Exploring factors associated with psychiatric hospitalization for persons living with family.

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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Feb 19;16:9949. doi: 10.1038/s41598-026-39394-7 Search in PMC Search in PubMed View in NLM Catalog Add to search Exploring factors associated with psychiatric hospitalization for persons living with family Odysseas Anastasopoulos Odysseas Anastasopoulos 1 School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece Find articles by Odysseas Anastasopoulos 1 , Eugenie Georgaca Eugenie Georgaca 1 School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece 2 Association for Regional Development and Mental Health (EPAPSY), Athens, Greece Find articles by Eugenie Georgaca 1, 2, ✉ , Julie Vaiopoulou Julie Vaiopoulou 3 Department of Education, University of Nicosia, Nicosia, Cyprus 4 School of Humanities, Hellenic Open University, Patras, Greece Find articles by Julie Vaiopoulou 3, 4 , Anastasia Zissi Anastasia Zissi 5 Department of Sociology, University of the Aegean, Lesvos, Greece Find articles by Anastasia Zissi 5 , Lily Peppou Lily Peppou 2 Association for Regional Development and Mental Health (EPAPSY), Athens, Greece 6 Department of Psychology, Panteion University of Social Sciences, Athens, Greece Find articles by Lily Peppou 2, 6 , Aikaterini Arvaniti Aikaterini Arvaniti 7 Department of Psychiatry, Democritus University of Thrace, Alexandroupoli, Greece Find articles by Aikaterini Arvaniti 7 , Maria Samakouri Maria Samakouri 7 Department of Psychiatry, Democritus University of Thrace, Alexandroupoli, Greece Find articles by Maria Samakouri 7 , Stelios Stylianidis Stelios Stylianidis 2 Association for Regional Development and Mental Health (EPAPSY), Athens, Greece 6 Department of Psychology, Panteion University of Social Sciences, Athens, Greece Find articles by Stelios Stylianidis 2, 6 ; Thessaloniki Mane Group Author information Article notes Copyright and License information 1 School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece 2 Association for Regional Development and Mental Health (EPAPSY), Athens, Greece 3 Department of Education, University of Nicosia, Nicosia, Cyprus 4 School of Humanities, Hellenic Open University, Patras, Greece 5 Department of Sociology, University of the Aegean, Lesvos, Greece 6 Department of Psychology, Panteion University of Social Sciences, Athens, Greece 7 Department of Psychiatry, Democritus University of Thrace, Alexandroupoli, Greece 8 2nd Department of Psychiatry, School of Medicine, Aristotle University of Thessaloniki, Psychiatric Hospital of Thessaloniki, Thessaloniki, Greece 9 1st Department of Psychiatry, School of Medicine, Aristotle University of Thessaloniki, General Hospital “Papageorgiou”, Thessaloniki, Greece 10 C Acute Ward, Psychiatric Hospital of Thessaloniki, Thessaloniki, Greece 11 D Acute Ward, Psychiatric Hospital of Thessaloniki, Thessaloniki, Greece 12 3rd Department of Psychiatry, School of Medicine, Department of Mental Health, AHEPA University General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece 13 B Acute Ward, Psychiatric Hospital of Thessaloniki Consortium, Thessaloniki, Greece 14 Psychiatric Department, G. Papanikolaou General Hospital, Thessaloniki, Greece ✉ Corresponding author. Received 2025 Apr 27; Accepted 2026 Feb 4; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13022279  PMID: 41714682 Abstract This study investigates the combinations of factors associated with involuntary psychiatric admission for persons living with their family and whether these are differentiated by type of family. Data from consecutive admissions in public acute psychiatric units of Thessaloniki, Greece, collected over one-year period were analyzed via Latent Profile Analysis. Of the three ensued profiles, only one, consisting mainly of younger men diagnosed with schizophrenia-spectrum disorders living with their family of origin, is linked to involuntary hospitalization. The other two profiles include mainly older people with recent onset of depressive disorder living with their created family and women living in socially deprived families, respectively. Severity and duration of psychopathology, inadequate contact with mental health services and socioeconomic adversity seem to constitute risk factors that might lead people living with their families to psychiatric hospitalization, when in crisis, despite the protective role that the family-related perceived social support and life satisfaction might play. Supporting families to manage their suffering member’s mental state through collaboration with appropriate mental health services, as well as empowering disadvantaged families to deal with the adverse effects of socioeconomic deprivation appear to be the main strategies to prevent psychiatric hospitalization, especially involuntary. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-39394-7. Keywords: Living with family, Family type, Psychiatric hospitalization, Involuntary/compulsory admission, Latent profile analysis Subject terms: Psychology, Health care, Public health, Quality of life, Risk factors Introduction Compulsory psychiatric admission is a controversial practice that raises ethical issues as well as issues of clinical effectiveness. The rates of involuntary admissions in Greece 1 are much higher than the European average 2 ; reducing compulsory admissions is a stated target of the National Mental Health Plan 2021–2030. This necessitates understanding the factors and mechanisms responsible for involuntary admissions. This paper is part of the multi-center research project “Study of Involuntary Hospitalizations in Greece (MANE)” that examined involuntary admissions in the public mental health care system in Greece. Here we focus on the role of living arrangements and family relations as risk factors for involuntary psychiatric admission. Our previous research 3 showed that living alone was a strong predictor of involuntary psychiatric admission, while living with others was not significantly associated with type of psychiatric hospitalization. It was hypothesized that, while living with others is associated with higher social support and stronger social networks, for certain individuals, when in deteriorating mental state, this might not buffer involuntary hospitalization, and might even contribute to it. Also, living with others is not a unified condition; it might differ considerably depending who these others are. This suggests a need for further investigation of the role of others in the living environment and its relation to psychiatric admission. Given that most persons with severe mental illness live with their families, the effect of living with others on compulsory admission is underpinned by the role of families 4 . In Greece, the family is the main social support and protection institution, operating as the main carer for persons with severe mental health problems 5 . Moreover, as dictated by the relevant legislation, the vast majority of involuntary hospitalizations in Greece are initiated by family members 6 . This makes understanding the family-related factors that are associated with involuntary admission an issue of utmost importance. Caring for a person with severe mental illness affects family dynamics, that may contribute to relapse and to caregivers resorting to involuntary admission 7 – 10 . Studies of families with a member with a severe mental disorder indicate that family functioning is related to several socio-demographic factors, such as age, education, migration status, place of residence, and employment; illness-related factors, such as illness onset, symptomatology, diagnosis and general functioning; and treatment-related factors, namely number, timing and duration of hospitalizations 11 . Symptom severity, global functioning and family functioning are related, in turn, to subjective quality of life and life satisfaction of persons with severe mental illness 12 , 13 . Generally, living with others, including living with one’s family, entails higher levels of social support and stronger social networks than living alone 14 . For persons with severe mental disorders, family support and the immediate social network seem to be associated with adherence to treatment regime and medication, thus mitigating vulnerability to involuntary admission 15 . On the other hand, living with one’s family and receiving social support has been found to statistically increase the risk of compulsory admission, when in acute phase 16 . Rationale of the current study Most of the studies mentioned above examine associations between specific aspects of family environment and the psychopathology of their suffering members or the type of psychiatric admission. Given the complexity of family situations, this work aims to investigate the combinations of family-related factors that may be associated with involuntary hospitalization and whether these are differentiated according to the type of family that the hospitalized person lives with, i.e., family of origin vs. the family they have created. The inconclusiveness of previous research findings does not allow research hypotheses to be formulated, thus the exploratory character of the study. The only hypothesis, in line with previous research findings 15 , is that patients living with their created family will present better social and clinical indicators and will be admitted involuntarily less often compared to those living with their family of origin. Method Setting Data for this retrospective cross-sectional study derived from the multisite research project “Study of Involuntary Psychiatric Hospitalizations in Greece (MANE)”. The current study used data collected from the Thessaloniki site of the MANE project, that was conducted under the auspices of the Schools of Psychology and Medicine of the Aristotle University of Thessaloniki (AUTh) and included all 8 public acute psychiatric units that operate in the metropolitan area of Thessaloniki. The process of involuntary admission in Greece is described in supplementary material. Sample The sample of the present study is a subset of the Thessaloniki MANE sample. The Thessaloniki MANE sample comprises of all consecutive admissions in the participating psychiatric units from March 2018 to February 2019. As this was a naturalistic study, all admission cases were considered eligible for inclusion, provided they gave written consent to participate. For the purposes of this particular study, we eliminated from the Thessaloniki MANE sample (N = 1003) entries from 2 psychiatric units (N = 211), that had originally opted out from a part of the MANE project that contained data utilized in this particular study. Also, as this research focused on patients who were living with their family, we excluded individuals living alone (Ν = 214), with non-relatives (N = 29), in supported accommodation (Ν = 13) and those who were homeless (Ν = 22). Thus, the sample utilized here comprised of 514 cases. Procedure and measures The MANE project was carried out in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments, and was approved by the Scientific Boards of all participating units, the Regional Health Authority (25,357/15–12-2017) and the Research Ethics Committee of the Psychology School of AUTh (016/24–11-2017). Data were collected within the first ten days of admission. Hospitalized persons who were considered by the unit treating clinicians to have capacity for consent and to be at an appropriate mental state for participation were briefed about the study, provided with a written information sheet and they signed the required informed consent. All procedures were in accordance with GDPR and the instructions of the research ethics committees. Data were retrieved from administrative, medical and clinical records, as well as obtained via a brief interview with patients, performed by trained researchers under the supervision of the unit staff and the project research team. The data utilized in this study is a subset of the data collected for MANE, consisting of variables that in previous research were found to be related to the type of psychiatric hospitalization. Both objective (a-g) and subjective (h–k) variables were included in this study. Specifically: a) Admission status, b) Living arrangements, c) Demographics on sex/gender, age group, education level and financial status, d) Clinical diagnosis (ICD-10), e) Severity of symptoms (Health of Nations and Outcomes Scale), f) General functioning (Global Assessment of Functioning), g) Illness- or treatment-related data on previous admissions and previous involuntary admissions, duration of disorder, adherence to medication and type of contact with mental health services, h) Social support (Oslo Social Support Scale), i) Social networks (European Social Survey), j) Quality of Life (Manchester Short Assessment of Quality of Life): Factor 1: Satisfaction with life and health related aspects & Factor 2: Satisfaction with quality of living environment 17 , k) Feelings of loneliness (European Social Survey). Details of the variables and their measurement are available as supplementary material. Analysis Latent Profile Analysis (LPA), which is a version of Latent Class Analysis (LCA), was utilized. LPA considers the latent variable as categorical and the observables at any level of measurement 18 , 19 , and creates typologies based on similar patterns of responses. The classification of cases is achieved via Bayesian statistics, using conditional probabilities, that is, the probabilities of observing a response pattern, given that a person belongs to a certain cluster/profile 19 . This person-centered approach has certain advantages over the traditional variable-centered analyses, because it is not constrained by statistical presuppositions, such as normality and linearity. LPA and LCA are valued tools for creating typologies, classifications and theory building within socio-psychological contexts 3 , 20 – 22 . The analytic technique, as in any psychometric method, provides several solutions, from which the best parsimonious model is chosen, based on suitable fit criteria. A stepwise LPA procedure was employed, to associate the resulting latent profiles with covariates and distal outcomes 23 , 24 . At Stage 1, selected variables were used as input for extracting distinct profiles. The findings of our previous study 3 indicate that involuntary hospitalization is linked to a combination of poor social and clinical indicators, specifically by deterioration of symptomatology and functioning in the context of low social support and weak social networks. Moreover, we found that living with others, compared to living alone, is characterized by better, yet variable, social indicators and is not associated with any type of psychiatric hospitalization. The literature points to the protective role of social support and social networks for involuntary hospitalization 6 , 14 , 15 and to associations of family dynamics and social support with perceived quality of life in persons with severe mental disorders 7 , 25 , 26 . Assuming that for persons living with their family the risk of psychiatric hospitalization, and especially involuntary, might be mediated by the social support that they receive from their family, participation in social networks and their subjective quality of life, we posited these as the central factors for the construction of profiles. We also included sociodemographic indicators that have been found to affect family functioning 11 . We, thus, set to examine whether specific combinations of social and demographic factors are linked to particular family arrangements and in turn to particular type of psychiatric admission. After statistical controls, four social indicators (Social Support, Social Networks, Quality of Life/Factor 1, Quality of Life/Factor 2) and three sociodemographic variables (Age, Gender, Educational level) were included in the measurement model (see Fig. 1 ). The structural model included the ensued profiles and two more variables, living arrangement as covariate (independent variable) and admission status as distal outcome (dependent variable). At Stage 2, further analyses were carried out, testing the association of the ensued profiles with several external covariates: one socio-demographic (financial status), three clinical (clinical diagnosis, symptom severity, global functioning), five illness- and treatment-related (duration of disorder, number of previous admissions, number of previous involuntary admissions, adherence to medication, type of contact with mental health services) and one social variable (feelings of loneliness). The variables “Age at onset of disorder” and “Type of previous treatment” were excluded from the model due to statistical insignificance during preliminary analysis. LatentGOLD6.0 was the software used. Fig. 1. Open in a new tab The LPA with the measurement and the structural model. Results Descriptive statistics Descriptive statistics for all sociodemographic and clinical variables considered are presented in Table 1 . Table 1. Descriptive Statistics of socio-demographic and clinical characteristics of the study sample. Admission status Involuntary Voluntary Total N = 282 N = 232 N = 514 54,90% 45,10% 100% M [SD]/N (%) 1 M [SD]/N (%) M [SD]/N (%) Living arrangements With family of origin 200 (70,9) 124 (53,4) 324 (63) With created family 82 (29,1) 108 (46,6) 190 (37) Sex Male 178 (63,1) 112 (48,3) 290 (56,4) Female 104 (36,9) 120 (51,7) 224 (43,6) Age 44,09 [± 14,70] 45,10 [± 13,72] 44,54 [± 14,26] 18–25 25 (8,9) 20 (8,6) 45 (8,8) 26–39 97 (34,4) 61 (26,3) 158 (30,7) 40–59 120 (42,6) 110 (47,4) 230 (44,7) ≥ 60 40 (14,2) 41 (17,7) 81 (15,8) Education Level Primary 51 (19,5) 46 (20,9) 97 (20,2) Secondary 135 (51,7) 115 (52,3) 25 (52) Further & Higher 75 (28,7) 59 (26,8) 134 (27,9) Financial Status Poverty level income 125 (60,1) 99 (55,9) 224 (58,2) Low income 35 (16,8) 44 (24,9) 79 (20,5) Average income 37 (17,8) 29 (16,4) 66 (17,1) High income 11 (5,3) 5 (2,8) 16 (4,2) Clinical Diagnosis 2 F20-F29 140 (49,6) 84 (36,2) 224 (43,6) F30-F31 38 (13,5) 45 (19,4) 83 (16,1) F32-F33 10 (3,5) 38 (16,4) 48 (9,3) Other 94 (33,3) 65 (28) 150 (30,9) GAF Pervasive impairment 222 (78,7) 150 (64,7) 372 (72,4) Serious impairment 26 (9,2) 42 (18,1) 68 (13,2) Moderate impairment 23 (8,1) 29 (12,5) 52 (10,1) No information available 11 (4) 11 (4,7) 22 (4,3) Previous admissions No previous admissions 91 (32,3) 63 (27,2) 154 (30) 1 47 (16,7) 29 (12,5) 76 (14,8) 2–3 57 (20,2) 58 (25) 115 (22,4) Above 3 57 (20,2) 55 (23,7) 112 (21,8) No information available 30 (10,6) 27 (11,6) 57 (11,1) Previous involuntary admissions No previous inv. admissions 100 (35,5) 131 (56,5) 231 (44,9) 1 55 (19,5) 26 (11,2) 81 (15,8) 2–3 43 (15,2) 16 (6,9) 59 (11,5) Above 3 29 (10,3) 11 (4,7) 40 (7,8) No information available 55 (19,5) 48 (20,73) 103 (20) Duration of disorder (years) < 1 73 (25,9) 32 (13,8) 105 (20,4) 1–2 26 (9,2) 25 (10,8) 51 (9,9) 3–10 67 (23,8) 56 (24,1) 123 (23,9) 11–20 40 (14,2) 54 (23,3) 94 (18,3) > 20 64 (22,7) 50 (21,5) 114 (22,2) No information available 12 (4,2) 15 (6,5) 27 (5,3) Adherence to medication Poor 102 (36,2) 52 (22,5) 154 (30) Medium 25 (8,9) 27 (11,6) 52 (10,1) Good 52 (18,4) 113 (48,7) 165 (32,1) No medication prescribed 74 (26,2) 27 (11,6) 101 (19,6) No information available 29 (10,3) 13 (5,6) 42 (8,2) Type of contact with mental health services (previous year) Hospitalization 46 (16,3) 33 (14,2) 79 (15,4) Outpatient unit 23 (8,2) 36 (15,5) 59 (11,5) Community services 8 (2,8) 3 (1,3) 11 (2,1) Private practitioner 47 (16,6) 53 (22,8) 100 (19,5) None 137 (48,6) 70 (30,2) 207 (40,2) Other 1 (0,4) 2 (0,9) 3 (0,6) Combined 17 (6,0) 30 (12,9) 47 (9,1) No information available 3 (1,1) 5 (2,2) 8 (1,6) Social Support Poor 109 (38,6) 80 (34,5) 189 (33,7) Moderate 93 (33) 94 (40,5) 187 (36,3) Strong 80 (28,4) 58 (25) 138 (26,8) Social networks Never 79 (28) 74 (31,9) 153 (29,8) Rarely 36 (12,8) 34 (14,6) 70 (13,6) Monthly 49 (17,4) 42 (18,1) 91 (17,7) Weekly 76 (26,9) 57 (24,6) 133 (25,5) Daily 42 (14,9) 25 (10,8) 67 (13) Quality of life 3 Factor 1 (MANSA) Couldn’t be worse 1 (0,4) 0 (0) 1 (0,2) Displeased 12 (4,2) 22 (9,5) 34 (6,6) Mostly dissatisfied 24 (8,5) 55 (23,7) 79 (15,4) Mixed 91 (32,3) 64 (27,6) 155 (30,2) Mostly satisfied 95 (33,7) 60 (25,9) 155 (30,2) Pleased 45 (15,9) 21 (9) 66 (12,8) Couldn’t be better 13 (4,6) 1 (0,4) 14 (2,7) No information available 1 (0,4) 9 (3,9) 10 (1,9) Factor 2 (MANSA) Couldn’t be worse 3 (0,6) 3 (1,3) 6 (1,1) Displeased 8 (1,6) 5 (2,2) 13 (2,6) Mostly dissatisfied 28 (5,5) 21 (9) 49 (9,5) Mixed 54 (10,7) 25 (10,8) 79 (15,3) Mostly satisfied 74 (14,6) 74 (31,9) 148 (28,8) Pleased 87 (17,2) 63 (27,2) 150 (29,2) Couldn’t be better 28 (5,5) 33 (14,2) 61 (11,9) No information available 0 (0) 8 (3,4) 8 (1,6) Feelings of loneliness None 89 (31,6) 44 (19) 133 (25,9) Slightly 54 (19,2) 32 (13,8) 86 (16,7) Moderately 42 (14,9) 48 (20,7) 90 (17,5) Very much 50 (17,7) 53 (22,8) 103 (20) Extremely 28 (9.9) 34 (14,7) 62 (12,1) No information available 19 (6,7) 21 (9) 40 (7,8) Open in a new tab Note 1: For the continuous variables, Mean and Standard Deviation [in brackets] are denoted. For the categorical and ordinal variables, the Frequency and Percentages (in parentheses) are presented. Note 2: F20-F29: Schizophrenia, Schizotypal disorder, Delusional disorders. F30-F31: Bipolar disorder. F32-F33: Depressive disorder, Other: F00-F09 (Organic mental disorders), F40-F48 (Anxiety disorders), F84 (Pervasive developmental disorders). Note 3: Factor1: Satisfaction with life and health-related aspects (MANSA items:10. Life as a whole, 11. Job/unemployment, 12. Financial situation, 15. Friendships, 16. Leisure activities, 22. Sex life, 24. Physical health, 25. Mental health)/ Factor 2: Satisfaction with quality of living environment (MANSA items: 17. Accommodation, 20. Personal safety, 21. Living situation, 23. Family). Latent profile analysis The classification procedure based on the seven input variables provided six cluster-solutions (Table 2 ), from which we chose the three-cluster model as the best fit, based on the lowest BIC value and parsimonious model. Table 3 shows the three profiles and their association with the seven indicators by depicting the relative category ratios for nominal variables and the mean values (in Z-scores) for interval variables. Table 2. LPA results. LL BIC(LL) Npar L 2 df p -value Class.Err 1-Cluster − 6294.995 13,149.888 91 7419.24 379 1 0 2-Cluster − 6180.083 12,993.898 103 7189.417 367 0.00 0.1071 3-Cluster − 6116.409 12,940.381 115 7062.067 355 0.10 0.1336 4-Cluster − 6084.213 12,949.823 127 6997.676 343 0.11 0.1628 5-Cluster − 6066.084 12,987.397 139 6961.418 331 0.09 0.1758 6-Cluster − 6047.148 13,023.359 151 6923.547 319 0.12 0.1808 Open in a new tab Table 3. The statistical significance of each indicator with the ensued Cluster/Profile memberships. Models for Indicators Profile 1 (45.73%) Profile 2 (28.15%) Profile 3 (26.13%) Gender Male 0.327*** − 0.1102 − 0.217* Female − 0.327*** 0.1102 0.217* Age Groups 18–25 1.3384 − 1.7182 0.3798 26–39 0.5868 − 0.5425 − 0.0443 40–59 − 0.3888 0.5295 − 0.1407 > 60 − 1.536*** 1.731** − 0.1948 Educational Level Primary − 0.403*** 0.286* 0.1162 Secondary 0.272** − 0.1675 − 0.1043 Further & higher 0.131 − 0.1189 − 0.0119 Social Support (Total) 0.350*** 0.617*** − 0.967*** Social Networks (Q1) 0.617*** 0.196 − 0.814*** Quality of Life/Factor 1 (MANSA) a 0.559*** 0.614*** − 1.173*** Quality of Life/Factor 2 (MANSA) b 0.1589 1.139*** − 1.299*** Open in a new tab Note: * p < 0.05, ** p < 0.01, *** p < 0.001. 1: s.e. = standard error. Profile 1 (45.73%) most likely includes males aged less than 60 with secondary education and is positively associated with Social Support, Social Networks and QoL/Factor1. Profile 2 (28.15%) most likely includes individuals aged above 60, with primary education, positively associated with Social Support, QoL/Factor1 and QoL/Factor2. Profile 3 (26.13%) most likely includes females, regardless of age and educational level, and is negatively associated with Social Support, Social Networks, QoL/Factor1 and QoL/Factor2. The associations of the three Profiles with type of family as covariate and admission status as distal outcome are presented in Table 4 , and in schematic form in Fig. 2 . Table 4. The associations of the Profiles with living arrangement as covariate and admission status as distal outcome. Living Arrangement (as Covariate) Admission Status (as Distal outcome) With family of origin With created family Involuntarily Voluntarily Profile 1 1.121*** − 1.121*** 0.314*** − 0.314*** s.e 1 0.282 0.282 0.084 0.084 z-value 3.972 − 3.972 3.74 − 3.74 Profile 2 − 1.134*** 1.134*** − 0.132 0.132 s.e 0.213 0.213 0.086 0.086 z-value − 5.313 5.313 − 1.534 1.534 Profile 3 0.013 − 0.013 − 0.183* 0.183* s.e 0.197 0.197 0.092 0.0915 z-value 0.065 − 0.065 − 1.998 1.998 Wald 28.279 13.981 p -value < 0.001 < 0.001 Open in a new tab * p < 0.05, ** p < 0.01, *** p < 0.001. 1: s.e. = standard error. Fig. 2. Open in a new tab Schematic representations of the three-Latent Profile Model with living arrangement as covariate and admission status as distal outcome. The results indicate that most likely members of Profile 1 live with their family of origin, members of Profile 2 with their created family, while members of Profile 3 are not associated with family type. Members of Profile 1 are more likely to be admitted involuntarily, members of Profile 3 to be admitted voluntarily, whereas Profile 2 membership is not statistically associated with admission status. In Stage 2, potential associations of Profile memberships with additional demographic, psychosocial and clinical factors were examined. The statistical significance of the association for each factor with the ensued profile memberships is presented in Table 5 and a description of the Profiles’ characteristics in Table 6 . Table 5. Statistical significance of each indicator with Profile memberships. Covariates Profile 1 (45.73%) Profile 2 (28.15%) Profile 3 (26.13%) Financial Status (MANSA) Poverty level income 0.0539 − 0.984*** 0.929* Low income − 0.3523 − 0.1055 0.4579 Average income − 0.0018 0.4252 − 0.4234 High income 0.3002 0.6641 − 0.9643 Financial Status Numeric (ratio) (MANSA) − 0.0771 0.565*** − 0.488*** Clinical diagnosis F20-F29 (Schizophrenia, Schizotypal disorder, Delusional disorder) 0.657*** − 0.835*** 0.1783 F30-F31 (Bipolar disorder) − 0.0515 0.2068 − 0.1553 F32-F33 (Depressive disorder) − 0.822*** 0.501*** 0.3206 Other (F00-F09 Organic mental disorders, F40-F48 Anxiety disorder) 0.2161 0.1274 − 0.343* HoNOS (Total) − 0.0802 − 0.1083 0.188* GAF Pervasive impairment 0.1804 − 0.369*** 0.1882 Serious impairment 0.0128 − 0.0639 0.0511 Moderate Impairment − 0.1932 0.432** − 0.2393 Previous admissions No previous admissions − 0.0682 0.526*** − 0.458*** 1 previous admission 0.3123 − 0.1865 − 0.1258 2–3 previous admissions 0.1449 − 0.334 0.1892 Above 4 previous admissions − 0.389** − 0.0056 0.395** Previous involuntary admissions No previous inv. admissions − 0.2565 0.5544*** − 0.2979 1 previous inv. admission 0.3861* − 0.3994 0.0133 2–3 previous inv. admissions 0.1521 − 0.2636 0.1115 Above 4 previous inv. admissions − 0.2817 0.1086 0.1731 Duration of disorder < 1 year − 0.1505 0.452*** − 0.3016 1–2 years 0.1652 − 0.0896 − 0.0756 3–10 years 0.325* − 0.28 − 0.0451 11–20 years 0.0777 − 0.2752 0.1975 > 20 years − 0.418** 0.1928 0.2247 Adherence to Medication Bad − 0.0779 − 0.2233 0.301* Medium − 0.1341 − 0.1755 0.3096 Good − 0.0597 0.0921 − 0.0324 No medication 0.2718 0.307 + − 0.579*** Type of previous treatment Medication − 0.299* − 0.008 0.307* Psychotherapeutic/ psychosocial treatment 0.0943 0.0769 − 0.1712 None 0.1435 0.0902 − 0.2337 Medication & psychosocial treatment 0.0613 − 0.1591 0.0977 Type of contact with mental health services Hospitalization − 0.3874 − 0.4746 0.862** Outpatient unit − 0.793*** 0.1468 0.646* Community services − 0.1039 − 0.109 0.2129 Private practitioner − 0.2311 0.0833 0.1478 None − 0.3485 − 0.2738 0.622** Other 1.3322 2.159** -3.491*** Mixed cases 0.532 − 1.5318 0.9998 Feelings of loneliness − 0.235*** − 0.439*** 0.674*** Open in a new tab Note: * p < 0.05, ** p < 0.01, *** p < 0.001. Table 6. Description of the Profiles’ characteristics (based on results presented in Tables 3 , 4 , and 5 ). Profile 1 (45.73%) Profile 2 (28.15%) Profile 3 (26.13%) Indicators Sex males – females Age less than 60 years old above 60 years old – Educational level secondary education primary education – Social support high high low Social network high – low QoL/Factor 1 (MANSA)—Satisfaction with Life and health related aspects high high low QoL/Factor 2 (MANSA)—Satisfaction with Quality of living environment – high low Covariates Living Arrangement family of origin their created family – Financial Status (MANSA) – high poverty Clinical diagnosis F20-F29 (schizophrenia spectrum) F32-F33 (depressive disorder) – HoNOS (Total) – – severe clinical condition GAF – moderate impairment – Previous admissions less than four none more than four Previous involuntary admissions one none – Duration of disorder 3 to 10 years less than one year – Adherence to Medication – – bad Type of previous treatment no medication – medication Type of contact with mental health services no contact other hospitalization outpatient unit no contact Feelings of loneliness no feelings of loneliness no feelings of loneliness strong feelings of loneliness Distal outcome Admission status involuntary – voluntary Open in a new tab Taking into account the supplementary information, the Profiles can be summarised as follows: Individuals in Profile 1 are mainly men, below 60, with secondary education, regardless of financial status, living with their family of origin. They have diagnosis of schizophrenia-spectrum disorders, with disorder duration between 3 and 10 years. They are likely to have had less than four previous involuntary admissions. They seem to have had no contact with outpatient mental health services and to not be receiving medication in the period prior to the current hospitalization. They report high social support, strong social network, high satisfaction with their life and no feelings of loneliness. They are more likely to have been involuntarily admitted. Individuals in Profile 2 tend to be above 60 years old, of both sex/genders, with low educational level and good income, living with their created family. They were diagnosed with depressive disorder, with disorder duration less than one year, had moderate impairment of functioning and no previous psychiatric admission. They report high social support, high satisfaction with their life as well as their living environment, and no feelings of loneliness. They are as likely to have been admitted voluntarily as involuntarily. Individuals in Profile 3 tend to be women, below the poverty threshold, independently of age and education level, equally likely to live with their family of origin or their created family. They have various diagnoses. They were experiencing severe clinical conditions in the period prior to hospitalization. They have had several previous psychiatric admissions. In the period preceding their current hospitalization, they had very limited contact with outpatient mental health services and bad adherence to medication. They report low social support, weak social networks, low satisfaction with their life as well as their living environment, and strong feelings of loneliness. They were most likely to have been admitted voluntarily. Discussion The study set to explore combinations of family-related factors that may be associated with psychiatric hospitalization and to determine whether these are differentiated by family type. The application of Latent Class Analysis resulted in three profiles that deepen our knowledge on the role that family arrangements play on involuntary psychiatric admission. Living with one’s family of origin Profile 1 contains almost half of the sample, portraying the typical profile of a significant proportion of individuals involuntarily hospitalized. It consists mainly of younger men, experiencing psychotic symptomatology, who live with their family of origin. We assume that members of this Profile continue to live with their family of origin due their mental health difficulties combined with the associated stigma and self-stigma 27 , 28 that pose obstacles to creating a family 29 . As indicated by other studies of this population, the family of origin seems to constitute their main social support system 30 , 31 and, when in crisis, initiates their admission, usually involuntary 9 . The fact that mainly men are included in this Profile might be related to women having later onset of psychosis and more favorable course with better social functioning 32 , which may allow them to create their own family. The social indicators of Profile 1 members are good, suggesting moderate to good perceived social support, in line with literature on patients living with their family 3 , 14 , 33 . However, satisfaction with their living environment is not as high as for other aspects of life, possibly reflecting the difficulties characterizing relationships in families with a member with psychosis 7 , 8 . Living with one’s created family In Profile 2, which covers approximately one quarter of the sample, participants live with the family that they have created. They tend to be older, with no history of mental disorders, who are admitted when they develop depressive symptomatology. We may assume that the fact that these individuals had created a family is related to them developing a mental disorder at later age, as well as to this disorder being in the depressive spectrum. This points to the importance of the age of onset of mental disorders, as well as their severity, for shaping one’s familial and social environment, which in turn influences the course of the disorder and treatment pathways. This Profile has the highest social indicators, with stronger statistical significance compared to those of Profile 1. This suggests that living with one’s created family is the most socially satisfying and supportive family arrangement. Living in socially deprived families Profile 3, also representing about a quarter of the sample, includes persons, mainly women, who live in families with poverty level income. They have limited contact with mental health services and, when their mental state deteriorates, they tend to be admitted voluntarily. This Profile also seems to confirm the mutual relation between socioeconomic and clinical factors, this time on the negative side. For members of this Profile, living in poverty co-exists, and is possibly associated, with low social support, sparce social networks, low satisfaction with life and living environment and strong feelings of loneliness, as indicated by the relevant literature 34 . The fact that members of this Profile have had several previous psychiatric hospitalizations suggests a certain duration of mental health problems, that seem to worsen in the period prior to their hospitalization. For these persistent mental health problems, members of this Profile report receiving medication as the only form of treatment. Moreover, the main reported prior form of contact with mental health services is hospitalization, with very limited contact with outpatient mental health services. These findings indicate long-term mental distress, that is not adequately dealt with due to lack of access to and contact with appropriate mental health services. This points to a vicious circle of psychosocial malaise and clinical symptomatology, that leads to repeated voluntary hospitalizations, presumably as a last resort for receiving much-needed care. The association of this Profile with voluntary hospitalization is a surprising finding, given that social deprivation has repeatedly been shown to be linked to involuntary hospitalization 35 . It might be explained, though, by the preponderance of women in this Profile; women are more frequently hospitalized voluntarily, as they more often than men seek help for mental health problems and they are perceived as less dangerous 20 , 36 . On the other hand, it might be that for disenfranchised people in mental distress hospitalization is a more easily accessible form of care, operating as substitute for the deficit in professional social support and health care 37 . It is also worth noting that, as suggested in the literature, voluntary admission potentially entails a range of informal coercion practices exercised not only by mental health professionals but also relatives 38 , 39 . This is a good example of the synergistic role of multiple adversities in mental health and treatment pathways, pointing to the need for intersectional research on severe mental disorders 40 . Links between family structure, social factors, mental health care and psychiatric hospitalization When comparing the two types of family arrangement with regard to type of psychiatric hospitalization, the association in the, albeit limited, literature between living with one’s family of origin and involuntary hospitalization 41 is verified, while living with the family that one has created is not associated with a particular type of psychiatric admission. However, having a richer picture of the profiles allows us to consider that variables other than family type might play a significant role in the type of psychiatric admission. When comparing Profile 1 and Profile 2, it can be assumed that the type and severity of symptomatology, as well as the duration of disorder, might be significantly associated with involuntary hospitalization. In the case of Profile 3, socioeconomic adversity and social deprivation, coupled with inadequate access to mental health services seem to lead to hospitalization. The relation between family type and social support is equally complex. The clear finding of higher perceived social support and more life satisfaction in people who live with their created family, commonly with their spouse and children, is in line with international literature 42 , 43 . On the other hand, individuals who live with their family of origin, presumably their parents, are satisfied with the social support they receive and their life overall, and only moderately happy with their living situation. People who created their own family in a way that it operates as a supportive and satisfactory living environment for them are more likely those with less severe psychopathology of shorter duration. For the majority of individuals with psychotic symptomatology who live throughout their adulthood with their parents, the family operates as the central source of support, but this cohabitation brings inevitable strains to the family dynamic that are well documented in the literature 44 – 46 . In Greece, the family continues to operate as the main source of support of its members, especially those who are considered vulnerable and in need 5 . This, combined with the lack of appropriate welfare support for independent living, might explain the large number of persons in Profile 1 who live with their family of origin well into their adulthood. The high social support provided by one’s family does not seem to deter involuntary psychiatric hospitalization. When one’s mental state deteriorates, symptomatology increases and functioning is compromised, it seems that the family resorts to hospitalization, to guarantee treatment for their loved ones, even against their will 47 – 49 . In Greece, the vast majority of requests for involuntary hospitalization are initiated by the person’s family 6 ; this is not surprising, given that most patients with severe psychopathology live with their family of origin and that legislation prioritizes family-initiated involuntary admissions, as discussed above. Resorting to involuntary hospitalization in order to secure mental health care for one’s relative is mainly linked to the lack of systematic contact with and monitoring by community mental health services 6 , 50 . As can be seen in Table 1 , approximately 40% of patients had no contact with mental health services in the year prior to hospitalization, in many cases despite persistent psychopathology. For those in contact with services, the most common types of contact were hospitalization, follow-up by the outpatient services of the hospitalization units and visiting a psychiatrist in private practice. Use of community mental health services was almost non-existent. This seems to be the case across Profiles, highlighting the gaps in community mental health care in the area, which in turn reflect the serious inadequacies of the mental health care system in Greece 2 , 51 . The inadequacies of the mental health service system in Greece in terms of providing continuity of care for persons with severe mental health difficulties has been repeatedly pointed out in the relevant literature 6 , 30 , 51 . The lack of continuity of care can also be deduced from the very low percentages of referrals to mental health services upon discharge that were recorded nationally 1 , 6 . The finding that regions of Greece that have an organised community mental health service system report considerably lower rates of involuntary hospitalizations is one more indication of the role of community care in deterring involuntary psychiatric hospitalizations 1 . In conclusion, it seems that it is the lack of availability of appropriate mental health services, more than the degree of support provided by one’s family, that might be behind psychiatric hospitalizations, especially involuntary ones 6 . An interesting finding is the quarter of the sample who, while they live with their family, feel that they are not socially supported and are not satisfied with their living situation. This might be accounted for by considering the other two characteristic features of this group, namely socioeconomic adversity and sex/gender. The social determinants of mental health, and in particular the role of structural inequality, poverty and socioeconomic adversity in poor mental health, have been established internationally. Specifically, the high levels of mental distress and psychosocial malaise associated with socioeconomic adversity are further exacerbated by inadequate psychosocial support and mental health care due to obstacles to accessing care due to structural inequality 52 – 55 . Given that women are more likely than men to experience socioeconomic adversity 56 , the systematic finding that they experience more frequent and more severe psychosocial malaise 57 is not surprising. Profile 3 in our findings portrays women living in poverty with persistent mental health problems and low social support and quality of life. It seems, thus, that for women living in poverty the family might not operate as a source of social support and that it might, in fact, adversely affect their well-being, producing mental distress. This points to the complex interaction of factors that affect the role of the family in supporting or burdening its members’ mental health. In the context of lack of support by their family, social environment and mental health services, whenever overwhelmed by mental distress, these women seem to resort to voluntary psychiatric hospitalization, presumably seeking emotional relief and safety. Strengths, limitations and future directions This study explores the complex interrelation between several contextual, person-centred variables, that are suggested in the literature to be related to psychiatric hospitalizations. Latent Class Analysis provides an ideal method for theory-driven models exploring complex interactions. This methodological stance enabled the investigation of the relationship between family arrangements and admission status as mediated by the combined effect of objective socio-demographic, illness and treatment related variables and those concerned with subjective social support and networks, quality of life and feelings of loneliness. Through repeated statistical trials, we identified, amongst the various candidate factors, those that are significantly associated with family type and admission status. This is a strength of the study, verifying and augmenting our understanding of the family related factors that are linked to involuntary hospitalizations, producing innovative, actionable knowledge. Moreover, it is a population study, covering all the persons hospitalized in the public acute psychiatric units of the metropolitan area of Thessaloniki over a year period, providing comprehensive, ecologically valid data. In terms of limitations, the two main types of family were treated as homogeneous categories, precluding the exploration of internal differentiations that might have been important, e.g., living with one’s siblings, one’s children etc. Also, the study utilized data already collected for the MANE project, that recorded only patient-centred family variables. We were, thus, unable to include family-related factors, such as relatives’ quality of life, family burden and family functioning, that could have clarified the relationship between our main variables 5 , 11 , 12 . Moreover, the MANE study did not collect data from private psychiatric clinics, that tend to be used by higher income families. This probably skewed our data towards families with lower income, as can be seen by the large percentage of persons with poverty level income recorded in Table 1 . Also, given that by law involuntary hospitalizations are implemented exclusively in public acute psychiatric clinics, not including private psychiatric clinics would be responsible for the high percentage of involuntary hospitalizations in the sample. Finally, the study was conducted in Thessaloniki, reflecting the socioeconomic and mental health system conditions there, and is not necessarily generalizable to other regions of Greece or internationally. Focusing on family unavoidably excluded participants in other forms of living arrangements, such as living alone or in non-family arrangements. In a previous study of the same project that included the whole sample 3 , it was estimated that a relatively large sub-group of the sample (27.6%) live alone, while very few participants live in supported accommodation (1.3%), with others non-relatives (2.9%) and were homeless (2.2%). These latter sub-groups were considered too small to influence the sample characteristics. Comparisons between the sub-group living alone with the sub-groups living with family showed significant differences only in the social variables of social support and social networks and in the type of admission, with individuals living alone experiencing significantly lower social support and weaker social networks and being admitted involuntarily. There were no systematic differences in any of the other demographic, social, clinical and treatment-related variables examined. We, thus, consider that the exclusion of participants who live alone from the sample of the present study did not affect the findings or limit their generalizability. LPA-related limitations should also be acknowledged. The profile solution may be sample-dependent, and the moderate entropy suggests some uncertainty in class assignment. The inclusion of subjective indicators (e.g., quality of life, loneliness) may further influence profile stability. Even though LPA is model-based, the solution can be indicator-dependent, meaning that the number and nature of profiles may change when alternative sets of variables are used. Future studies should consider sensitivity analyses with alternative indicator sets to evaluate the robustness and replicability of the profile structure. In terms of methodology, employing a cluster analysis method did not allow determining the causal pathways and the direction of these relationships. Moreover, the relatively small number of variables used in LCA meant that some variables had to be prioritized for the formation of profiles, while others be considered as external covariates, posing restrictions to the types of relationships between variables that could be explored. A further study could test a mediation analysis between patients who live with families of origin vs created families on involuntary admission status mediated by clinical and social factors. Future studies should examine the impact of the broader socioeconomic and systemic factors that we identified on family functioning to elucidate the relations between ecological factors, individual psychopathology and family dynamics. Moreover, they could explore the nature of relationships between particular variables within each of the profiles, for example, the contribution of clinical factors vs stigma as reasons for not creating one’s own family in Profile 1. A more detailed focus on created families could determine whether certain family characteristics, paths or conditions render their members with a mental health problem prone to voluntary or involuntary admission 42 , 58 , 59 . Finally, the factors that drive women in socioeconomic adversity to repeated voluntary hospitalizations are worth exploring further, as well as ways of supporting them and averting their revolving door pathway 36 , 60 . Conclusions and recommendations The current study aims to contribute to the discussion of contextual features that are related to involuntary admission 61 , focusing on the role of living environments. We produced distinct profiles of persons hospitalized in acute psychiatric units who live with their family, enabling the exploration of the mutual interrelations between demographic, socioeconomic, social, clinical and treatment-related factors that operate within each family context to drive or deter involuntary psychiatric admission. As can be seen in Profiles 1 and 2, the family seems to provide social support and to be a source of satisfaction for the majority of the participants. Still, when faced with deterioration of mental state, these families would resort to involuntary hospitalization. This finding suggests that supporting families to manage effectively the mental distress of their member, in all its phases and particularly in acute states, is important for preventing hospitalization. Family therapy or psychoeducation might contribute to improving family functioning and interactional dynamics 62 , 63 . Supporting the family to develop its own strategies for dealing with the mental distress of its member outside the crisis periods is important for preventing crises that might result in hospitalization 62 . The family can also be encouraged to act as gatekeepers, ensuring the continuous monitoring of its member through liaising with mental health services 9 . It is also essential to intervene towards improving families’ strategies for dealing with the mental health crises of their members, with advance crises plans that include both internal to the family management strategies and timely referrals to appropriate mental health services 63 – 65 . Engaging relatives/carers as partners in the formulation of individualized care plans, taking into consideration their preferences, needs and mental health state, as well as their views on involuntary hospitalization 64 in a collaborative model of care is essential for ensuring continuity of engagement with mental health services and effective timely intervention. This would help families to prevent and deal with mental health crises, potentially avoiding hospitalization, especially involuntary 48 , 49 , 63 , 66 . The association between lack of contact with community mental health services and psychiatric hospitalization, across Profiles, points to the importance of the existence and availability of community mental health services for regular monitoring and support as well as for community-based crisis services 48 , 49 , 67 . Assertive Community Treatment 68 and Intensive Case Management 69 teams can be established for continuous monitoring of populations with severe and enduring psychopathology. Establishing community mental health teams offering comprehensive mental health care with various levels of engagement depending on the severity of psychopathology and the phase of distress, along the lines of Flexible ACT 70 , ACCESS 71 and the Model Project of Need-Adapted Care 72 , might be more feasible for a country like Greece, that does not have a fully functional community mental health care system. There are also well-established models of crisis interventions, such as Crisis Resolution Teams 73 and Crisis Homes 65 that could be utilized to deescalate crises without hospitalization. Coordination between community-based and inpatient services is essential to ensure continuity of care as well as continuous training of mental health staff to provide comprehensive, collaborative and respectful care 47 , 49 , 74 . For individuals with relatively recent onset of mental distress, as in Profiles 1 and 2, timely appropriate treatment is of the essence, to prevent continuous or recurring psychopathology, increasing burden to the family, deterioration of functioning and revolving door phenomena 75 . Specifically for individuals with psychosis in Profile 1, which is the largest sub-group of our sample, it is important to design early interventions in psychosis services that engage both the person and their family, aiming for better long-term management of mental distress, adherence to treatment and prevention of psychiatric hospitalizations 28 , 76 . For the not-insignificant percentage of persons with long-term mental health problems who live in socioeconomic adversity and detrimental family environments, in Profile 3, emphasis should be placed on providing appropriate social and mental health care and support. Community-based interventions and intersectoral collaboration between social/welfare services and mental health services for providing relief from socioeconomic strain, countering the adverse effects of poverty, supporting families and local communities to build resources, strengthening availability of mental health and social care services are important to fortify the mental health of these most vulnerable populations. Many examples exist, that could be drawn upon 51 , 52 , 77 . The finding that the majority of persons in this group are women points to the need for interventions that increase social support, quality of life and empowerment of women with mental disorders in the family and perhaps encourage a re-negotiation of family roles 78 – 80 . Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (92.7KB, pdf) Acknowledgements The authors wish to thank the MSc in Clinical Psychology and Neuropsychology of the Aristotle University of Thessaloniki, the MSc in Social Psychiatry of Democritus University of Thrace and the Association for Regional Development and Mental Health for their support for the research work that led to this publication and for agreeing to fund the publication of this paper. Author contributions Conceptualization : Odysseas Anastasopoulos, Eugenie Georgaca, Anastasia Zissi, Stelios Stylianidis; Methodology : Odysseas Anastasopoulos, Eugenie Georgaca, Anastasia Zissi, Lily Peppou; Formal analysis and investigation : Odysseas Anastasopoulos, Julie Vaiopoulou; Writing - original draft preparation : Odysseas Anastasopoulos, Eugenie Georgaca, Julie Vaiopoulou; Writing - review and editing : Anastasia Zissi, Lily Peppou, Aikaterini Arvaniti, Maria Samakouri, Stelios Stylianidis, Thessaloniki Mane Group; Resources: Thessaloniki Mane Group; Supervision : Eugenie Georgaca. Funding No funds, grants, or other support was received for conducting this study. Publication of this article is funded by the MSc in Clinical Psychology and Neuropsychology of the Aristotle University of Thessaloniki, the MSc in Social Psychiatry of Democritus University of Thrace and the Association for Regional Development and Mental Health. Data availability Original data cannot be available at present, as this is an ongoing project, but will be made available at a later stage. Processed data is provided within the manuscript. Competing interests The authors declare no competing interests. Ethics approval The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Approval was granted by the Hospital Scientific Boards of all participating clinics, the National Data Protection Agency, the Regional Health Authority and the Research Ethics Committee of the School of Psychology of Aristotle University of Thessaloniki. Informed consent Informed consent was obtained from all individual participants included in the study. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Dimitrios Sevris is deceased. A list of authors and their affiliations appears at the end of the paper. Contributor Information Eugenie Georgaca, Email: [email protected]. Thessaloniki Mane Group: Vasileios Panteleimon Bozikas , Ioannis Diakogiannis , Konstantinos Fokas , Georgios Garyfallos , Ioanna Gkolia , Vassiliki Karpouza , Ioannis Nimatoudis , Georgios Patsinakidis , Dimitrios Sevris , and Aikaterini Vlachaki References 1. Stylianidis, S. et al. Involuntary psychiatric hospitalizations in Greece: Contemporary research and policy implications. Psychiatriki. 34 , 204–211 (2023). [ DOI ] [ PubMed ] [ Google Scholar ] 2. Turnpenny, A., Petri, G., Finn, A., Beadle-Brown, J. & Nyman, M. Mapping and understanding exclusion: Institutional, coercive and community-based services and practices across Europe. 10.22024/UniKent/01.02/64970 (2018). 3. Georgaca, E., Anastasopoulos, O., Stamovlasis, D., Zissi, A., Peppou, L. E., Arvaniti, A., Samakouri, M., Stylianidis, S. & the Thessaloniki MANE Group. What renders living alone a risk factor for involuntary psychiatric admission?. Curr Psychol . 43 , 13828 (2024). 4. Rugkåsa, J. & Canvin, K. Mental health, coercion and family caregiving: issues from the international literature. BJPsych Int. 14 (3), 56–58 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Koutra, K., Simos, P., Triliva, S., Lionis, C. & Vgontzas, A. N. Linking family cohesion and flexibility with expressed emotion, family burden and psychological distress in caregivers of patients with psychosis: A path analytic model. Psychiatry Res. 240 , 66–75 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 6. Stylianidis, S. et al. Mental health care in Athens: Are compulsory admissions in Greece a one-way road?. Int. J. Law Psychiatry 52 , 28–34 (2017). [ DOI ] [ PubMed ] [ Google Scholar ] 7. Fekadu, W., Mihiretu, A., Craig, T. K. J. & Fekadu, A. Multidimensional impact of severe mental illness on family members: Systematic review. BMJ Open 9 (12), e032391 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Kuhney, F. S., Miklowitz, D. J., Schiffman, J. & Mittal, V. A. Family-based psychosocial interventions for severe mental illness: Social barriers and policy implications. Policy Insights Behav. Brain Sci. 10 (1), 59–67 (2023). [ Google Scholar ] 9. Rugkåsa, J., Molodynski, A. & Burns, T. Family carers and coercion in the community. Coercion in Community Mental Health Care: International Perspectives . 161–178 (2016). 10. Dixon, J., Stone, K. & Laing, J. Beyond the call of duty: A Qualitative study into the experiences of family members acting as a Nearest Relative in Mental Health Act assessments. Br. J. Soc. Work. 52 (7), 3783–3801 (2022). [ Google Scholar ] 11. Koutra, K., Triliva, S., Roumeliotaki, T., Lionis, C. & Vgontzas, A. N. Identifying the socio-demographic and clinical determinants of family functioning in Greek patients with psychosis. Int. J. Soc. Psychiatry 61 (3), 251–264 (2015). [ DOI ] [ PubMed ] [ Google Scholar ] 12. Ribé, J. M. et al. Quality of life in family caregivers of schizophrenia patients in Spain: caregiver characteristics, caregiving burden, family functioning, and social and professional support. Int. J. Psychiatry Clin. Pract. 22 (1), 25–33 (2018). [ DOI ] [ PubMed ] [ Google Scholar ] 13. Watson, P. et al. A meta-analysis of factors associated with quality of life in first episode psychosis. Schizophr. Res. 202 , 26–36 (2018). [ DOI ] [ PubMed ] [ Google Scholar ] 14. van der Post, L. F. M. et al. Social support and risk of compulsory admission: Part IV of the Amsterdam study of acute psychiatry. Psychiatr. Serv. 63 (6), 577–583 (2012). [ DOI ] [ PubMed ] [ Google Scholar ] 15. Guzzetta, F. et al. First-ever admitted psychiatric inpatients in Italy: Clinical characteristics and reasons contributing to admission: Findings from a national survey. Psychiatry Res. 176 (1), 62–68 (2010). [ DOI ] [ PubMed ] [ Google Scholar ] 16. Thoits, P. A. Perceived social support and the voluntary, mixed, or pressured use of mental health services. Soc. Mental Health. 1 (1), 4–19 (2011). [ Google Scholar ] 17. Petkari, E., Giacco, D. & Priebe, S. Factorial structure of the Manchester short assessment of quality of life in patients with schizophrenia-spectrum disorders. Qual. Life Res. 29 (3), 833–841 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Dayton, C. M. & Dayton, M. C. Latent class scaling analysis (Sage.1998). 19. Magidson, J. & Vermunt, J. K. Latent class factor and cluster models, bi-plots, and related graphical displays. Sociol. Methodol. 31 (1), 223–264 (2001). [ Google Scholar ] 20. Georgaca, E. et al. Clinical determinants of involuntary psychiatric hospitalization: A clinical profile approach. J. Clin. Psychol. 79 , 2081–2100 (2023). [ DOI ] [ PubMed ] [ Google Scholar ] 21. Mollborn, S., Lawrence, E. M. & Hummer, R. A. A gender framework for understanding health lifestyles. Soc. Sci. Med. 265 , 113182 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Read, S. et al. Public preferences for paying for social care in later life in England: A latent class analysis. Soc. Sci. Med. 274 , 113803 (2021). [ DOI ] [ PubMed ] [ Google Scholar ] 23. Bakk, Z., Tekle, F. B. & Vermunt, J. K. Estimating the association between latent class membership and external variables using bias-adjusted three-step approaches. Sociol. Methodol. 43 (1), 272–311 (2013). [ Google Scholar ] 24. Stamovlasis, D., Papageorgiou, G., Tsitsipis, G., Tsikalas, T. & Vaiopoulou, J. Illustration of step-wise latent class modeling with covariates and taxometric analysis in research probing children’s mental models in learning sciences. Front. Psychol. 9 , 532 (2018). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. de Medina-Moragas, A. J., Lima-Serrano, M., Fernández-Fernández, M. J. & Lima-Rodríguez, J. S. Quality of life of individuals with serious mental illness and family caregivers. Curr. Psychol. 43 (25), 21756–21767 (2024). [ Google Scholar ] 26. Wakim, M. L., Al Haddad, C., Haddad, C., Mouawad, M. & Hachem, D. The relationship between perceived social support and quality of life among hospitalized patients with schizophrenia. Sci. Rep. 15 (1), 36039 (2025). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Degnan, A., Berry, K., Humphrey, C. & Bucci, S. The relationship between stigma and subjective quality of life in psychosis: A systematic review and meta-analysis. Clin. Psychol. Rev. 85 , 102003 (2021). [ DOI ] [ PubMed ] [ Google Scholar ] 28. Georgaca, E., Zissi, A. & Cromby, J. Internalized stigma and self-presentation strategies of persons with psychotic and psychiatric experiences. J. Commun. Psychol. 50 , 2875–2891 (2022). [ DOI ] [ PubMed ] [ Google Scholar ] 29. Walker, S. et al. Clinical and social factors associated with increased risk for involuntary psychiatric hospitalisation: a systematic review, meta-analysis, and narrative synthesis. Lancet Psychiatry. 6 (12), 1039–1053 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Economou, M., Lazaratou, H. & Ploumpidis, D. Compulsory admissions in Greece: multifaceted action is required. The Lancet. 391 (10129), 1481 (2018). [ DOI ] [ PubMed ] [ Google Scholar ] 31. Koutra, K. et al. Family functioning in first-episode and chronic psychosis: The role of patient’s symptom severity and psychosocial functioning. Commun. Ment. Health J. 52 , 710–723 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 32. Haarmans, M. Everything you always wanted to know about sex (and gender) in psychosis but were afraid to ask: A narrative review in Psychopathology in women (ed. M. Sáenz-Herrero) 453–479 (Springer Nature Switzerland, 2019). 33. van der Post, L. F. M. et al. Involuntary admission of emergency psychiatric patients: Report from the Amsterdam study of acute psychiatry. Psychiatr. Serv. 60 (11), 1543–1546 (2009). [ DOI ] [ PubMed ] [ Google Scholar ] 34. Smyth, N., Siriwardhana, C., Hotopf, M. & Hatch, S. L. Social networks, social support and psychiatric symptoms: Social determinants and associations within a multicultural community population. Soc. Psychiatry Psychiatr. Epidemiol. 50 (7), 1111–1120 (2015). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Weich, S. et al. Variation in compulsory psychiatric inpatient admission in England: A cross-classified, multilevel analysis. Lancet Psychiatry 4 (8), 619–626 (2017). [ DOI ] [ PubMed ] [ Google Scholar ] 36. Feeney, A., Umama-Agada, E., Gilhooley, J., Asghar, M. & Kelly, B. D. Gender, diagnosis and involuntary psychiatry admission in Ireland: a report from the Dublin Involuntary Admission Study (DIAS). Int. J. Law Psychiatry 66 , 101472 (2019). [ DOI ] [ PubMed ] [ Google Scholar ] 37. McCarthy, M. Poor people view hospitals as providing better access, care, and social support. BMJ 17 (347), f4556 (2013). [ DOI ] [ PubMed ] [ Google Scholar ] 38. Klingemann, J., Świtaj, P., Lasalvia, A. & Priebe, S. Behind the screen of voluntary psychiatric hospital admissions: A qualitative exploration of treatment pressures and informal coercion in experiences of patients in Italy, Poland and the United Kingdom. Int. J. Soc. Psychiatry 68 (2), 457–464 (2022). [ DOI ] [ PubMed ] [ Google Scholar ] 39. Hempeler, C., Potthoff, S., Scholten, M., Juckel, G. & Gather, J. Strategies to promote treatment compliance: a grounded theory study with relatives of people with a serious mental health condition. BMC Psychiatry 24 (1), 490 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Haarmans, M., McKenzie, K., Kidd, S. A. & Bentall, R. P. Gender role strain, core schemas, and psychotic experiences in ethnically diverse women: A role for sex- and gender-based analysis in psychosis research?. Clin. Psychol. Psychother. 25 (6), 774–784 (2018). [ DOI ] [ PubMed ] [ Google Scholar ] 41. Di Lorenzo, R. et al. Involuntary Hospitalizations in an Italian Acute Psychiatric Ward: A 6-Year Retrospective Analysis. Patient Prefer. Adher. 17 , 3403–3420 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Berghöfer, A., Martin, L., Hense, S., Weinmann, S. & Roll, S. Quality of life in patients with severe mental illness: a cross-sectional survey in an integrated outpatient health care model. Qual. Life Res. 29 , 2073–2087 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Thomas, P. A., Liu, H. & Umberson, D. Family relationships and well-being. Innovation in Aging. 1 (3), igx025 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Adrien, V. et al. Association between family functioning and psychotic transition in ultra-high risk adolescents and young adults. Front. Psych. 21 (14), 1177311 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Harries, C. I., Smith, D. M., Gregg, L. & Wittkowski, A. Parenting and Serious Mental Illness (SMI): A Systematic Review and Metasynthesis. Clin. Child. Fam. Psychol. Rev. 26 (2), 303–342 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Thompson, E. et al. Family functioning moderates the impact of psychosis-risk symptoms on social and role functioning. Schizophr. Res. 204 , 337–342 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Stylianidis, S. et al. Patients’ views and experiences of involuntary hospitalization in Greece: A focus group study. Int. J. Cult. Ment. Health 11 (4), 425–436 (2018). [ Google Scholar ] 48. Abou Seif, N., Wood, L. & Morant, N. Invisible experts: A systematic review & thematic synthesis of informal carer experiences of inpatient mental health care. BMC Psychiatry. 22 , 347 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Bartl, G. et al. A qualitative meta-synthesis of service users’ and carers’ experiences of assessment and involuntary hospital admissions under mental health legislations: a five-year update. BMC Psychiatry 24 (1), 476 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. Rains, L. S. et al. Understanding increasing rates of psychiatric hospital detentions in England: development and preliminary testing of an explanatory model. BJPsych Open. 6 (5), e88 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Loukidou, E. et al. Greek mental health reform: Views and perceptions of professionals and service users. Psychiatriki. 24 , 37–44 (2013). [ PubMed ] [ Google Scholar ] 52. Alegría, M., NeMoyer, A., Falgàs Bagué, I., Wang, Y. & Alvarez, K. Social determinants of mental health: Where we are and where we need to go. Curr. Psychiatry Rep. 20 (11), 95 (2018). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Allen, J., Balfour, R., Bell, R. & Marmot, M. Social determinants of mental health. Int. Rev. Psychiatry 26 (4), 392–407 (2014). [ DOI ] [ PubMed ] [ Google Scholar ] 54. Pūras, D. Final report of the Special Rapporteur on the right of everyone to the enjoyment of the highest attainable standard of physical and mental health. UN. Human Rights Council. https://digitallibrary.un.org/record/1298436?v=pdf (2020). 55. Silva, M., Loureiro, A. & Cardoso, G. Social determinants of mental health: A review of the evidence. Eur. J. Psychiatry. 30 (4), 259–292 (2016). [ Google Scholar ] 56. American Psychological Association. Fact sheet: Women and socioeconomic status. https://www.apa.org/pi/ses/resources/publications/women (2025). 57. Christiansen, D. M., McCarthy, M. M. & Seeman, M. V. Where sex meets gender: How sex and gender come together to cause sex differences in mental illness. Front. Psych. 13 , 856436 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 58. Georgaca, E. & Zissi, A. Socially differentiated life trajectories of individuals with experience of psychosis: A biographical study. Mental Health & Prevention. 14 , 100153 (2019). [ Google Scholar ] 59. Tan, J. H. P., Conlon, C., Tsamparli, A., O’Neill, D. & Adamis, D. The association between family dysfunction and admission to an acute mental health inpatient unit: A prospective study. Irish J. Psychol. Med. 39 (4), 340–350 (2022). [ DOI ] [ PubMed ] [ Google Scholar ] 60. Falkenburg, J. & Tracy, D. K. Sex and schizophrenia: a review of gender differences. Psychosis 6 (1), 61–69 (2014). [ Google Scholar ] 61. Aluh, D. O. et al. Beyond Patient Characteristics: A Narrative Review of Contextual Factors Influencing Involuntary Admissions in Mental Health Care. Healthcare. 11 (14), 1986 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Xu, Z. et al. Effectiveness of interventions to promote help-seeking for mental health problems: systematic review and meta-analysis. Psychol. Med. 48 (16), 2658–2667 (2018). [ DOI ] [ PubMed ] [ Google Scholar ] 63. Naughton-Doe, R. et al. Interventions that support unpaid carers of adult mental health inpatients: a scoping review. J. Ment. Health 34 (1), 105–121 (2025). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 64. Jankovic, J. et al. Family caregivers’ experiences of involuntary psychiatric hospital admissions of their relatives: A qualitative study. PLoS ONE 6 (10), e25425 (2011). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Gooding, P., McSherry, B. & Roper, C. Preventing and reducing ‘coercion’ in mental health services: An international scoping review of English-language studies. Acta Psychiatr. Scand. 142 (1), 27–39 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Zoladl, M. et al. Applying collaborative care model on intensive caregiver burden and resilient family caregivers of patients with mental disorders: A randomized controlled trial. Iran. J. Psychiatry 15 (1), 17 (2020). [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Barbui, C. et al. Efficacy of interventions to reduce coercive treatment in mental health services: umbrella review of randomised evidence. Br. J. Psychiatry 218 , 185–195 (2021). [ DOI ] [ PubMed ] [ Google Scholar ] 68. Marshall, M. & Lockwood, A. Assertive community treatment for people with severe mental disorders. Cochrane Database Syst. Rev . Cd001089 (2000). [ DOI ] [ PubMed ] 69. Dieterich, M. et al. Intensive case management for severe mental illness. Cochrane Database of Syst. Rev . 1:CD007906 (2017). [ DOI ] [ PMC free article ] [ PubMed ] 70. van Veldhuizen, J. R. FACT: Α Dutch version of ACT. Commun. Ment. Health J. 43 (4), 421–433 (2007). [ DOI ] [ PubMed ] [ Google Scholar ] 71. Schöttle, D. et al. Reduction of involuntary admissions in patients with severe psychotic disorders treated in the ACCESS integrated care model including therapeutic Assertive Community Treatment. Front. Psych. 10 , 736 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 72. Wullschleger, A., Berg, J., Bermpohl, F. & Montag, C. Can, “Model Projects of Need-Adapted Care” reduce involuntary hospital treatment and the use of coercive measures?. Front. Psych. 9 , 168 (2018). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. Wheeler, C. et al. Implementation of the Crisis Resolution Team model in adult mental health settings: A systematic review. BMC Psychiatry 15 , 74 (2015). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Berring, L. L. Factors and processes facilitating recovery from coercion in mental health services: A meta-ethnography. Healthcare. 12 , 628 (2024). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Stefanis, N. C. et al. A proposal for the implementation of Early Intervention in Psychosis (EIP) services in Greece: If not now, when?. Psychiatrike = Psychiatriki 29 (2), 107–117 (2018). [ DOI ] [ PubMed ] [ Google Scholar ] 76. Chronister, J., Chou, C. C., Kwan, K. L. K., Lawton, M. & Silver, K. The meaning of social support for persons with serious mental illness. Rehabil. Psychol. 60 (3), 232 (2015). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 77. Castillo, E. G. et al. Community interventions to promote mental health and social equity. Curr. Psychiatry Rep. 21 , 35 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 78. Gaino, L. V. et al. The role of social support in the psychological illness of women. Rev. Lat. Am. Enfermagem 18 (27), e3157 (2019). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 79. Mowbray, C. T., Bybee, D., Hollingsworth, L., Goodkind, S. & Oyserman, D. Living arrangements and social support: Effects on the well-being of mothers with mental illness. Soc. Work Res. 29 (1), 41–55 (2005). [ Google Scholar ] 80. Shooshtari, S., Abedi, M. R., Bahrami, M. & Samouei, R. Empowerment of women and mental health improvement with a Preventive approach. J. Educ. Health Promotion. 7 , 31 (2018). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1 (92.7KB, pdf) Data Availability Statement Original data cannot be available at present, as this is an ongoing project, but will be made available at a later stage. Processed data is provided within the manuscript. 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