The method and analysis plan were preregistered ( Ethical approval (ethical permission number: IV/2423–3/2022/EKU) was obtained from the national Medical Research Council. All procedures were carried out in accordance with the Declaration of Helsinki on ethical principles for medical research involving human subjects, and with the relevant guidelines and regulations. Written informed consent was obtained from all subjects before participating in the study.
Study sample
A cross-sectional design was used to measure mental health competencies, mental disorder symptoms, and well-being in psychiatric and non-clinical community samples. In the case of the former sample, data were collected between 22 April 2022 and 2 February 2023 from four Hungarian healthcare facilities under the following conditions. In the Department of Psychiatry and Psychotherapy, Semmelweis University, data were collected from inpatients after their medication had been adjusted (1.5–2 weeks after admission). Outpatients of the Community Psychiatry Centre, Semmelweis University completed the questionnaire at their first medical examination. Data from outpatients at the Psychosomatic Centre of the Institute of Behavioural Sciences, Semmelweis University were collected during the patients’ third therapy session. Data were collected from inpatients at the National Institute of Mental Health, Neurology, and Neurosurgery at the Nyírő Gyula Hospital after medication adjustment (1.5–2 weeks after admission), and from outpatients during their third therapy session. The sample received the information statement, the consent form, and the questionnaire in paper format. In a separate document, the patient’s psychiatrist or clinical psychologist provided information about the diagnosis of the patient’s mental disorder(s), the severity of the presenting symptoms, and the patient’s pharmacotherapy. The inclusion criteria were: (1) age: 18–80 years; (2) voluntary participation; and (3) diagnosis with (a) mental disorder(s). The exclusion criterion was a condition that impaired cognitive function and prevented the completion of the questionnaire. The psychiatric sample consisted of 129 patients (44 male, 85 female), aged M = 39.6 (SD = 14.9).
The non-clinical community sample is a non-representative, convenient sample collected online to provide additional information compared to the psychiatric sample. Participants received the same information statement, consent form and self-report questionnaires. Data were collected between 7 August 2022 and 18 April 2023. A total of 253 Hungarian adults (43 men, 210 women), aged M = 40.1 (SD = 14.1), participated in the study. Further socio-demographic indicators of the samples are presented in Supplementary Table 1.
Measures
Fourteen questions referred to sociodemographic data. Twenty-seven questions measured general mental state and physical condition and symptoms. One question (Positive experience%) assessed the proportion of the respondent’s recent positive experiences (1 = 10% positive experiences and 90% negative experiences … 9 = 90% positive experiences and 10% negative experiences). Details of the measures are presented below and in Supplementary Tables 2, 3 and 4 and Supplementary Fig. 1.
Mental health
Participants completed the Mental Health Test (MHT)10, which serves as an operationalised, comprehensive measure of the Maintainable Positive Mental Theory10. The MHT measures the five pillars of mental health based on psychological competencies and capacities.
The first pillar of the MPMHT is Global Well-being, which integrates existing well-being theories and encompasses multi-component subjective well-being in the emotional, psychological, social, and spiritual domains of life14,15,16. Table 1 outlines the pillars of Global Well-being and their relationship with self-regulation, savoring capacity, resilience, and creative and executive efficiency.
Savoring is the second pillar and refers to the ability to mentally relive pleasurable memories and experiences, generating mental well-being and extending it to future events14. Savoring is a necessary skill for MPMHT, as it contributes to achieving and maintaining positive mental health17.
The third pillar is Creative and Executive Efficiency, which enables individuals to cope with difficulties and challenges by mobilising their competencies in individual and social problem solving14,18.
The fourth pillar is Self-regulation, which is the ability to regulate and control temperament, emotions, and negative states while persevering to achieve a goal. This ability plays a crucial role in mental health and represents one of the most adaptive variables of human behaviour19,20,21.
Finally, Resilience is the fifth pillar and refers to an individual’s psychological ability to mobilise their resources and maintain positive mental health when faced with unexpected, stressful situations. The higher the level of resilience, the faster an individual can recover from such situations22,23,24.
According to the MPMHT, these five pillars are responsible for an individual’s mental health. The competencies associated with the five pillars can be trained, improved, and strengthened. As such, the pillars provide an easy-to-understand concept for psychiatric and non-clinical populations. First, it provides a structural model for assessing individual capacities and resources (personal sources of resilience, one’s own creativity and executive competencies, and sources of peer support and social connectedness).
Secondly, on the basis of this assessment, people living with mental disorder(s) can establish a balance with their own physical and mental status, as well as with the outside world, by promoting their development, creating a stable state for personal and social functioning (self-regulation) and an equilibrium of positive and negative emotions (coping, savoring). The mindful application of the five-pillar model, and therefore the presence and effective functioning of these elements, can improve mental and physical well-being and social functioning, increase the level of spiritual connectedness, and, through the maintenance/promotion) of mental and physical support and global functioning25.
Mental disorders
Participants completed the Symptom Checklist-90-Revised26 from which the General Severity Index was calculated and used to indicate mental disorders symptoms.
Criterion variables
The measures used were the Global Well-being Scale14; the PERMA-Profiler27; the Psychological Immune System Inventory, short form (PISI)18; the Psychological Well-being Scale28; the Satisfaction with Life Scale29; the Positivity Scale30. Finally, we included in the analysis those variables for which we received a sufficient number of completions in both samples. They are strongly correlated with the other criterion variables see Ref.10. In the case of the psychiatric sample, each respondent’s psychiatrist or clinical psychologist was asked to provide a paper report on the patient, including: (1) the name of the patient’s mental disorder(s) according to DSM-52 or ICD3 depending on the institution’s protocol; (2) the severity of symptoms; and (3) the patient’s pharmacotherapy.
Statistical processing
To estimate the connection of mental health competencies and mental disorders symptoms with the selected measures of well-being, we fit a series of ordinary least squares linear regression models31. Because we aim to explore and compare the relationship and strength of our main independent variables in a psychiatric and non-clinical setting, all models were fitted separately throughout our analysis for both samples and all well-being related dependent variables.
For both samples and all three dependent variables, we examine four (Table 2) increasingly complex models. M1 is our base model, which only consist of the main demographic and socio-economic control variables, namely age, gender, and educational attainment. M2 introduces the connection of the Mental Health Test’s mean scale with mental health competencies (MHC) as a continuous variable. Compared to M2, M3 introduces the mental disorders symptoms’ (MDS) mean scale also as a continuous variable. Finally, M4 measures whether there is a possible interaction effect between the two main dependent variables. As a supplementary analysis, we also analysed possible interactions between the control variables and the MHC and MDS but did not find any meaningful results (results available from authors).
To assess the goodness-of-fit of all models, we use four commonly employed measures. First, we compare models using the Bayesian Information Criterion (BIC)32 and Akaike Information Criterion (AIC)33 of M1–M4, which are measures of relative goodness-of-fit (smaller values indicating a better fit) that penalize the addition of unimportant predictor variables that increase model complexity without improving model performance. Next, we calculate the adjusted coefficient of determination (adjusted R-squared) of all models, where higher values indicate a better fit. Finally, we compare models using a series of ANOVA tests, where a significant (p < 0.05) result indicates that the more complex model fits our data better.
For all models, we test whether the model fulfils the OLS regression assumptions and check for the presence of outlier and/or influential observations which can possibly bias our results31. While some observations had to be removed (see removed N in tables) due to being outliers, diagnostic plots and relevant statistics indicate that all presented models are adequate (available from authors). In addition, as the MHC and MDS variables are prone to be correlated, the absence of multicollinearity in models M3 is especially important. We explore the issue using the commonly employed Variance Inflation Factors (VIF, values above 5 indicate potential issues), presented in Supplementary Table 5.
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