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To answer our research questions, we employed a qualitative analysis to assess COPMI’s disorder knowledge, and a quantitative analysis to detect differences within their knowledge. Ethical approval was granted by the Ethics Committee in the Department of Psychology of Philipps University Marburg; all participating children and their legal guardians provided written informed consent prior to study participation.

MHL interviews were conducted with COPMI as part of the COMPARE—family study (7, 42). COMPARE—family is a preventive intervention for COPMI with the aim of interrupting the transmission of mental disorders in children of a parent with mental disorders. COMPARE—family was conducted as a multi-centered randomized controlled trial with two treatment arms in which families were assessed at four main measurement points. The inclusion criteria participating families had to meet were as follows: (1) a parent seeks outpatient psychotherapeutic care, (2) the parent currently meets diagnostic criteria for a DSM-5 disorder (41), and (3) the parent is caring for at least one child between the ages of 1.5 and 16 years. The exclusion criteria were as follows: (1) the parent is already in psychotherapeutic treatment, (2) the parent needs acute inpatient treatment (e.g., acute risk of committing suicide or acute psychosis), (3) all children fulfill the criteria for a severe mental illness and need prompt treatment, (4) the parent uses benzodiazepines continuously (intermittent drug use less than once every 2 weeks is allowed), or (5) the family has insufficient German language skills. Data used in this study were collected from the first assessment point (T1), which took place prior to the COMPARE—family treatment. At T1, a sociodemographic questionnaire was administered to assess household characteristics, and a structured clinical interview (DIPS) was conducted with the ill parent to assess mental disorders according to the DSM-5 (41), comorbid disorders, and disorder severity. Mental disorders in COPMI were assessed by administering a structured clinical interview (Kinder-DIPS) with one of the parents. Furthermore, at T1, the MHL interview was conducted face-to-face or via telephone with each participating family member. The interviews were conducted from 2018 to 2022 in the various COMPARE centers and lasted approximately 20 min. For analysis, the interviews were recorded and transcribed. If there were any ambiguities in the transcription, the transcription was checked and supplemented by a second person. Furthermore, randomly selected 5% of the transcribed interviews were cross-checked against the first version. We only analyzed the child version of the MHL interview here.

Data of N = 181 participants aged 5 to 17 years as a subsample of the COMPARE—family study were examined in the present study; detailed characteristics are presented in Table 1 . Participants were classified as “child” when aged 5 to 11 years (n = 130) representing elementary school age. Participants were classified as “adolescent” when aged 12 to 17 years (n = 51) representing middle school and high school age. The children were on average M = 8.22 years old (SD = 1.79), and 66 were female (50.77%). The majority of the interviewed children were already going to school (n = 116, 89.23%), and 21 were diagnosed with a clinically relevant mental disorder at pre-assessment. No data were available from one child regarding the diagnosis of a mental disorder. Most children were interviewed about their parent’s Depressive Disorders or Anxiety Disorders. Two children were excluded from our analysis of specific disorder knowledge as they were interviewed about Depressive Disorders, while their parent’s primary diagnosis fell into the Anxiety Disorders or Somatic Symptom and Related Disorders. The adolescents were on average M = 13.43 years old (SD = 1.27), and 25 were female (49.02%). All were going to school; 11 were diagnosed with a clinically relevant mental disorder. Most adolescents were interviewed about their parent’s Depressive Disorders or Somatic Symptom and Related Disorders.

Characteristics of the ill parent of the participating children and adolescents were taken into account. The characteristics of the other parent were not included in the analyses. Thus, a total of N = 148 parents with a mental illness were included in our sample; detailed characteristics are presented in Table 2 . Twenty-one of the parents participated with two children and six participated with three children. The parents were on average M = 41.13 years old (SD = 6.88) with 110 of them being female (74.32%). Most were employed (83.56 %) and in a relationship (78.77 %); no data were obtained on employment and relationship status regarding two parents. Socioeconomic status was calculated as the family’s Highest International Socio-Economic Index [HISEI; (43)]. The HISEI indicates a family’s highest international socio-economic index (ISEI) value, which generally corresponds to the higher ISEI score of both parents. The HISEI can range from 16 to 90, and a high value corresponds to a high occupational status. The mean HISEI in the sample was M = 53.93 (SD = 17.95); HISEI data from five families were not available. The family’s mean HISEI of the present study slightly exceeds the average HISEI in the German PISA 2018 sample [=51.8, (44)] for comparison. The German PISA 2018 sample included more than 5,000 students, most of whom were 15 years old. Most of the parents’ primary diagnoses fell within the spectrum of Depressive Disorders, followed by Anxiety Disorders. The other diagnoses were distributed among Trauma- and Stressor-Related Disorders, Somatic Symptom and Related Disorders, and other disorders. Regarding disorder severity, we ignored subclinical values (0–3), while values between four and eight covered a moderate-to-severe spectrum. The diagnosis of most parents was estimated at level six or seven in severity; one value was not available. More than half showed clinically comorbid disorders; no data were available from two parents.

We conducted a semi-structured MHL interview to assess disorder knowledge. It is adapted from an interview, which was conducted as part of the psychoeducation intervention “Hope, Meaning, and Continuity” for families in which at least one parent has a depressive disorder (45). The MHL interview contains a total of 12 questions about mental disorders in general, knowledge about the ill parent’s primary diagnosis, causes of the parental mental disorder, dealing with it, and communication about the disorder. For this study’s analyses, we relied on only the first six questions displayed in Table 3 . First, all children were asked about what they knew about mental illness in general, and then disorder-specific knowledge was collected from the children in line with the parent’s primary diagnosis. Children were also asked whether their parents had a mental disorder or other problems with their feelings currently or in the past. However, not every child was asked every interview question due to the correct and incorrect application of skipping rules by the different study center’s interviewer. To specify, the application of correct skipping rules provided for skipping from question 2 to 6, if a child indicated that he or she had no knowledge about the primary diagnosis. If question 6 was negated as well, the interviewer could skip to question 11. Incorrect application of skipping rules led, for example, to the interviewer skipping from question 2 directly to question 11, leaving out question 6.

We analyzed the qualitative content of the transcribed MHL interviews following qualitative research guidelines (46) using the software MAXQDA (47). First, LK and MS determined the aim of the category construction on the basis of the research questions regarding which general and specific disorder knowledge COPMI have. Furthermore, the type of categories and level of abstraction were determined with a mixed form of content structuring and evaluative qualitative analysis with a high level of abstraction. In the next step, LK screened half of the interviews to become familiar with the material. The size of the segments to be coded was determined as partial sentences, sentences, or short paragraphs, and then the sequential analysis was implemented via a per-interview approach. Initially, LK defined seven higher-order categories relying on the interview structure. A randomly selected sample of 35% of the interviews was then coded in vivo by LK until the category system was saturated. During the subsequent systematization and organization of these inductively developed categories, we discovered a strong similarity with the DSM-5 disorder criteria (41). Following consultation with MS and HC, the inductively developed categories were substituted by deductive categories. These comprise the disorder-specific definitions and diagnostic criteria as described in the DSM-5 (41) and are matched to the initial inductive categories. In addition to these DSM-5-oriented deductive categories, three additional categories (no knowledge, correct conceptions, incorrect conceptions) were added to prevent loss of information. The developed category system was then applied by LK to 50% of the material to test its applicability to the data set. LK then developed a coding book and analyzed the whole set of interviews. To verify the quality of the coding system and determine interrater reliability, KP additionally analyzed 25% of the interviews independently using the coding book. KP was blinded for the codes that were assigned to the material by LK. Interrater reliability was estimated using Cohen’s kappa (κ) within the corresponding function in MAXQDA.

Chi-square tests of independence were applied to identify associations between COPMI’s age and sex with general and specific disorder knowledge. Associations were tested at a significance level α of 5%. Odds ratios (OR) were calculated as effect sizes. To better align with the assumption of chi-square tests, which stipulates that expected frequencies should exceed 5 in at least 80% of the cells (48), we combined the two primary categories of “no knowledge” and “incorrect conceptions” given the limited number of participants displaying exclusively incorrect conceptualizations. Three levels of knowledge groups resulted in our analyzing in terms of age and sex: “no knowledge,” “good conceptualization,” and “good disorder knowledge.” The group “no knowledge” included all COPMI who displayed either no knowledge or only incorrect conceptions. The group “good conceptualization” included all COPMI who displayed either only correct conceptions or a mixture of correct conceptions and incorrect ones. The group “good disorder knowledge” included all COPMI displaying disorder knowledge that was also in line with DSM-5 descriptions (41) regardless of any additional display of incorrect or correct conceptualizations. This was implemented for both general and specific disorder knowledge. For general disorder knowledge, Pearson’s chi-square values are reported, as the assumptions of chi-square tests were met. For specific disorder knowledge, maximum likelihood ratio chi-square values are reported, as the described assumption of chi-square tests was not met.

An overview of the categorical system is displayed in Table 4 . The system consists of three category levels: higher-order categories, main categories and subcategories. The developed higher-order categories include the COPMI’s knowledge about mental disorders in general (category 1), whether the interviewee knew that his or her parent was ill (category 2), specific knowledge about the parent’s disorder (categories 3–6), and specific knowledge about additionally required disorder criteria (category 7). Four main categories were developed for the higher-order categories of general and specific disorder knowledge (categories 1 and 3–6): no knowledge (x.1), incorrect conceptions (x.2), correct conceptions (x.3), and general/specific disorder knowledge (x.4).

The main categories no knowledge (x.1), incorrect conceptions (x.2), and correct conceptions (x.3) have no further subcategories. COPMI who did not answer or stated that they lacked knowledge were assigned to the no knowledge category. Inappropriate or incorrect mentions were assigned to the category of incorrect conceptions. Appropriate, correct, and exemplary descriptions exceeding any DSM-5-descriptions were assigned to the category of correct conceptions. For general/specific disorder knowledge, subcategories were developed. For general disorder knowledge (1.4), these subcategories (1.4.1 to 1.4.6) are in line with the DSM’s definition of a mental disorder (41). For specific disorder knowledge (3.4; 4.4; 5.4; 6.4), subcategories (3.4.1–3.4.9; 4.4.1–4.4.6; 5.4.1–5.4.7; 6.4.1–6.4.5) are in line with the specific DSM-5 disorder definitions and main diagnostic criteria (41). Three additionally required disorder criteria (category 7) were assessed together across all the specific disorders surveyed, namely, the time criterion, the criterion of distress or impairment, and the exclusion criterion of differential diagnoses. These represent the diagnostic criteria listed at the end of each disorder in the DSM-5 (41). The interrater reliability across all ratings was moderate according to McHugh (49) with κ = 0.67.

The majority of interviewed children (N = 77, 59.23%) showed no knowledge of mental illness in general, as can be seen in Figure 1 . Mentions from 10 children (7.69%) were assigned to the category of incorrect conceptions, for instance, describing mental disorders as something “where one has no real illness, but rather that it’s the hormones” (10-year-old female, C9) or “if you can’t speak German properly” (11-year-old male, C10). Incorrect conceptions also included misconceptions about mentally ill people (“that’s those in wheelchairs or so”; 8-year-old male, C11). Sixteen children (12.31%) revealed correct conceptions of the construct of mental disorders providing example diagnoses. Regarding general disorder knowledge, the following three aspects were mentioned most frequently: 21 children (16.15%) associated mental illness with psychobiological processes, for example, as “something in the brain, like a faulty circuit in the brain” (11-year-old female, C1) or as “some change in the genes” (9-year-old male, C2). Eighteen (13.85%) could describe it as dysfunction or illness, for example, as “something like a disease” (8-year-old male, C3) or “that you have some problems and can't handle them like the other people around you” (11-year-old female, C4). Sixteen children (12.31%) described affected psychological functioning with most of them referring to affected emotions like “when you are very sad” (9-year-old male, C5) or “that someone is angry” (8-year-old male, C6). Beyond that, mentions from three children (2.31%) were assigned to the category “deviation from the norm” and two described associated distress or impairment (“if you can't do it like someone who is perhaps completely healthy”; 11-year-old female, C7). We assigned one child’s description of mental disorders as a syndrome (“Depression, for example, can be many things: grief, anger, anxiety, nausea, dizziness”; 8-year-old female, C8).

Among the adolescents, five (9.80%) displayed no knowledge. The mentions of two adolescents (3.92%) were assigned to the category of incorrect conceptions. They described that mental disorders were “not really an illness” (14-year-old male, A5). Almost half of all adolescents (N = 22, 43.14%) revealed correct conceptions. These comprised example diagnoses and adequate descriptions (“that you have an altered impression on your environment”; 13-year-old female, A9). Meanwhile, regarding general disorder knowledge, most adolescents associated mental disorders with one of the following three aspects: 21 (41.18%) described it as dysfunction or illness. Twenty adolescents (39.22%) associated mental disorders with psychobiological processes, like “a change of character [ … ] in the brain” (14-year-old male, A1), “if you have problems that are psychological in nature” (13-year-old female, A2) or “where the head tends to go a bit crazy from time to time” (12-year-old male, A3). The same number of adolescents (39.22%) described affected psychological functioning as follows: emotions were most frequently described as “when you don’t feel so well” (12-year-old female, A4), and behavior was referred to as being unusual, “peculiar” (14-year-old male, A1) or altered. Mentions of 11 adolescents (21.57%) were assigned to the category “deviation from the norm”, for example “if a person’s behavior deviates from the ‘norm’” (14-year-old male, A5) or “this is when you perhaps behave differently than, let's say, normal. There is no such thing as normal, but there is such a thing as not behaving in a way that would be good for other people either” (15-year-old female, A6). Associated distress or impairment was mentioned by seven adolescents (13.73%) in the form of “problems [ … ] that can affect your health” (13-year-old female, A7) or “so that you can no longer live properly” (14-year-old male A8). Two adolescents (3.92%) described mental disorders as a syndrome.

Of all COPMI, n = 118 children were asked whether their parent was ill. Almost 67% did not know that their parent had a mental disorder (n = 79, 66.95%), while one quarter of the children suspected some form of disorder or knew about it (n = 30, 25.42%), like this 7-year-old boy (C12): “Yes, I have a feeling about it. I just think there’s something not right with my parents”. For n = 9 children, it was unclear whether they knew, suspected, or did not know about the parental mental disorder due to the child’s answer’s unclear wording or due to biased wording of the question (“And do you think, for example, that your parents were ever really sad?”; interviewer I1). The remaining n = 63 children in the sample were not asked the corresponding question.

In sum, n = 50 adolescents were interviewed on whether their parent was ill. While n = 23 (46%) of them did not know about their parent’s disorder, n = 27 (54%) suspected or knew about some form of mental disorder. One adolescent was not asked the corresponding question.

Separate chi-square tests for independence revealed significant associations between COPMI’s age and sex with general and specific disorder knowledge. First, age was significantly associated with general disorder knowledge, χ² (2) = 41.84, p < 0.001. The odds of displaying general disorder knowledge versus no knowledge were 15.26 times higher among adolescents than children. Similarly, the odds of revealing good conceptions rather than no knowledge were 19.68 times higher among adolescents than children. Second, age proved to be significantly associated with specific disorder knowledge, Lχ² (2) = 22.90, p < 0.001. The odds of showing specific disorder knowledge rather than none were 5.74 times higher among adolescents than children. Similarly, the odds of exhibiting specific disorder knowledge rather than a good conceptualization were 7.77 times higher in adolescents than children. Third, COPMI’s sex was significantly associated with general disorder knowledge, χ² (2) = 7.05, p < 0.05. The odds of showing general disorder knowledge versus no knowledge were 1.94 times higher in males than in females. Similarly, the odds of displaying good conceptions rather than no knowledge were 3.96 times higher in males than in females. COPMI’s sex was not significantly associated with specific disorder knowledge, Lχ² (2) = 0.44, p = 0.819.

One of the major strengths of our study is our sample’s heterogeneity supporting the transferability of our findings. This heterogeneity is characterized by the multi-centered approach, the broad age range of included COPMI, consideration of different parental diagnoses, comorbidities and degrees of disorder severity, as well as different sex dyads between COPMI and affected parent. Nonetheless, the extent of transferability of our sample’s findings to COPMI in general may be limited. As the COMPARE—family study required parents to be willing to undergo psychotherapy and regularly attend study appointments, our findings could be biased by an overrepresented group of COPMI with highly motivated parents. Different findings might have yielded for COPMI outside the care system. However, as our sample’s COPMI displayed a lack of knowledge of mental disorders, we can assume that COPMI outside the care system would have even lower levels of knowledge. It is also worth noting that all types of mental disorders were considered in the COMPARE—family study, but not all COPMI could be accounted for in our analysis of specific disorder knowledge, as the disorders were considered on a group level. The conclusiveness of our results can therefore not be generalized to all types of mental disorders, and the disorder groups underrepresented in this study should be investigated further. Nevertheless, the large sample size is one of the strengths of our study, which enabled a comprehensive comparison of disorder knowledge.

Another study limitation is the moderate interrater reliability of the code system we developed. To use the deductive approach more broadly, a higher level of agreement between coders should be aimed at. This can be achieved by revising the codebook and a second coder’s training. Meanwhile, a major strength of this study lies in our deductive approach, which enabled us to capture the presence and extent of disorder knowledge. Previous studies on knowledge in MHL have utilized vignettes (8, 37), inductive qualitative content analysis (27, 31), questionnaires, and mixed methods (18, 36). However, some of these methods have limitations. For instance, symptom recognition in vignettes does not equate to knowledge and is viewed critically (53, 54). Furthermore, MHL questionnaires are still being developed (18) and do not include the specific parental diagnosis. Moreover, assessing correct knowledge and comparing results within the inductive approach remains problematic because of different methodologies (24). To address some of the aforementioned methodological limitations, we took a novel qualitative approach in this study. By applying the DSM-oriented deductive qualitative content analysis, knowledge levels could be determined congruently in line with different forms of knowledge (general and specific) and among different samples (children and adolescents; various disorder groups). Findings generated in this way have the potential to indicate which variables should be targeted in MHL interventions aiming to strengthen COPMI’s disorder knowledge.

Practical implications of the present study include the consideration of our findings in MHL interventions for COPMI. For example, we demonstrated that males displayed more general knowledge about disorders and that they might lack the skills to apply that knowledge. We also showed that the two age groups had different levels of specific knowledge regarding the different specific disorders. Drawing from our findings, we recognize a great need for psychoeducation among younger children whose parents suffer from depressive disorders. The lack of knowledge was most prevalent in this subgroup, while at the same time, depression is one of the most common mental disorders affecting a large number of families. Since targeting knowledge as an intervention may have limited effects on actual behavioral change (55), it might be beneficial to consider integrating psychoeducational aspects of MHL with mental health action components as, for example, proposed by Marinucci et al. (56).

Research implications from the present work arise from pending research on the relationship between COPMI’s disorder knowledge and coping strategies as well as the development of child psychopathology in the transmission model. Other research implications include the analysis of associations between the child’s, their ill parent’s, and their healthy parent’s disorder knowledge. We, therefore, suggest that the DSM-oriented deductive qualitative content analysis approach be retained, optimized, and applied to other samples and additional measurement time points within the COMPARE—family study and beyond.