierarchical (ω_H). For the former, ω > 0.65 and for the latter, ω_H > 0.80 were assumed to imply acceptable reliability (Kalkbrenner, 2023).

Factorial validity of the PID5BF + M in the PC sample was assessed using ordinal confirmatory factor analysis (CFA) based on robust weighted least squares means and variances (WLSMV) adjusted estimation, which has been shown to yield more accurate factor loadings for ordinal data than robust maximum likelihood (MLR; Li, 2016). Consistent with previous research (Kerber et al., 2022; Rek et al., 2021), a PID5BF + M higher-order model was specified with two related items (e.g., item 01 and item 19) loading on a corresponding first-order facet (e.g., emotional lability) and three related facets (e.g., emotional lability, anxiousness, and separation insecurity) in turn loading on a corresponding second-order domain (e.g., negative affectivity). As goodness of fit indices, the χ²-likelihood ratio statistic, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) were considered. A CFI and TLI value > 0.95, as well as an RMSEA value < 0.06 and a SRMR value < 0.08, were applied as approximate cut-offs indicating adequate model fit (Hu & Bentler, 1999). Convergent validity of PID5BF + M was tested by applying Pearson correlations between the factor scores of the domain scales and the factor scores of the PHQ-9, GAD-7, and PHQ-15.

To examine differences in maladaptive personality traits between PC patients and the general population, regression analyses were conducted. Each model included one of the five maladaptive personality domains (negative affectivity, detachment, antagonism, disinhibition, and psychoticism) as the dependent variable. The independent variables comprised sample setting (PC vs. general population) and sociodemographic factors, which were added to control for potential confounding. Sociodemographic factors included age, gender (female and male), marital status (single, relationship/married, annulled, and widowed), highest level of education (no school-leaving certificate, lower secondary school diploma, secondary school diploma, and high school diploma), and vocational/professional training (no vocational training, vocational training, and mastership/technical college/university degree). Anankastia was excluded from regression analyses due to its different operationalization in the PID5BF+ (completed by the general population sample) than in the PID5BF + M (completed by the PC sample; see Appendix 2 for a figure of the regression model). To ensure comparability of PID5BF+/M data between PC patients and the general population, measurement invariance was tested across both samples. Due to the occurrence of a Heywood case in the higher-order PID5BF + M model when applying CFA in the PC sample, an alternative model was implemented. Following prior studies (Bach et al., 2020; Riegel et al., 2021; Zinchuk et al., 2023), two related items per facet were aggregated to form facet scores, which were then used as indicators in a multi-group CFA (e.g., facet scores for emotional lability, anxiousness, and separation insecurity were defined to load on a corresponding domain factor representing negative affectivity). As the facet scores were derived from the sum of two items each and were therefore not considered ordinal, MLR was used instead of WLSMV. Measurement invariance was assessed sequentially at three levels: (1) evaluating the factor structure (configural invariance), (2) constraining factor loadings (metric invariance), and (3) restricting item intercepts (scalar invariance), with the latter being necessary for meaningful group comparisons (Putnick & Bornstein, 2016). Consistent with previous research addressing measurement invariance of the PID5BF+/M (Kerber et al., 2022; Riegel et al., 2021), a change of less than |0.01| in CFI and RMSEA between levels was used to indicate equivalence.

Results

Descriptive Statistics

In the PC sample, the mean values of the PID5BF + M items ranged from 0.20 (SD: 0.48) to 1.12 (SD: 0.94), with the majority of means < 1. Concerning domain scales, antagonism showed the lowest (mean = 2.42 [SD: 2.69]) and anankastia the highest (mean = 4.95 [SD: 3.82]) mean score (Table 1). The mean total PID5BF + M score was 22 (SD: 14.55).

Table 1

Descriptive Statistics, McDonald’s ω_H, and CFA Results of a 6-Factor PID5BF+M Higher-Order Model.

Items	M	SD	Skew	Kurtosis	Factor loadings
Negative affectivity (ω_H: .80)	4.83	3.72	0.69	−0.02	6-factor higher-order model
Item 01	1.12	0.94	0.27	−0.99	0.81	Emotional Lability: 0.79
Item 19	1.00	0.94	0.51	−0.78	0.94
Item 07	1.08	0.95	0.42	−0.84	0.90	Anxiousness: 0.85
Item 25	0.72	0.85	0.99	0.16	0.91	Anxiousness: 0.85
Item 13	0.70	0.87	1.05	0.17	0.79	Separation Insecurity: 0.84
Item 31	0.21	0.55	2.93	9.03	0.76
Detachment (ω_H: .79)	3.35	3.15	1.19	1.49
Item 04	0.88	0.85	0.60	−0.53	0.72	Withdrawal: 0.85
Item 22	0.42	0.68	1.63	2.19	0.83	Withdrawal: 0.85
Item 10	0.46	0.75	1.63	2.05	0.84	Anhedonia: 0.84
Item 28	0.63	0.80	1.02	0.08	0.81	Anhedonia: 0.84
Item 16	0.63	0.84	1.14	0.37	0.71	Intimacy Avoidance: 0.85
Item 34	0.33	0.66	2.10	4.06	0.83
Antagonism (ω_H: .81)	2.42	2.69	1.46	2.45
Item 02	0.52	0.77	1.39	1.20	0.71	Manipulativeness: 0.73
Item 20	0.22	0.55	2.87	8.53	0.96	Manipulativeness: 0.73
Item 08	0.62	0.79	1.05	0.26	0.73	Deceitfulness: 1.02
Item 26	0.42	0.66	1.46	1.45	0.75	Deceitfulness: 1.02
Item 14	0.38	0.67	1.83	3.00	0.69	Grandiosity: 0.96
Item 32	0.26	0.57	2.42	6.04	0.74	Grandiosity: 0.96
Disinhibition (ω_H: .75)	3.62	3.08	0.92	0.66
Item 03	0.36	0.64	1.78	2.83	0.75	Irresponsibility: 0.72
Item 21	0.33	0.68	2.20	4.30	0.66	Irresponsibility: 0.72
Item 09	0.68	0.80	0.92	0.00	0.77	Impulsivity: 0.96
Item 27	0.70	0.78	0.86	−0.01	0.77	Impulsivity: 0.96
Item 15	0.69	0.82	0.99	0.23	0.87	Distractibility: 0.76
Item 33	0.86	0.87	0.69	−0.38	0.78	Distractibility: 0.76
Psychoticism (ω_H: .81)	2.82	2.94	1.29	1.49
Item 05	0.94	0.96	0.50	−1.01	0.65	Unusual beliefs & experiences: 0.99
Item 23	0.44	0.80	1.83	2.45	0.67	Unusual beliefs & experiences: 0.99
Item 11	0.41	0.74	1.74	2.24	0.80	Eccentricity: 0.90
Item 29	0.53	0.76	1.27	0.73	0.78	Eccentricity: 0.90
Item 17	0.30	0.61	2.14	4.32	0.87	Perceptual dysregulation: 0.80
Item 35	0.20	0.48	2.68	7.58	0.84	Perceptual dysregulation: 0.80
Anankastia (ω_H: .85)	4.95	3.82	0.68	0.07
Item 06	1.09	0.93	0.37	−0.86	0.80	Perfectionism: 0.92
Item 18	0.90	0.93	0.66	−0.65	0.82	Perfectionism: 0.92
Item 12	1.10	0.93	0.26	−1.02	0.81	Rigidity: 0.94
Item 24	0.85	0.84	0.55	−0.71	0.71	Rigidity: 0.94
Item 30	0.48	0.79	1.58	1.57	0.80	Orderliness: 0.92
Item 36	0.53	0.77	1.30	0.89	0.79	Orderliness: 0.92
					Fit indices
					χ²; df; p-value	1,690.535; 561; p < .001
					CFI	0.949
					TLI	0.942
					RMSEA (90% CI)	0.044 [0.042, 0.047]
					SRMR	0.058

Note. M = mean; SD = standard deviation; ω_H = McDonald’s Omega hierarchical; bold = negative latent variance; χ² = chi-squared; df = degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; CI = confidence interval; SRMR = standardized root mean square residual; The column “6-factor higher-order model” is divided into a left-hand column with the standardized factor loadings for the items and a right-hand column with the standardized factor loadings for the higher-order facets, each corresponding to two items (e.g., facet Emotional Lability: items 01 and 19).

Factorial Validity and Reliability

Standardized factor loadings of the 6-factor PID5BF + M higher-order model ranged from 0.65 to 0.96 for the items and from 0.72 to 1.02 for the facets. Model fit indices were acceptable: CFI = 0.949, TLI = 0.942, RMSEA = 0.044, SRMR = 0.058. Notably, the model estimation resulted in a Heywood case (i.e., a negative variance for the facet deceitfulness, see Table 1). In a next step, following Kerber et al. (2022), unstandardized facet factor loadings of items were constrained to 1 to avoid potential facet factor underidentification associated with only two items per facet. However, the negative variance for the facet deceitfulness remained (see link to statistical output in the data availability statement). To test whether this may be related to distinct characteristics of the PC sample, another CFA was run – without constraining unstandardized loadings to 1 – including data from both the PC and general population samples. Due to operationalization differences in the domain anankastia between the PID5BF+ (completed by the general population sample) and the PID5BF + M (completed by the PC sample), it was excluded from the analysis. No negative variance occurred, while the overall model fit was similar (factor loadings items: 0.48–0.96; factor loadings facets: 0.66–0.97; CFI: 0.931; TLI: 0.921; RMSEA: 0.058, CI [0.057, 0.059]; SRMR: 0.054; Appendix 3).

McDonald’s ω_H values between 0.75 and 0.85 were observed, indicating an acceptable reliability of PID5BF + M domain scales (Table 1).

Convergent Validity

Factor scores of all PID5BF + M domain scales and total scores correlated significantly positively with the factor scores of the PHQ-9, GAD-7, and PHQ-15. Correlation estimates were highest for the PID5BF + M total score and the domain scale score of negative affectivity. The overall lowest correlation estimates were found for the domain scale score of antagonism (Table 2).

Table 2

Correlation Estimates of PID5BF + M Domain Scale and Total Factor Scores With PHQ-9, GAD-7, and PHQ-15 Factor Scores.

		Correlation estimates
Questionnaires	Negative affectivity	Detachment	Antagonism	Disinhibition	Psychoticism	Anankastia	Total score
PHQ-9	0.68	0.67	0.38	0.64	0.55	0.37	0.72
GAD-7	0.73	0.54	0.39	0.60	0.53	0.44	0.72
PHQ-15	0.57	0.49	0.24	0.48	0.46	0.37	0.58

Note. All correlation estimates were significant at p < .001; PHQ-9 = Patient Health Questionnaire-9; GAD-7 = Generalized Anxiety Disorder Screener-7; PHQ-15 = Patient Health Questionnaire-15.

Association Between Setting and Maladaptive Personality Traits

Measurement invariance testing showed changes in CFI and RMSEA remaining within the accepted threshold (<|0.01|) from configural to metric invariance (△CFI = −0.005; △RMSEA = −0.000) and from metric to scalar invariance (△CFI = −0.008; △RMSEA = 0.001), supporting group comparisons.

Regression analysis indicated that the PC setting was incrementally associated with significantly lower factor scores of all maladaptive personality traits (excluding anankastia) compared to the general population. Concerning the sociodemographic variables, significantly lower levels of maladaptive personality traits were mainly found for female gender (except for negative affectivity), increasing age, being or having been in a relationship/married, as well as for participants with a higher level of education and vocational/professional training. However, the associations with education and training appeared smaller and less consistent across traits. The unadjusted coefficients of determination (R²) ranged from 0.05 to 0.13 across the five personality domains, indicating that the sample setting and socio-demographic variables explained a limited share of total variance in maladaptive personality traits (Table 3). For details on variance inflation factor (VIF) values, see the link to the statistical output in the data availability statement. Across all regression models, VIF values were <2, falling within the recommended threshold (VIF < 5; James et al., 2013).

Table 3

Regression Analysis of the Association Between Setting (PC, General Population) and PID5BF+/M Domain Scale Scores, Controlled for Gender, Age, Marital Status, Highest Level of Education, and Vocational/Professional Training.

Sociodemographic factors	Standardized regression estimates
Sociodemographic factors	Negative affectivity	Detachment	Antagonism	Disinhibition	Psychoticism
R ²	0.081	0.081	0.132	0.050	0.096
Setting
General population
PC	−0.178***	−0.140***	−0.190***	−0.128***	−0.257***
Gender
Male
Female	0.170***	−0.126***	−0.236***	−0.015	−0.081***
Age (increasing age)	−0.095***	0.035	−0.221***	−0.154***	−0.073**
Marital status
Single
Relationship/married	−0.096***	−0.249***	−0.089***	−0.103***	−0.138***
Annulled	−0.049*	−0.108***	−0.033	−0.024	−0.035
Widowed	−0.030	−0.082***	−0.006	−0.004	−0.029
Highest level of education
No school-leaving certificate	0.011	0.027	0.005	0.018	0.025
Lower secondary school diploma	0.069**	0.024	−0.028	0.040	0.055**
Secondary school diploma	0.057**	0.019	−0.013	0.042	0.024
High school diploma
Vocational/ professional training
No vocational training	0.052**	0.010	0.017	0.041*	0.037*
Vocational training	0.048*	0.032	0.027	0.052*	0.068**
Mastership/technical college/university degree

Note.*p < .05. **p < .01. ***p < .001.

R ² = coefficient of determination, unadjusted; italic = reference category; PC = primary care.

Regression coefficients indicate the estimated difference in maladaptive personality trait scores for each category compared to its respective reference category, controlling for all other variables in the model. Positive values reflect higher trait scores relative to the reference category, while negative values reflect lower scores.

As recent data on vocational/professional training of the general population were only available for the second and third cohort of the GESIS wave “Ge” (data on vocational/professional training from 2016 and 2018, respectively), another regression analysis was performed, excluding the participants of the first cohort (data on vocational/professional training from 2013). Results on the association between the PC setting and maladaptive personality traits remained unchanged (Appendix 4).

Discussion

Assessing personality pathology and maladaptive traits as transdiagnostic indicators of mental illness (Fowler et al., 2022; Kerber et al., 2022; Kotov et al., 2010; Rek et al., 2021) may assist the diagnostic procedure of GPs (Neulinger et al., 2024; Porcerelli et al., 2019). This study aimed to examine the factorial validity, reliability, and convergent validity of the PID5BF + M in German PC patients. In addition, the association between the PC setting and maladaptive personality traits was investigated in comparison to the general population.

Model fit of the PID5BF + M

The overall acceptable model fit aligns with the initial validation study of the PID5BF + M by Bach et al. (2020) and subsequent validation studies in Czech (Riegel et al., 2021) and Russian (Zinchuk et al., 2023) samples. These studies used exploratory structural equation modeling (ESEM) instead of CFA. ESEM has been suggested to better accommodate the complexity of personality due to its less restrictive modeling assumptions (Hopwood & Donnellan, 2010). However, the three studies examined a less complex model of the PID5BF + M, estimating model fit only at the facet level; that is, loading three related facets onto their respective personality domain, without accounting for individual items. Considering model complexity similar to the current study, Rek et al. (2021) and Kerber et al. (2022) both specified a higher-order model to examine the PID5BF+ model fit. Results indicated an overall acceptable model fit (CFI: 0.925/0.941; RMSEA: 0.060/0.055; SRMR: 0.057/0.059, respectively). Notably, both studies reported scaled rather than robust model fit indices. To ensure comparability, the present study took the same approach. Thus, findings should be viewed in light of potential overestimation of model fit (Savalei, 2018, 2021), as robust indices would have indicated a poorer model fit in the PC sample (robust CFI: 0.860; robust TLI: 0.843; robust RMSEA: 0.074). In general, inadequate CFA results for established personality questionnaires are common and may stem from the complexity of operationalizing personality (Hopwood & Donnellan, 2010). Model complexity rather than model misspecification may have also contributed to the negative variance obtained for the deceitfulness facet, which was also reported by Kerber et al. (2022). A smaller sample size and fewer indicators per latent variable have been associated with an increased likelihood of a Heywood case (Cooperman & Waller, 2022), particularly in hierarchical CFA (Marsh, 1987). Thus, using a larger combined sample (i.e., PC and general population) likely improved model estimation, allowing for a complex higher-order structure with only two items per facet while avoiding a Heywood case and achieving a comparable model fit.

Reliability of the PID5BF + M

McDonald’s ω_H values for the PID5BF + M domain scales were higher than those reported by Rek et al. (2021; ω_H = 0.66–0.71). It should be noted that a fixed cut-off indicating acceptable reliability for McDonald’s ω_H is still under debate (Garcia-Garzon et al., 2021; Reise et al., 2013). Internal consistency estimates ≥ 0.70 were suggested to be adequate for research settings (Nunnally, 1978). Using categorical McDonald’s Omega, both the Czech (Riegel et al., 2021) and Russian (Zinchuk et al., 2023) studies obtained acceptable internal consistency (ω = 0.65–0.75; ω = 0.83–0.90, respectively) for all domain scales, except for detachment (ω = 0.59) in the Czech-speaking sample. The high McDonald’s ω_H coefficient found for anankastia in the current study further supports the superiority of defining anankastia with the three facets of perfectionism, rigidity, and orderliness, rather than the two facets of rigid perfectionism and perseveration, as is done in the PID5BF+ (Bach et al., 2020; Kerber et al., 2022; Riegel et al., 2021).

Association Between Maladaptive Personality Traits and the PC Setting

Personality disorders are likely to be more prevalent in individuals seeking healthcare services (Tyrer et al., 2015). Therefore, the significantly lower manifestations of maladaptive personality traits in the PC sample relative to the general population sample were unexpected. Other studies assessing PID-5 personality traits have similarly reported higher values in community samples (Bach et al., 2020; Rek et al., 2021) and among PC patients (Porcerelli et al., 2019). One explanation may be socioeconomic status, demonstrated to be negatively associated with personality pathology (Luo et al., 2024; Rek et al., 2021) and mental health (Fryers et al., 2005; Kivimäki et al., 2020; Lampert et al., 2018; Pinto-Meza et al., 2013). As the PC sample was mainly collected in the urban and suburban regions of Munich – one of the most affluent cities in Germany (Fink et al., 2019; City of Munich, 2022, p. 61) – a higher overall socioeconomic status of the PC sample relative to the general population sample can be assumed. In addition, given the role of PC as the first point of contact for individuals with mental health concerns (Alexander et al., 2010), the setting likely selects for individuals from the general population experiencing heightened mental distress. As a result, the variable “setting” may capture some of the variance in mental distress between PC patients and the general population. However, this study was unable to explicitly account for this factor, as no corresponding measure of mental distress was available for the general population sample. Thus, future research should aim to include measures of both socioeconomic status and mental distress to examine additional explanatory factors. The coefficients of determination are also noteworthy: only a limited proportion of the total variance in maladaptive personality traits (5–13%) was accounted for by the sociodemographic variables and sample setting. Within the context of simple linear regression, however, these R² values correspond to correlation estimates between 0.22 and 0.36, which are generally regarded as moderate to large effect sizes (Funder & Ozer, 2019).

The observed associations between maladaptive personality traits and the sociodemographic variables age, gender, marital status, and level of education have already been discussed by Rek et al. (2021), as the majority of data for the regression analyses were based on the same German population sample (i.e., same data of GESIS).

Convergent Validity of the PID5BF + M

The correlation estimates found between personality pathology and maladaptive personality traits with depressive, anxiety, and somatic symptom severity support the convergent validity of the PID5BF + M. Fowler et al. (2022) reported a similar correlation pattern between the PID-5 domain scale scores and the PHQ-9, GAD-7, and PHQ-15 scores. Severity of personality pathology and negative affectivity in particular, as well as detachment, were most commonly associated with psychopathology, linking both to the presence of mental health conditions and their change over time (Böhnke et al., 2014; Fowler et al., 2022; Hong & Tan, 2021; Kerber et al., 2022; Rek et al., 2021; Wendt et al., 2024; Wright et al., 2015; Zinchuk et al., 2023). Furthermore, psychoticism was shown to be a differentiating factor between mental health conditions of varying severity (Kerber et al., 2022). For disinhibition, a longitudinal link with changes in psychological functioning was indicated (Wright et al., 2015). Conversely, in a mixed German sample of students and inpatients, no incremental association was found between disinhibition and the presence of depressive, anxiety, or somatic symptoms (Zimmermann et al., 2014). Concerning anankastia, a link with obsessive-compulsive personality disorder was suggested (Bach et al., 2020), symptoms of which co-occur with depression, anxiety, and somatic symptom disorder (Bienvenu et al., 2012; Sharma et al., 2021; Torres et al., 2016). In addition, perfectionism, as a facet of anankastia, was identified as an underlying factor of internalizing mental disorders (Bienvenu et al., 2012; Sharma et al., 2021; Torres et al., 2016). The comparably lower correlations of antagonism with the PHQ-9, GAD-7, and PHQ-15 scores may reflect the stronger attribution of this domain to externalizing than internalizing psychopathology (Lynam & Miller, 2019).

Implications for Assessment and Treatment

The findings support the notion that maladaptive personality traits function as higher-order transdiagnostic factors for lower-order mental health conditions (Böhnke et al., 2014; Fowler et al., 2022; Hong & Tan, 2021; Kerber et al., 2022; Kotov et al., 2010; Rek et al., 2021; Stanton et al., 2016; Subica et al., 2016; Wendt et al., 2024; Wright et al., 2015). In agreement with literature (Böhnke et al., 2014; Kerber et al., 2022; Neulinger et al., 2024; Porcerelli et al., 2019; Rek et al., 2021; Widiger & Oltmanns, 2017), especially overall personality pathology and negative affectivity – while not disregarding other personality traits (Kotov et al., 2010) – may assist GPs in screening patients for mental health conditions, particularly depressive, anxiety, and somatoform symptom severity. Given the time constraints of PC consultations (Irving et al., 2017), administering the full 36-item PID5BF + M for routine screening may not be feasible. A pragmatic alternative could be a two-step screening approach (Neulinger et al., 2024; Rogers et al., 2021) in which the negative affectivity subscale is first used to flag potential psychological distress, followed by the full PID5BF + M for a more comprehensive assessment if indicated. To inform subsequent treatment, further research is needed to identify bridging factors that mediate the relationship between maladaptive personality traits and mental health symptoms (Rodriguez-Seijas et al., 2015; Sauer-Zavala et al., 2022). One promising example is the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (Barlow et al., 2017), which targets transdiagnostic factors such as mindfulness, cognitive flexibility as well as emotion-based avoidance – underlying mechanisms of neuroticism – and has been shown to effectively reduce both mental health symptoms (Carlucci et al., 2021) and neuroticism itself (Sauer-Zavala et al., 2021). Ultimately, a shift toward dimensional classification models that reflect the hierarchical structure of psychopathology (see e.g., HiTOP; Kotov et al., 2017) would allow for identification of broad vulnerability factors at higher levels, followed by targeted assessment of specific lower-order mechanisms, facilitating both diagnostic precision and treatment planning in clinical practice.

Strengths and Limitations

This study is the first to provide a comprehensive evaluation of the psychometric properties of the PID5BF + M in the PC setting, assessing its confirmatory factorial validity, reliability, and convergent validity. Other strengths of the study are the large sample size, the use of the PID5BF + M as a standalone measure instead of extracting data from longer PID-5 versions (Bach et al., 2020), and complementing analyses with a representative sample of the German population.

The generalization of results to the entire PC setting is limited by the restriction of data collection to the area of Munich, Germany. Another limitation is that measurement invariance of the PID5BF + M between the PC and general population samples was assessed only at the facet level, warranting reevaluation at the item level. However, previous research has already supported measurement invariance of the PID-5 across clinical and non-clinical samples (Bach et al., 2018). Also noteworthy is the exclusion of items on anankastia from the measurement invariance and regression analyses due to the different operationalization of this domain in the PID5BF+ and PID5BF + M. Although supported by research (Pires et al., 2023; Zimmermann et al., 2020), it should further be considered that the total score of the PID5BF + M was used as a surrogate measure of the severity of personality pathology. Lastly, the PID5BF + M is a very brief version of the original Personality Inventory for DSM-5. Thus, any presented results should be understood as provisional rather than confirmatory.

Conclusion

The PID5BF + M demonstrated adequate factorial validity and reliability in the PC sample. Correlations between maladaptive personality traits and pathology with depressive, anxiety, and somatic symptom severity supported its convergent validity. GPs may consider the PID5BF + M as a valuable tool to screen for maladaptive personality traits and pathology in their patients, providing transdiagnostic insights into lower-order symptoms of mental illness. Future research should reexamine the psychometric properties of the PID5BF + M higher-order model by applying robust model fit indices and less stringent model specifications (e.g., allowing for correlated residuals) to better capture the complexity of personality assessment. In addition, the association between PC and the manifestation of maladaptive personality traits and pathology warrants further investigation. Finally, future studies could explore the mechanisms linking maladaptive personality traits to mental health symptoms to refine screening approaches and inform targeted treatment strategies in clinical practice.

Footnotes

Appendix

2.2. Heterogeneous correlation matrix of sociodemographic variables.

Variable	Age	Gender	Marital status	Highest level of education	Vocational/professional training
Age	1.000	−0.095	0.510	−0.308	0.135
Gender	−0.095	1.000	0.151	−0.037	−0.178
Marital status	0.510	0.151	1.000	−0.229	0.102
Highest level of education	−0.308	−0.037	−0.229	1.000	0.543
Vocational/professional training	0.135	−0.178	0.102	0.543	1.000

Acknowledgements

*The POKAL-Group (PrädiktOren und Klinische Ergebnisse bei depressiven ErkrAnkungen in der hausärztLichen Versorgung [POKAL, DFG-GrK 2621]) consists of the following principal investigators: Markus Bühner, Tobias Dreischulte, Peter Falkai, Jochen Gensichen, Peter Henningsen, Caroline Jung-Sievers, Helmut Krcmar, Kirsten Lochbühler, Karoline Lukaschek, Gabriele Pitschel-Walz, Barbara Prommegger, Andrea Schmitt, and Antonius Schneider. The following doctoral students are members of the POKAL-Group: Katharina Biersack, Vita Brisnik, Christopher Ebert, Julia Eder, Feyza Gökce, Carolin Haas, Lisa Pfeiffer, Lukas Kaupe, Jonas Raub, Philipp Reindl-Spanner, Hannah Schillok, Petra Schönweger, Clara Teusen, Marie Vogel, Victoria von Schrottenberg, Jochen Vukas, and Puya Younesi.

Author Contributions

Conceptualization: C.E. and V.S.; Data curation: V.S. and T.O.; Formal analysis: T.O., P.S., and C.E.; Funding acquisition: J.G.; Investigation: V.S.; Methodology: C.E., V.S., T.O., and P.S.; Project administration: C.E.; Resources: V.S.; Software: T.O., P.S., C.E., and V.S.; Supervision: J.Z., C.E. and V.S.; Visualization: C.E.; Writing—original draft: C.E., V.S., and T.O.; Writing—review and editing: C.E., V.S., T.O., P.S., J.Z., and J.G.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.Z. was a co-developer of the PID5BF + M. The authors have no other conflicts of interest with respect to the research, authorship, and/or publication of this article to declare.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was funded by the German Research Foundation (DFG-GrK 2621/POKAL-Kolleg) and endorsed by the German Center for Mental Health (Deutsches Zentrum für Psychische Gesundheit [DZPG], grant: 01EE2303A). Neither of them had a role in the design, data collection, data analysis, and reporting of this study.

Ethical Considerations

The Ethics Committee of the Medical Faculty of the Technical University of Munich reviewed and approved this study's procedures (approval no. 2022-158 S) on December 4, 2022.

Consent to Participate

To maintain complete anonymity, only oral informed consent was collected from participants. This approach was endorsed by the Ethics Committee and aligned with ethical standards outlined in the World Medical Association Declaration of Helsinki.

Consent for Publication

Not applicable.

ORCID iDs

Christopher Ebert

Philipp Sterner

Johannes Zimmermann

Data Availability Statement

For reasons of data protection, neither the PC dataset nor the general population (i.e., GESIS) dataset can be made publicly available. To facilitate an approximate replication of the analyses, provides variance-covariance matrices of the PID5BF + M facets and heterogeneous correlation matrices of the sociodemographic variables for both the PC dataset and the combined PC/general population dataset. The statistical code and output can be obtained from the Figshare repository.

References

Abdi

Pak

(2019). The mediating role of emotion dysregulation as a transdiagnostic factor in the relationship between pathological personality dimensions and emotional disorders symptoms severity. Personality and Individual Differences, 142, 282–287. https://doi.org/https://doi.org/10.1016/j.paid.2018.09.026

Alexander

C. L.

Arnkoff

D. B.

Glass

C. R.

(2010). Bringing psychotherapy to primary care: Innovations and challenges. Clinical Psychology: Science and Practice, 17(3), 191–214. https://doi.org/10.1111/j.1468-2850.2010.01211.x

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5™ (5th ed.). American Psychiatric Publishing, Inc. https://doi.org/10.1176/appi.books.9780890425596

American Psychiatric Association. (2025). DSM-5-TR Online Assessment Measures. https://www.psychiatry.org/psychiatrists/practice/dsm/educational-resources/assessment-measures

Bach

Kerber

Aluja

Bastiaens

Keeley

J. W.

Claes

Fossati

Gutierrez

Oliveira Sérgio

E. S.

Pires

Riegel Karel

Rolland

J.-P.

Roskam

Sellbom

Somma

Spanemberg

Strus

Thimm

J. C.

Wright

A. G. C.

Zimmermann

(2020). International assessment of DSM-5 and ICD-11 personality disorder traits: Toward a common nosology in DSM-5.1. Psychopathology, 53(3–4), 179–188. https://doi.org/10.1159/000507589

Bach

Sellbom

Simonsen

(2018). Personality Inventory for DSM-5 (PID-5)in clinical versus nonclinical individuals: Generalizability of psychometric features. Assessment, 25(7), 815–825. https://doi.org/10.1177/1073191117709070

Barlow

D. H.

Curreri

A. J.

Woodard

L. S.

(2021). Neuroticism and disorders of emotion: A new synthesis. Current Directions in Psychological Science, 30(5), 410–417. https://doi.org/10.1177/09637214211030253

Barlow

D. H.

Farchione

T. J.

Sauer-Zavala

Murray Latin

Ellard

K. K.

Bullis

J. R.

Bentley

K. H.

Boettcher

H. T.

Cassiello-Robbins

(2017). Unified protocol for transdiagnostic treatment of emotional disorders: Therapist guide. Oxford University Press. https://doi.org/10.1093/med-psych/9780190685973.001.0001

Bienvenu

O. J.

Samuels

J. F.

Wuyek

L. A.

Liang

K. Y.

Wang

Grados

M. A.

Cullen

B. A.

Riddle

M. A.

Greenberg

B. D.

Rasmussen

S. A.

Fyer

A. J.

Pinto

Rauch

S. L.

Pauls

D. L.

McCracken

J. T.

Piacentini

Murphy

D. L.

Knowles

J. A.

Nestadt

(2012). Is obsessive-compulsive disorder an anxiety disorder, and what, if any, are spectrum conditions? A family study perspective. Psychological Medicine, 42(1), 1–13. https://doi.org/10.1017/s0033291711000742

10.

Blümel

Spranger

Achstetter

Maresso

Busse

(2020). Germany: Health system review. Health System in Transition, 22(6), 1–272.

11.

Böhnke

J. R.

Lutz

Delgadillo

(2014). Negative affectivity as a transdiagnostic factor in patients with common mental disorders. Journal of Affective Disorders, 166, 270–278. https://doi.org/10.1016/j.jad.2014.05.023

12.

Boschloo

van Borkulo

C. D.

Rhemtulla

Keyes

K. M.

Borsboom

Schoevers

R. A.

(2015). The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS One, 10(9), e0137621. https://doi.org/10.1371/journal.pone.0137621

13.

Bosnjak

Dannwolf

Enderle

Schaurer

Struminskaya

Tanner

Weyandt

K. W.

(2017). Establishing an open probability-based mixed-mode panel of the general population in Germany: The GESIS panel. Social Science Computer Review, 36(1), 103–115. https://doi.org/10.1177/0894439317697949

14.

Brown

J. D.

Wissow

L. S.

(2012). Rethinking the mental health treatment skills of primary care staff: a framework for training and research. Administration and Policy in Mental Health, 39(6), 489–502. https://doi.org/10.1007/s10488-011-0373-9

15.

Bullis

J. R.

Boettcher

Sauer-Zavala

Farchione

T. J.

Barlow

D. H.

(2019). What is an emotional disorder? A transdiagnostic mechanistic definition with implications for assessment, treatment, and prevention. Clinical Psychology: Science and Practice, 26(2), e12278. https://doi.org/10.1111/cpsp.12278

16.

Busse

Blümel

Knieps

Bärnighausen

(2017). Statutory health insurance in Germany: A health system shaped by 135 years of solidarity, self-governance, and competition. The Lancet, 390(10097), 882–897. https://doi.org/10.1016/S0140-6736(17)31280-1

17.

Carlucci

Saggino

Balsamo

(2021). On the efficacy of the unified protocol for transdiagnostic treatment of emotional disorders: A systematic review and meta-analysis. Clinical Psychology Review, 87, 101999. https://doi.org/10.1016/j.cpr.2021.101999

18.

Caspi

Houts

R. M.

Ambler

Danese

Elliott

M. L.

Hariri

Harrington

Hogan

Poulton

Ramrakha

Rasmussen

L. J. H.

Reuben

Richmond-Rakerd

Sugden

Wertz

Williams

B. S.

Moffitt

T. E.

(2020). Longitudinal assessment of mental health disorders and comorbidities across 4 decades among participants in the Dunedin Birth Cohort Study. JAMA Netw Open, 3(4), e203221. https://doi.org/10.1001/jamanetworkopen.2020.3221

19.

City of Munich. (2022). Munich Poverty Report 2022 [internet]. City of Munich, Department of Social Affairs. https://www.muenchen.info/soz/pub/pdf/674_SOZ_Muenchner-Armutsbericht-2022_barrierefrei.pdf

20.

Clark

L. A.

Cuthbert

Lewis-Fernández

Narrow

W. E.

Reed

G. M.

(2017). Three approaches to understanding and classifying mental disorder: ICD-11, DSM-5, and the National Institute of Mental Health’s Research Domain Criteria (RDoC). Psychological Science in the Public Interest, 18(2), 72–145. https://doi.org/10.1177/1529100617727266

21.

Cooperman

A. W.

Waller

N. G.

(2022). Heywood you go away! Examining causes, effects, and treatments for Heywood cases in exploratory factor analysis. Psychological Methods, 27(2), 156–176. https://doi.org/10.1037/met0000384

22.

Dalgleish

Black

Johnston

Bevan

(2020). Transdiagnostic approaches to mental health problems: Current status and future directions. Journal of Consulting and Clinical Psychology, 88(3), 179–195. https://doi.org/10.1037/ccp0000482

23.

Fink

Hennicke

Tiemann

(2019). Unequal Germany: Socioeconomic disparities report 2019 [internet]. Friedrich-Ebert-Stiftung. https://library.fes.de/cgi-bin/populo/digbib.pl?f_TIT=ungleiches%20deutschland&f_STW=lang-eng&f_ABC=periodic-item&t_listen=x&sortierung=jab

24.

Forbes

M. K.

Neo

Nezami

O. M.

Fried

E. I.

Faure

Michelsen

Twose

Dras

(2023). Elemental psychopathology: Distilling constituent symptoms and patterns of repetition in the diagnostic criteria of the DSM-5. Psychological Medicine, 54(5), 1–9. https://doi.org/10.1017/s0033291723002544

25.

Fowler

J. C.

Lamkin

Allen

J. G.

Madan

Oldham

J. M.

Petersen

N. J.

Frueh

B. C.

(2022). Personality trait domains predict psychiatric symptom and functional outcomes. Psychotherapy (Chic), 59(1), 38–47. https://doi.org/10.1037/pst0000421

26.

Fox

(2022). _polycor: Polychoric and Polyserial Correlations_. R package version 0.8-1 [software]. https://r-forge.r-universe.dev/polycor

27.

Fox

Weisberg

(2019). An R Companion to Applied Regression [software] (3rd ed.). Sage. https://www.john-fox.ca/Companion/

28.

Fryers

Melzer

Jenkins

Brugha

(2005). The distribution of the common mental disorders: Social inequalities in Europe. Clinical Practice and Epidemiology in Mental Health, 1(1), 14. https://doi.org/10.1186/1745-0179-1-14

29.

Funder

D. C.

Ozer

D. J.

(2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168. https://doi.org/10.1177/2515245919847202

30.

Gaebel

Kowitz

Fritze

Zielasek

(2013). Use of health care services by people with mental illness. Deutsches Ärzteblatt International, 110(47), 799–808. https://doi.org/10.3238/arztebl.2013.0799

31.

Garcia-Garzon

Abad

F. J.

Garrido

L. E.

(2021). On omega hierarchical estimation: A comparison of exploratory bi-factor analysis algorithms. Multivariate Behavioral Research, 56(1), 101–119. https://doi.org/10.1080/00273171.2020.1736977

32.

Gili

Comas

García-García

Monzón

Antoni

S. B.

Roca

(2010). Comorbidity between common mental disorders and chronic somatic diseases in primary care patients. General Hospital Psychiatry, 32(3), 240–245. https://doi.org/10.1016/j.genhosppsych.2010.01.013

33.

Gohel

Moog

Heckmann

(2025). officer: Manipulation of Microsoft Word and PowerPoint Documents. R package version 0.6.8 [software]. https://davidgohel.github.io/officer/

34.

Gohel

Skintzos

(2024). flextable: Functions for Tabular Reporting. R package version 0.9.7 [software]. https://davidgohel.github.io/flextable/

35.

Green

L. A.

Fryer

G. E.

Jr. Yawn

B. P.

Lanier

Dovey

S. M.

(2001). The ecology of medical care revisited. The New England Journal of Medicine, 344(26), 2021–2025. https://doi.org/10.1056/nejm200106283442611

36.

Häfner

Petzold

E. R.

(2007). The role of primary care practitioners in psychosocial care in Germany. The Permanente Journal, 11(1), 52–55. https://doi.org/10.7812/tpp/06-111

37.

Hanel

Henningsen

Herzog

Sauer

Schaefert

Szecsenyi

Löwe

(2009). Depression, anxiety, and somatoform disorders: Vague or distinct categories in primary care? Results from a large cross-sectional study. Journal of Psychosomatic Research, 67(3), 189–197. https://doi.org/10.1016/j.jpsychores.2009.04.013

38.

Haslam

McGrath

M. J.

Viechtbauer

Kuppens

(2020). Dimensions over categories: A meta-analysis of taxometric research. Psychological Medicine, 50(9), 1418–1432. https://doi.org/10.1017/s003329172000183x

39.

Hong

R. Y.

Tan

Y. L.

(2021). DSM-5 personality traits and cognitive risks for depression, anxiety, and obsessive-compulsive symptoms. Personality and Individual Differences, 169, 110041. https://doi.org/https://doi.org/10.1016/j.paid.2020.110041

40.

Hopwood

C. J.

Donnellan

M. B.

(2010). How should the internal structure of personality inventories be evaluated? Personality and Social Psychology Review, 14(3), 332–346. https://doi.org/10.1177/1088868310361240

41.

Hopwood

C. J.

Kotov

Krueger

R. F.

Watson

Widiger

T. A.

Althoff

R. R.

Ansell

E. B.

Bach

Michael Bagby

Blais

M. A.

Bornovalova

M. A.

Chmielewski

Cicero

D. C.

Conway

De Clercq

De Fruyt

Docherty

A. R.

Eaton

N. R.

Edens

J. F.

… Zimmermann

(2018). The time has come for dimensional personality disorder diagnosis. Personal and Mental Health, 12(1), 82–86. https://doi.org/10.1002/pmh.1408

42.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

43.

Irving

Neves

A. L.

Dambha-Miller

Oishi

Tagashira

Verho

Holden

(2017). International variations in primary care physician consultation time: A systematic review of 67 countries. BMJ Open, 7(10), e017902. https://doi.org/10.1136/bmjopen-2017-017902

44.

James

Witten

Hastie

Tibshirani

(2013). An introduction to statistical learning (Vol. 112). Springer.

45.

Jorgensen

D. T.

Pornprasertmanit

Schoemann

M. A.

Rosseel

Miller

Quick

Garnier-Villarreal

Selig

Boulton

Preacher

Coffman

Rhemtulla

Robitzsch

Enders

Arslan

Clinton

Panko

Merkle

Chesnut

… Johnson

R. A.

(2022). Package “semTool,”—Useful tools for structural equation modeling [software]. https://cran.r-project.org/web/packages/semTools/semTools.pdf

46.

Kalkbrenner

M. T.

(2023). Alpha, omega, and H internal consistency reliability estimates: Reviewing these options and when to use them. Counseling Outcome Research and Evaluation, 14(1), 77–88. https://doi.org/10.1080/21501378.2021.1940118

47.

Kerber

Schultze

Müller

Rühling

R. M.

Wright

A. G. C.

Spitzer

Krueger

R. F.

Knaevelsrud

Zimmermann

(2022). Development of a short and ICD-11 compatible measure for DSM-5 maladaptive personality traits using ant colony optimization algorithms. Assessment, 29(3), 467–487. https://doi.org/10.1177/1073191120971848

48.

Kivimäki

Batty

G. D.

Pentti

Shipley

M. J.

Sipilä

P. N.

Nyberg

S. T.

Suominen

S. B.

Oksanen

Stenholm

Virtanen

Marmot

M. G.

Singh-Manoux

Brunner

E. J.

Lindbohm

J. V.

Ferrie

J. E.

Vahtera

(2020). Association between socioeconomic status and the development of mental and physical health conditions in adulthood: A multi-cohort study. The Lancet Public Health, 5(3), e140–e149. https://doi.org/10.1016/S2468-2667(19)30248-8

49.

Kotov

Gamez

Schmidt

Watson

(2010). Linking “big” personality traits to anxiety, depressive, and substance use disorders: A meta-analysis. Psychological Bulletin, 136(5), 768–821. https://doi.org/10.1037/a0020327

50.

Kotov

Krueger

R. F.

Watson

Achenbach

T. M.

Althoff

R. R.

Bagby

R. M.

Brown

T. A.

Carpenter

W. T.

Caspi

Clark

L. A.

Eaton

N. R.

Forbes

M. K.

Forbush

K. T.

Goldberg

Hasin

Hyman

S. E.

Ivanova

M. Y.

Lynam

D. R.

Markon

… Zimmerman

(2017). The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126(4), 454–477. https://doi.org/10.1037/abn0000258

51.

Kroenke

Spitzer

R. L.

Williams

J. B.

(2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

52.

Kroenke

Spitzer

R. L.

Williams

J. B.

(2002). The PHQ-15: Validity of a new measure for evaluating the severity of somatic symptoms. Psychosomatic Medicine, 64(2), 258–266. https://doi.org/10.1097/00006842-200203000-00008

53.

Krueger

R. F.

Derringer

Markon

K. E.

Watson

Skodol

A. E.

(2012). Initial construction of a maladaptive personality trait model and inventory for DSM-5. Psychological Medicine, 42(9), 1879–1890. https://doi.org/10.1017/s0033291711002674

54.

Lahey

B. B.

(2009). Public health significance of neuroticism. The American Psychologist, 64(4), 241–256. https://doi.org/10.1037/a0015309

55.

Lampert

Kroll

L. E.

Kuntz

Hoebel

(2018). Health inequalities in Germany and in international comparison: Trends and developments over time. Journal of Health Monitoring, 3(Suppl 1), 1–24. https://doi.org/10.17886/rki-gbe-2018-036

56.

Leibniz Institute for the Social Sciences. (2023). GESIS Panel. https://www.gesis.org/gesis-panel/gesis-panel-home

57.

C.-H.

(2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949. https://doi.org/10.3758/s13428-015-0619-7

58.

Linden

(2004). Mental disorders in primary care. In Diefenbacher

(Ed.), Consultation-liaison psychiatry in Germany, Austria and Switzerland (Vol. 26, pp. 52–65). KARGER. https://www.karger.com/Article/FullText/79760

59.

Löwe

Decker

Müller

Brähler

Schellberg

Herzog

Herzberg

P. Y.

(2008). Validation and standardization of the generalized anxiety disorder screener (GAD-7) in the general population. Medical Care, 46(3), 266–274. https://doi.org/10.1097/MLR.0b013e318160d093

60.

Luo

Zhang

Antonoplis

Mroczek

D. K.

(2024). The effects of socioeconomic status on personality development in adulthood and aging. Journal of Personality, 92(1), 243–260. https://doi.org/10.1111/jopy.12801

61.

Lynam

D. R.

Miller

J. D.

(2019). The basic trait of Antagonism: An unfortunately underappreciated construct. Journal of Research in Personality, 81, 118–126. https://doi.org/https://doi.org/10.1016/j.jrp.2019.05.012

62.

Makowski

Wiernik

Patil

Lüdecke

Ben-Shachar

(2022). correlation: Methods for Correlation Analysis [software]. https://CRAN.R-project.org/package=correlation

63.

Marsh

H. W.

(1987). The hierarchical structure of self-concept and the application of hierarchical confirmatory factor analysis. Journal of Educational Measurement, 24(1), 17–39. https://doi.org/10.1111/j.1745-3984.1987.tb00259.x

64.

Menzies

R. E.

Richmond

Sharpe

Skeggs

Liu

Coutts-Bain

(2024). The “revolving door” of mental illness: A meta-analysis and systematic review of current versus lifetime rates of psychological disorders. The British Journal of Clinical Psychology, 63(2), 178–196. https://doi.org/10.1111/bjc.12453

65.

Mokraoui

N. M.

Haggerty

Almirall

Fortin

(2016). Prevalence of self-reported multimorbidity in the general population and in primary care practices: A cross-sectional study. BMC Research Notes, 9, 314. https://doi.org/10.1186/s13104-016-2121-4

66.

Neulinger

Ebert

Lochbühler

Bergmann

Gensichen

Lukaschek

(2024). Screening tools assessing mental illness in primary care: A systematic review. European Journal of General Practice, 30(1), 2418299. https://doi.org/10.1080/13814788.2024.2418299

67.

Newson

J. J.

Pastukh

Thiagarajan

T. C.

(2021). Poor separation of clinical symptom profiles by DSM-5 disorder criteria. Frontiers in Psychiatry, 12, 775762. https://doi.org/10.3389/fpsyt.2021.775762

68.

Nunnally

J. C.

(1978). Psychometric theory (2nd ed.). McGraw-Hill.

69.

Oltmanns

J. R.

Widiger

T. A.

(2018). A self-report measure for the ICD-11 dimensional trait model proposal: The personality inventory for ICD-11. Psychological Assessment, 30(2), 154–169. https://doi.org/10.1037/pas0000459

70.

Perkins

E. R.

Sörman

McDermott

K. A.

Patrick

C. J.

(2019). Interrelations among biologically relevant personality traits, emotion regulation strategies, and clinical symptoms. Journal of Psychopathology and Behavoural Assessment, 41(4), 549–559. https://doi.org/10.1007/s10862-018-9709-z

71.

Pinto-Meza

Moneta

M. V.

Alonso

Angermeyer

M. C.

Bruffaerts

Caldas

Almeida

J. M.

de Girolamo

de Graaf

Florescu

Kovess Masfety

O’Neill

Vassilev

Haro

J. M.

(2013). Social inequalities in mental health: results from the EU contribution to the World Mental Health surveys initiative. Social Psychiatry and Psychiatric Epidemiology, 48(2), 173–181. https://doi.org/10.1007/s00127-012-0536-3

72.

Pires

Henriques-Calado

Sousa Ferreira

Bach

Paulino

Gama Marques

Ribeiro Moreira

Grácio

Gonçalves

(2021). The utility of ICD-11 and DSM-5 traits for differentiating patients with personality disorders from other clinical groups. Frontiers in Psychiatry, 12, 633882. https://doi.org/10.3389/fpsyt.2021.633882

73.

Pires

Henriques-Calado

Sousa Ferreira

Gama Marques

Ribeiro Moreira

Barata

B. C.

Paulino

Gonçalves

(2023). Bridging the ICD11 and the DSM-5 personality disorders classification systems: The role of the PID5BF + M. Frontiers in Psychiatry, 14, 1004895. https://doi.org/10.3389/fpsyt.2023.1004895

74.

Porcerelli

J. H.

Hopwood

C. J.

Jones

J. R.

(2019). Convergent and discriminant validity of personality inventory for DSM-5-BF in a primary care sample. Journal of Personality Disorders, 33(6), 846–856. https://doi.org/10.1521/pedi_2018_32_372

75.

Putnick

D. L.

Bornstein

M. H.

(2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004

76.

R Core Team. (2021). R: A language and environment for statistical [software]. R Foundation for Statistical Computing. https://www.R-project.org/

77.

Reise

S. P.

Bonifay

W. E.

Haviland

M. G.

(2013). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95(2), 129–140. https://doi.org/10.1080/00223891.2012.725437

78.

Rek

Kerber

Kemper

Zimmermann

(2021). Getting the personality inventory for DSM-5 ready for clinical practice: Norm values and correlates in a representative sample from the German population. Open Science Framework [Preprint]. https://doi.org/https://doi.org/10.31234/osf.io/5hm43

79.

Revelle

(2024). psych: Procedures for Psychological, Psychometric, and Personality Research [software]. Northwestern University. https://CRAN.R-project.org/package=psych

80.

Riegel

K. D.

Ksinan

A. J.

Schlosserova

(2021). Psychometric properties of the independent 36-item PID5BF+M for ICD-11 in the Czech-speaking community sample. Frontiers in Psychiatry, 12, 643270. https://doi.org/10.3389/fpsyt.2021.643270

81.

Roca

Gili

Garcia-Garcia

Salva

Vives

Garcia Campayo

Comas

(2009). Prevalence and comorbidity of common mental disorders in primary care. Journal of Affective Disorders, 119(1), 52–58. https://doi.org/10.1016/j.jad.2009.03.014

82.

Rodriguez-Seijas

Eaton

N. R.

Krueger

R. F.

(2015). How transdiagnostic factors of personality and psychopathology can inform clinical assessment and intervention. Journal of Personality Assessment, 97(5), 425–435. https://doi.org/10.1080/00223891.2015.1055752

83.

Rogers

Hartigan

S. E.

Sanders

C. E.

(2021). Identifying mental disorders in primary care: Diagnostic accuracy of the connected mind fast check (CMFC) electronic screen. Journal of Clinical Psychology in Medical Settings, 28(4), 882–896. https://doi.org/10.1007/s10880-021-09820-1

84.

Rosseel

Jorgensen

D. T.

Rockwood

Oberski

Byrnes

Vanbrabant

Savalei

Merkle

Hallquist

Rhemtulla

Katsikatsou

Barendse

Scharf

(2022). Package “lavaa,”—Latent Variable Analysis [software]. https://cran.r-project.org/web/packages/lavaan/lavaan.pdf

85.

Sauer-Zavala

Fournier

J. C.

Jarvi Steele

Woods

B. K.

Wang

Farchione

T. J.

Barlow

D. H.

(2021). Does the unified protocol really change neuroticism? Results from a randomized trial. Psychological Medicine, 51(14), 2378–2387. https://doi.org/10.1017/s0033291720000975

86.

Sauer-Zavala

Southward

M. W.

Semcho

S. A.

(2022). Integrating and differentiating personality and psychopathology in cognitive behavioral therapy. Journal of Personality, 90(1), 89–102. https://doi.org/10.1111/jopy.12602

87.

Savalei

(2018). On the computation of the RMSEA and CFI from the mean-and-variance corrected test statistic with nonnormal data in SEM. Multivariate Behavioral Research, 53(3), 419–429. https://doi.org/10.1080/00273171.2018.1455142

88.

Savalei

(2021). Improving fit indices in structural equation modeling with categorical data. Multivariate Behavioral Research, 56(3), 390–407. https://doi.org/10.1080/00273171.2020.1717922

89.

Sharma

L. P.

Balachander

Lin

Manohar

Khanna

Garg

Thomas

T. L.

A. C. L.

Selles

R. R.

Højgaard

Skarphedinsson

Stewart

S. E.

(2021). Comorbidities in obsessive-compulsive disorder across the lifespan: A systematic review and meta-analysis. Frontiers in Psychiatry, 12, 703701. https://doi.org/10.3389/fpsyt.2021.703701

90.

Stanton

Rozek

D. C.

Stasik-O’Brien

S. M.

Ellickson-Larew

Watson

(2016). A transdiagnostic approach to examining the incremental predictive power of emotion regulation and basic personality dimensions. Journal of Abnormal Psychology, 125(7), 960–975. https://doi.org/10.1037/abn0000208

91.

Subica

A. M.

Allen

J. G.

Frueh

B. C.

Elhai

J. D.

Fowler

J. C.

(2016). Disentangling depression and anxiety in relation to neuroticism, extraversion, suicide, and self-harm among adult psychiatric inpatients with serious mental illness. The British Journal of Clinical Psychology, 55(4), 349–370. https://doi.org/10.1111/bjc.12098

92.

Talen

M. R.

Baumer

J. G.

Mann

M. M.

(2013). Screening Measures in Integrated Behavioral Health and Primary Care Settings. In Talen

M. R.

BurkeValeras

(Eds.), Integrated Behavioral Health in Primary Care: Evaluating the Evidence, Identifying the Essentials (pp. 239–272). Springer. https://doi.org/10.1007/978-1-4614-6889-9_12

93.

Torres

A. R.

Fontenelle

L. F.

Shavitt

R. G.

Ferrão

Y. A.

do Rosário

M. C.

Storch

E. A.

Miguel

E. C.

(2016). Comorbidity variation in patients with obsessive–compulsive disorder according to symptom dimensions: Results from a large multicentre clinical sample. Journal of Affective Disorders, 190, 508–516. https://doi.org/https://doi.org/10.1016/j.jad.2015.10.051

94.

Tyrer

Reed

G. M.

Crawford

M. J.

(2015). Classification, assessment, prevalence, and effect of personality disorder. Lancet, 385(9969), 717–726. https://doi.org/10.1016/s0140-6736(14)61995-4

95.

Tyrer

Guo

(2016). The general neurotic syndrome: A re-evaluation. Psychotherapy and Psychosomatics, 85(4), 193–197. https://doi.org/10.1159/000444196

96.

Wendt

Kerber

Müller

Zimmermann

(2024). Maladaptive personality trait change during the first eight months of the COVID-19 pandemic in the German general population. Open Science Framework [Preprint]. https://doi.org/https://doi.org/10.31234/osf.io/n8yek

97.

Wickham

Bryan

(2023). readxl: Read Excel Files [software]. https://readxl.tidyverse.org

98.

Wickham

François

Henry

Müller

Vaughan

(2023). dplyr: A grammar of data manipulation [software]. https://dplyr.tidyverse.org

99.

Widiger

T. A.

Oltmanns

J. R.

(2017). Neuroticism is a fundamental domain of personality with enormous public health implications. World Psychiatry, 16(2), 144–145. https://doi.org/10.1002/wps.20411

100.

Widiger

T. A.

Samuel

D. B.

(2005). Diagnostic categories or dimensions? A question for the diagnostic and statistical manual of mental disorders—Fifth edition. Journal of Abnormal Psychology, 114(4), 494–504. https://doi.org/10.1037/0021-843x.114.4.494

101.

Woods

W. C.

Arizmendi

Gates

K. M.

Stepp

S. D.

Pilkonis

P. A.

Wright

A. G. C.

(2020). Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder. Journal of Consulting and Clinical Psychology, 88(3), 240–254. https://doi.org/10.1037/ccp0000472

102.

World Health Organisation. (1993). The ICD-10 classification of mental and behavioural disorders: Diagnostic criteria for research. https://www.who.int/standards/classifications/classification-of-diseases

103.

World Health Organisation. (2008). Integrating mental health into primary care: A global perspective [internet]. https://www.who.int/publications/i/item/9789241563680

104.

World Health Organisation. (2018). Mental health in primary care: Illusion or inclusion? [internet]. https://www.who.int/publications/i/item/WHO-HIS-SDS-2018.38

105.

World Health Organisation. (2019/2021). International classification of diseases, eleventh revision (ICD-11) [internet]. https://icd.who.int/browse11

106.

Wright

A. G.

Calabrese

W. R.

Rudick

M. M.

Yam

W. H.

Zelazny

Williams

T. F.

Rotterman

J. H.

Simms

L. J.

(2015). Stability of the DSM-5 Section III pathological personality traits and their longitudinal associations with psychosocial functioning in personality disordered individuals. Journal of Abnormal Psychology, 124(1), 199–207. https://doi.org/10.1037/abn0000018

107.

Zimmermann

Altenstein

Krieger

Holtforth

M. G.

Pretsch

Alexopoulos

Spitzer

Benecke

Krueger

R. F.

Markon

K. E.

Leising

(2014). The structure and correlates of self-reported DSM-5 maladaptive personality traits: Findings from two German-speaking samples. Journal of Personality Disorders, 28(4), 518–540. https://doi.org/10.1521/pedi_2014_28_130

108.

Zimmermann

Müller

Bach

Hutsebaut

Hummelen

Fischer

(2020). A common metric for self-reported severity of personality disorder. Psychopathology, 53(3–4), 168–178. https://doi.org/10.1159/000507377

109.

Zinchuk

Kustov

Bach

Pashnin

Gersamija

Yakovlev

Voinova

Popova

Guekht

(2023). Evaluation of a 36-item measure of ICD-11 and DSM-5 personality disorder trait domains and facets in Russian inpatients. Psychological Assessment, 35(5), e22–e30. https://doi.org/10.1037/pas0001223

Maladaptive Personality Traits in Primary Care: Validation of the German Modified Brief Personality Inventory for DSM-5 (PID5BF + M)

Abstract

Keywords

Introduction

Mental Health Conditions in Primary Care: Limitations of Categorical Diagnostic Taxonomies

Personality Disorders and the Shift to a Dimensional Approach to Psychopathology

Transdiagnostic Mental Health Screening in PC: The Potential of the PID5BF + M

Research Gap: The Psychometric Properties and Clinical Utility of the PID5BF + M in PC

Methods

Contextual Background on the German Healthcare System

Procedure and Participants

Measures

Modified Personality Inventory for DSM-5 Brief Form

Patient Health Questionnaire-9

Generalized Anxiety Disorder Screener-7

Patient Health Questionnaire-15

Data Analysis

Results

Descriptive Statistics

Factorial Validity and Reliability

Convergent Validity

Association Between Setting and Maladaptive Personality Traits

Discussion

Model fit of the PID5BF + M

Reliability of the PID5BF + M

Association Between Maladaptive Personality Traits and the PC Setting

Convergent Validity of the PID5BF + M

Implications for Assessment and Treatment

Strengths and Limitations

Conclusion

Footnotes

Appendix

Acknowledgements

Author Contributions

Declaration of Conflicting Interests

Funding

Ethical Considerations

Consent to Participate

Consent for Publication

ORCID iDs

Data Availability Statement

References