Background
Severe mental illnesses (SMI)—including schizophrenia, bipolar disorder, and unipolar depressive disorder—remain a profound public health concern. SMI is estimated to affect up to 6% of the population globally,1 and individuals with SMI experience a 15–20-year shorter lifespan compared to the general population, with markedly elevated all-cause mortality and premature death due to somatic comorbidities.2–6 This inequality stems from multifaceted contributors, among them disparities in healthcare provision.7 Integrated care – defined broadly as coordinated, person-centred services that bridge mental and physical health – offers a promising strategy to address this disparity in healthcare provision.8,9
Mental health has been a growing public health concern, particularly following the Covid 19 pandemic, with many studies noting a sharp rise in depression and anxiety.10,11 The latest Flash Eurobarometer on Mental Health revealed close to one in two respondents reporting experience of an emotional or psychological problem in the previous year.12 Longitudinal studies have found very high proportions of study populations being treated for mental illness across study periods.13–15 Against this backdrop of pervasive and growing experiences of mental illness and broader emotional distress, public knowledge and awareness remains underdeveloped, a situation which is exacerbated when it comes to SMI, with continued misconceptions and stigma surrounding many diagnoses, with media depictions continuing to play a likely role.16,17 Whilst reduced stigma in some areas may have promoted better awareness of depression and anxiety the broader issue of public knowledge of and stigma around mental health remains salient.18–21
The constellation of factors is challenging. On the one hand, people suffering from SMI are receiving substandard somatic care, and a potential policy solution exists in promoting integrated care. On the other, public misconceptions and persistent stigma may lead to reluctance to share healthcare spaces, in addition to providing an environment in which mental illness may continue to go unreported and thereby have a greater impact on those affected.22
The impact of misconceptions and stigma around mental illness have been well established.23,24 Stigma is a multidimensional and socially embedded process involving stereotyping, prejudice, social distancing, and discrimination, operating at individual, interpersonal, institutional, and societal levels. For people with mental illness, these processes may contribute to social exclusion, reduced opportunities in areas such as employment and housing, reluctance to disclose mental health problems or seek treatment, and poorer access to appropriate healthcare. As a result, there have been numerous campaigns to improve knowledge around mental health or Mental Health Literacy (MHL) since at least the 1980s,25,26 with continuing campaigns aimed at reducing stigma.16,17,27 Studies have shown varying but measurable success raising MHL through public awareness campaigns,17,28 and a reduction in some areas of stigma, although the evidence is inconclusive. There is a distinct lack of comparable longitudinal data, and it is even more challenging to connect reported mental health knowledge and attitudes with actual behaviour.29,30 Despite these difficulties, efforts have been made to develop standardised measures that have had their psychometric properties validated, from the extensively used Community Attitudes to Mental Illness (CAMI) scale25,31 to the Mental Health Literacy Scale (MHLS),32 Mental Health Knowledge Schedule (MAKS)33 and the Reported and Intended Behaviour Scale (RIBS).30
When it comes to SMI public knowledge may be further limited. Some studies have focused on schizophrenia and bipolar disorder, and some looked at public understanding of causes of schizophrenia,34 but in general MHL studies have remained at the overarching level of general mental health.29,35 A potential consequence has been a difficulty in linking specific negative attitudes to specific mental illnesses. For example, fear of violence has been linked to schizophrenia.36 This negative attitude could be the result of enduring misconceptions about schizophrenia and might be better addressed by improved education on specific mental illnesses and specifically on the effects of substance misuse, alongside challenging negative media depictions, rather than broad-brush mental health awareness campaigns.
Issues of stigma and public knowledge are also relevant in the acceptance of integrated care models for mental and physical illness. The developers of the CAMI scale situate the tool within a history of attempts to assess community acceptance of integration of those suffering from mental illness and their care services into the community.25 However, the CAMI scale is a broad measure of public attitudes and prejudices and not directly focused on the integration of services, which remains under investigated when it comes to public opinion. Considering that implementing integrated care models also requires public financial investment, assessing community support for integrated care for people with severe mental disorders is of increasing importance.
Given this situation, we set out to investigate public knowledge, attitudes and practices relating to SMI and integrated care across six European countries, alongside the public’s willingness to pay for a proposed new integrated care model assessed by a Discrete Choice Experiment (DCE).
Methods
Study design
Ethics approval for this study has been received from the research ethics committee of Aix-Marseille University (reference number: 2026-05-21-12). This study forms part of the research project EU-Mind (European Mental and physical health initiative for people with severe mental disorders). The project is a three-year, interdisciplinary collaboration spanning six European countries—Denmark, Finland, France, Italy, Poland, and Sweden. The project seeks to design and evaluate scalable, cross-national models of integrated care to improve the organization and quality of healthcare for people living with SMI and co-occurring physical health conditions. For the study at hand, an online survey was designed. The first part of the survey asks about citizens’ knowledge, attitudes, and practices (KAP) regarding severe mental disorders and their current management. The second part of the survey asks about the priorities associated with an integrated mental/somatic health care model via discrete choice experimentation (DCE). The DCE will enable us to estimate how much European citizens are willing to pay for different models of integrated care based on different attributes.
Population
Individuals aged ≥18 years, drawn from an existing market research panel by Norstat, will be contacted via email to complete the online survey (via computer, tablet or smartphone). No personally identifying data will be collected, and Norstat are fully GDPR compliant. The company will manage the implementation of the survey, with data collection lasting two to three weeks (depending on participation rates) to reach the desired number of respondents. The study panel is recruited, not an opt-in panel, and over half of the panellists are probability samples obtained through Computer Assisted Telephone Interviews, with the remainder recruited online. Sampling will be stratified to be representative of age, sex, education level and household income, with repeated recruitment rounds until the target proportions are reached. All participants will provide electronic consent before participating in the online survey.
Sample size
Based on statistical tables presenting the different sizes of samples according to target population’s size (N > 100,000) – taking a margin of error of 5% and confidence level of 99%, at minima 700 individuals will be required per country.37 To improve the statistical quality of our results, we will target 1,500 valid questionnaires for each country, with response rates and data-collection handled by Norstat.
Knowledge, attitudes and practices survey
KAP instrument
Following a literature search of PubMed and ScienceDirect, supplemented by backward and forward citation searching of relevant articles, conducted between May and October 2025, we identified the principal existing measures of knowledge, attitudes, and practices in mental health – the most relevant, recent and validated measures being the MHLS,32 the MAKS33 and the RIBS,30 alongside the longstanding and extensively used CAMI scale.25,31 We also referenced the 2023 Eurobarometer survey on mental health.12 As our study focused on three types of severe mental illness, schizophrenia, bipolar disorder and unipolar depressive disorder, we developed a new questionnaire informed by this literature review. The questionnaire is comprised of 15 knowledge, 15 attitude and 8 behaviour focused questions. We adapted a ‘depth-of-exposure’ question from the United States General Social Survey.38 The final part of the survey is comprised of the discrete choice experiment section.
KAP indicators
Knowledge indicators will be constructed based on responses to the knowledge section of the questionnaire. Participants will first be asked to select the conditions they consider to be severe mental illnesses. An indicator will be constructed based on the number of correctly identified conditions, with a maximum of three, and the number of incorrectly selected confounders, with a maximum of five. After this initial assessment of respondents’ unaided understanding, the questionnaire will specify that schizophrenia, bipolar disorder, and major depressive disorder are the severe mental illnesses addressed in the survey. Providing this common definition is intended to ensure that responses to the subsequent knowledge, attitude, and practice items are based on a shared understanding of the conditions under consideration. Based on this information, respondents are then asked to estimate the prevalence of SMI in their country, and to identify symptoms for each of the relevant illnesses – schizophrenia, bipolar and major depressive disorder. For each illness three correct symptoms and three confounder symptoms will be listed, and a knowledge score constructed based on responses. Subsequent questions will address general knowledge about SMI, in which five-point scale responses will form the basis of the composite knowledge indicator.
Attitudes will be assessed through respondents’ beliefs and emotional reactions to hypothetical situations involving people with severe mental illness. Fifteen attitude items were developed based on themes identified in established mental health stigma and attitude measures, particularly the Community Attitudes to Mental Illness scale, subsequently adapted to reflect the study’s specific focus on severe mental illness and integrated mental and physical healthcare. Items were reviewed to ensure contemporary, relevant, and non-stigmatising wording. Responses will be rated on a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.
Practices will be addressed as willingness to engage in particular actions regarding someone with a SMI, as reported by the respondent. A composite indicator scale of positive to negative attitude will be constructed based on responses to the attitudes and behaviour questions.
The questionnaire will include a final section, adapted from the US General Social Survey, addressing the depth and quality of exposure of the respondent to friends or acquaintances whom they know to suffer from SMI (proximity of contact, frequency of contact, and positive or negative effects of contact).
The questionnaire is available in the extended data to this protocol.
The discrete choice experiment
The discrete choice experiment (DCE) was formulated in accordance with the DIRECT checklist for DCEs developed by Ride et al.39
Attribute selection
DCE methodology in healthcare holds that a healthcare model can have multiple attributes, with multiple levels within each attribute. The DCE combines different levels of different model attributes in unique combinations/scenarios and asks respondents to make choices about their preferences between various hypothetical scenarios.40–42
In our study these attributes will relate to a new integrated care model for people with SMI, the delineation of which is one of the aims of the broader EU-Mind project. The defining attributes of this model of care, alongside the different attribute levels, will be derived from a Delphi consensus study also nested in the EU-MIND project.43 Envisaged examples of attributes for an integrated care model are as follows: Location of Care and the Level of Integration, Access & Convenience, Care Team & Coordination, Principal Objective of the Care Pathway, Physical Health Monitoring, Involvement in Care, User Fees, and Costs to the healthcare system. We will generate a discrete choice questionnaire based on the final list of attributes and levels relevant to somatic and mental care that emerge from the Delphi panel. Given that healthcare funding and payments at point of delivery vary across our surveyed countries, we will inform participants that the ‘costs to the healthcare system’ attribute is designed to assess perceived value.
Attributes’ levels
The attribute levels will be selected based on the Delphi results. To ensure clarity and reduce respondent burden, the full set of choice scenarios will be divided into smaller, distinct blocks. Each participant will be assigned just one block to evaluate. Data on socio-demographic characteristics of respondents will be also collected, namely: gender, age, educational level, and household income.
Pilot study
For a qualitative usability and comprehension test of the KAP and DCE survey, we will conduct a small pilot study involving a convenience sample of 20 people in France. Participants will provide written or verbal feedback on ease of comprehension and cognitive burden on the French version of the questionnaire. Necessary changes will be made according to pilot participants’ feedback. The survey will then be translated from French into target languages using AI, subsequently checked by bilingual specialists in the field of psychiatry, following suggested best practice in this emerging field.44 We envisage only minor adjustments on the subsequent 6 language versions. Norstat will then conduct their own initial test launch of the questionnaire of an estimated 200 respondents to assess usability and comprehension.
Statistical analysis
Descriptive analysis
Sociodemographic data, KAP answers, DCE priorities and price estimates will be presented descriptively as means, medians, or proportions and will be stratified by gender and country. Parametric and non-parametric statistics will be used to compare knowledge, attitudes, practices and maximum price differences for an integrated care model between countries and respondents’ profile, using either analyses of variances for continuous variables or the Chi Squared test (or the Fisher exact test) for qualitative measures.
KAP analysis
Answers to the Knowledge questions will be presented descriptively as the means or proportions and compared using either analyses of variances for continuous variables or the Chi Squared test (or the Fisher exact test) for qualitative measures. Within these questions, and when necessary, responses originally collected on a five-point Likert scale will be converted into binary format for secondary or sensitivity analysis (with “good” or “poor” knowledge).
Data collected in the attitudes section will be used to construct a composite indicator such as “Positive attitude” or “Negative attitude”, by use of multiple correspondence analysis (MCA). MCA is an exploratory method used to summarize a complex data set comprised of qualitative variables into simplified dimensions.45 Subsequently, a hierarchical clustering approach will be used to create an attitude indicator. Finally, a generalized linear model for clustered (on country) samples will be used to probe the associations between participants with opposing attitudes and country of citizenship, adjusted for sociodemographic characteristics.
DCE analysis
Prior to analysis, response quality will be assessed. The DCE will include an attention-check DCE choice card presenting a scenario with one obviously correct choice. In addition, routine left or right selections will be considered indicators of poor quality responses. The proportion of respondents with poor quality responses will be calculated, and subsequent analysis will be based on two cases: all respondents, and only respondents with good quality answers.
The preferences for the attributes will be determined with a multinomial logistic regression model (MNL) for panel data with the choice of the respondents as the dependent variable and the attributes as independent variables.39,46 Model estimates will be derived from dummy-coded effect attributes. The relative importance of each attribute will be determined by country using profile-based normalization.47 We will also introduce random effects in the multinomial logit model using mixed MNLs, which provide more accurate estimates of coefficients when responses from the same patients are correlated.48,49 Confidence intervals for revealed price (“utility”) will be estimated for each attribute. We will also carry out preplanned assessments of statistical interactions between attributes and demographic characteristics or KAP indicator results to examine whether particular characteristics would be associated with attribute preferences.
All analyses will be conducted with the R Foundation for Statistical Computing, version 4.6.0,50 using ‘Apollo’, ‘FactoMineR’,‘Cluster’, and ‘lmer’ among other packages.
Discussion
This study will provide cross-national evidence on public knowledge, attitudes, and intended practices regarding severe mental illness, as well as on citizens’ preferences and willingness to pay for integrated mental and physical healthcare. By combining a KAP survey with a discrete choice experiment across six European countries, the study is expected to provide insight not only into public understanding and stigma, but also into which features of integrated care are most strongly valued and supported. These findings may help inform the design and implementation of integrated care models that are both clinically relevant and societally acceptable.
The discrete choice experiment was selected because one of the principal aims of the study is to examine how European citizens value different possible configurations of integrated care. Compared with contingent valuation, which typically requires respondents to assess a clearly defined and fully specified intervention, the DCE approach allows preferences to be elicited for individual attributes and levels of a care model that had not yet been finalised at the time of study design.51,52 This makes it possible to estimate the relative importance of different components of integrated care and the trade-offs respondents are willing to make between them. The use of structured and visually presented choice scenarios is also well suited to an online survey format. DCE methods are increasingly used in mental health research to assess preferences for services, policies, and models of care.49,53–58
For the KAP we chose to develop our own questions. Although there is an obvious benefit to using scales that have already had their psychometric properties assessed, and to reuse established questionnaires to provide longitudinally comparable data, several limitations made this approach not fully suitable for the present study. Principally, differences in study aims, target populations, and methodological approaches have limited standardisation across studies, contributing to the proliferation of measurement instruments. This is also the case with our study, which focuses on the burdens of SMI and the issue of integrated care. Our study also had the dual aims of assessing public knowledge of SMI and attitudes towards people with SMI. For this reason, we included one question in which respondents selected what they considered to be SMI from a list of eight mental health conditions. We then informed respondents of the three conditions considered within our survey – schizophrenia, bipolar and unipolar depression (referred to as Major Depression in the survey, considered more comprehensible to the public). We adapted the behaviour (practices) questions from the existing MHLS, to refer to severe mental illness and developed additional general knowledge questions to target common misconceptions addressed in other scales, adjusted for our focus on severe mental illness. We then adapted attitude questions, drawing on CAMI themes, to target both severe mental illness and the issue of integrated care. Exposure is understood to affect attitudes, and in order to improve the quality of this factor we adopted a shortened version of the exposure question used in the US General Social Survey.38
Expectations
Broadly we expect that exposure will correlate with better knowledge and more positive attitudes, albeit not uniformly,38 and that better knowledge will correlate with more positive attitudes. We also assume positive attitudes will translate into more positive behavioural inclinations. Lastly, we assume that, in general, better knowledge and positive attitudes will translate into a higher valuation of a new integrated care model. Within the DCE, we expect that those who select a high cost model will do so for greater intended benefit in terms of life expectancy.
Strengths and limitations
Online surveys are becoming increasingly popular as a cost-effective means of targeting large numbers of people. As access to the internet has become widespread, both via computers and smartphones, such measures have become more viable in terms of representativeness. However, the downsides to internet surveys persist. Access is mediated through market-research companies who curate panels of respondents, to varying degrees of rigour. Despite efforts to ensure panel quality, it remains difficult to identify and exclude highly experienced or frequent survey participants who may circumvent standard screening and quality-control procedures. Such respondents may participate primarily for financial incentives and may provide less attentive or less reliable responses, potentially affecting data quality.59 This means that any data obtained in this way must be interpreted with due consideration to its limitations. With that said, it is increasingly a part of the social and scientific landscape and has become a valuable tool.
Although older meta-analyses suggest that online surveys enhanced participants perceptions of anonymity compared to interviews, thereby reducing social-desirability bias,60 this has been questioned,61 leaving this potential benefit uncertain. While online surveys provide flexibility to respondents in terms of time and place of completion, they probably tend to exclude people from areas with incomplete coverage, elderly people and low income households.62
Traditional back-translation processes prove costly and time consuming, and recent advances in the quality of AI translation suggest that the rigour of these traditional approaches does not offer a significant difference in quality.44 However, translation difficulties remain an issue, particularly regarding more specialist language relating to severe mental illness and integrated care.
Combining the KAP and DCE components in a single questionnaire may increase respondent burden, particularly because the survey addresses complex and potentially unfamiliar topics, including severe mental illness, healthcare pathways, and integrated care. The length and complexity of the questionnaire may lead to respondent fatigue, reduced attention, incomplete responses, or attrition before completion. We hope that the potential benefit of a snapshot of public knowledge and opinion on these difficult topics will balance out the potential for low-quality responses due to respondent fatigue or the complexity or relatively specialist nature of the subject matter.
Our study does not use mental health KAP measures that have had their psychometric properties extensively validated. However, as mentioned, this was not possible considering the specific focus of this study.
Conclusion
This study will provide cross-national evidence on public knowledge, attitudes, and intended practices regarding severe mental illness, as well as on citizens’ preferences and willingness to pay for integrated mental and physical healthcare. By identifying which components of integrated care are most strongly valued, how preferences vary across countries and population groups, and how these preferences relate to knowledge and attitudes, the study will generate evidence to inform the design, prioritisation, and implementation of future integrated care models. The findings may also support policymakers in developing care models that are not only clinically appropriate, but also publicly acceptable and aligned with societal priorities.
Data availability
No data are associated with this article.
Acknowledgements
The authors are grateful to the other EU-MIND consortium collaborators who provided help for this work. They include: Laurent Boyer, Alicja Bukowska, Ewelina Cichoń, Guillaume Fond, Eloi Fournier, Coralie Gandré, Enrico Ginelli, Tomasz Gondek, Tapio T. Gustafsson, Aleksi Hamina, Ulker Isayeva, Søren Paaske Johnsen, Andrzej Kiejna, Peter Lindgren, Pierre-Michel Llorca, Sandrine Loubière, Mirko Manchia, Ellenor Mittendorfer-Rutz, René Ernst Nielsen, Veronica Orleans, Pasquale Paribello, Vanessa Pauly, Masoud Rahmati, Esben Andersen Revsbech, Heidi Taipale, Owen Taylor, Jari Tiihonen, Agata Todzia-Kornaś, Esther Touitou-Burckard, Jan Brink Valentin, Mariusz Zięba.
References
- 1.
National institute of mental health:
Mental illness.2023.
Reference Source
- 2.
Gustafsson TT, Pauly V, Hamina A, et al.:
Cause-specific mortality due to physical illness in severe mental disorders in Europe: a population-based multi-country cohort study.
Lancet Public Health.
2026 Aug; 11(8): e506–e517. PubMed Abstract
| Publisher Full Text
- 3.
McGinty E, Baller J, Azrin S, et al.:
Quality of Medical Care for Persons with Serious Mental Illness: A Comprehensive Review.
Schizophr. Res.
2015 Jul; 165(0): 227–235. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 4.
Coldefy M, Gandré C:
Persons with Severe Mental Disorders: Greatly Reduced Life Expectancy and Fourfold Premature Mortality.
Issues Health Econ.
2018; 237: 1–7.
Reference Source
- 5.
Gandré C, Coldefy M:
Disparities in the Use of General Somatic Care among Individuals Treated for Severe Mental Disorders and the General Population in France.
Int. J. Environ. Res. Public Health.
2020; 17(10). PubMed Abstract
| Publisher Full Text
| Free Full Text
- 6.
Saha S, Chant D, McGrath J:
A systematic review of mortality in schizophrenia: is the differential mortality gap worsening over time?
Arch. Gen. Psychiatry.
2007 Oct; 64(10): 1123–1131. PubMed Abstract
| Publisher Full Text
- 7.
Lawrence D, Kisely S:
Inequalities in healthcare provision for people with severe mental illness.
J. Psychopharmacol. Oxf. Engl.
2010 Nov; 24: 61–68. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 8.
Isayeva U, Touitou-Burckard E, Bukowska A, et al.:
Integrated care programmes for severe mental illnesses and comorbid physical health disorders: the EU-MIND umbrella review to inform future actions in Europe.
EClinicalMedicine.
2026 Aug; 98: 104105. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 9.
Bartels SJ, DiMilia PR, Fortuna KL, et al.:
Integrated Care for Older Adults with Serious Mental Illness and Medical Comorbidity: Evidence-Based Models and Future Research Directions.
Clin. Geriatr. Med.
2020 May; 36(2): 341–352. PubMed Abstract
| Publisher Full Text
- 10.
World Mental Health Report:
Transforming Mental Health for All.
1st ed.Geneva:
World Health Organization;
2022; 1.
Reference Source
- 11.
Xiong J, Lipsitz O, Nasri F, et al.:
Impact of COVID-19 pandemic on mental health in the general population: A systematic review.
J. Affect. Disord.
2020 Dec; 277(277): 55–64. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 12.
European Commission:
Directorate General for Health and Food Safety., Ipsos European Public Affairs. Mental health: report.LU:
Publications Office;
2023. [cited 2025 Aug 25].
Reference Source
- 13.
Caspi A, Houts RM, Ambler A, et al.:
Longitudinal Assessment of Mental Health Disorders and Comorbidities Across 4 Decades Among Participants in the Dunedin Birth Cohort Study.
JAMA Netw. Open.
2020 Apr 21; 3(4): e203221. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 14.
Schaefer JD, Caspi A, Belsky DW, et al.:
Enduring mental health: Prevalence and prediction.
J. Abnorm. Psychol.
2017 Feb; 126(2): 212–224. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 15.
Kessing LV, Ziersen SC, Caspi A, et al.:
Lifetime Incidence of Treated Mental Health Disorders and Psychotropic Drug Prescriptions and Associated Socioeconomic Functioning.
JAMA Psychiatry.
2023 Oct 1; 80(10): 1000–1008. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 16.
Ciciurkaite G, Pescosolido BA:
Mental Health Literacy and Public Stigma: Examining the Link in 17 Countries.
Med. Res. Arch.
2024 Jul; 12(7). PubMed Abstract
| Publisher Full Text
| Free Full Text
- 17.
Gaschet MAP, Suvalo O, Klymchuk V:
Mental health stigma in Ukraine over time: A cross-sectional study.
Glob. Ment. Health Camb. Engl.
2025; 12: e49. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 18.
Ahad AA, Sanchez-Gonzalez M, Junquera P:
Understanding and Addressing Mental Health Stigma Across Cultures for Improving Psychiatric Care: A Narrative Review.
Cureus.
2023; 15(5): e39549. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 19.
Amsalem D, Jankowski SE, Pagdon S, et al.:
Stigma Reduction Via Brief Video Interventions: Comparing Presentations by an Actor Versus a Person With Lived Experience.
Psychiatr. Serv.
2024 May 1. Washington, DC. Publisher Full Text
- 20.
Prizeman K, Weinstein N, McCabe C:
Effects of mental health stigma on loneliness, social isolation, and relationships in young people with depression symptoms.
BMC Psychiatry.
2023 Jul 21; 23(1): 527. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 21.
Song N, Hugh-Jones S, West RM, et al.:
The effectiveness of anti-stigma interventions for reducing mental health stigma in young people: A systematic review and meta-analysis.
Camb. Prisms Glob. Ment. Health.
2023 Jan; 10: e39. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 22.
Correll CU, Galling B, Pawar A, et al.:
Comparison of Early Intervention Services vs Treatment as Usual for Early-Phase Psychosis: A Systematic Review, Meta-analysis, and Meta-regression.
JAMA Psychiatry.
2018 Jun 1; 75(6): 555–565. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 23.
Pescosolido BA, Martin JK:
The Stigma Complex.
Annu. Rev. Sociol.
2015 Aug; 41: 87–116. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 24.
Sartorius N:
Stigma and mental health.
Lancet.
2007 Sep 8; 370(9590): 810–811. PubMed Abstract
| Publisher Full Text
- 25.
Taylor SM, Dear MJ:
Scaling Community Attitudes Toward the Mentally Ill.
Schizophr. Bull.
1981 Jan 1; 7(2): 225–240. Publisher Full Text
- 26.
Jorm AF, Korten AE, Jacomb PA, et al.:
‘Mental health literacy’: a survey of the public’s ability to recognise mental disorders and their beliefs about the effectiveness of treatment.
Med. J. Aust.
1997 Feb 17; 166(4): 182–186. PubMed Abstract
| Publisher Full Text
- 27.
Dias P, Campos L, Almeida H, et al.:
Mental Health Literacy in Young Adults: Adaptation and Psychometric Properties of the Mental Health Literacy Questionnaire.
Int. J. Environ. Res. Public Health.
2018 Jul; 15(7): 1318. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 28.
Hansson L, Stjernswärd S, Svensson B:
Changes in attitudes, intended behaviour, and mental health literacy in the Swedish population 2009-2014: an evaluation of a national antistigma programme.
Acta Psychiatr. Scand.
2016 Aug; 134(Suppl 446): 71–79. PubMed Abstract
| Publisher Full Text
- 29.
Angermeyer MC, Dietrich S:
Public beliefs about and attitudes towards people with mental illness: a review of population studies.
Acta Psychiatr. Scand.
2006 Mar; 113(3): 163–179. Publisher Full Text
- 30.
Evans-Lacko S, Rose D, Little K, et al.:
Development and psychometric properties of the Reported and Intended Behaviour Scale (RIBS): a stigma-related behaviour measure.
Epidemiol. Psychiatr. Sci.
2011 Sep; 20(3): 263–271. Publisher Full Text
- 31.
Sanabria-Mazo JP, Doval E, Bernadàs A, et al.:
Over 40 years (1981–2023) assessing stigma with the Community Attitudes to Mental Illness (CAMI) scale: a systematic review of its psychometric properties.
Syst. Rev.
2023 Apr 14; 12(1): 66. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 32.
O’Connor M, Casey L:
The Mental Health Literacy Scale (MHLS): A new scale-based measure of mental health literacy.
Psychiatry Res.
2015 Sep 30; 229(1): 511–516. PubMed Abstract
| Publisher Full Text
- 33.
Evans-Lacko S, Little K, Meltzer H, et al.:
Development and Psychometric Properties of the Mental Health Knowledge Schedule.
Can. J. Psychiatr.
2010 Jul; 55(7): 440–448. PubMed Abstract
| Publisher Full Text
- 34.
Angermeyer MC, Matschinger H:
Lay beliefs about schizophrenic disorder: the results of a population survey in Germany.
Acta Psychiatr. Scand.
1994; 89(s382): 39–45. Publisher Full Text
- 35.
Wei Y, McGrath PJ, Hayden J, et al.:
Measurement properties of tools measuring mental health knowledge: a systematic review.
BMC Psychiatry.
2016 Aug 23; 16(1): 297. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 36.
Durand-Zaleski I, Scott J, Rouillon F, et al.:
A first national survey of knowledge, attitudes and behaviours towards schizophrenia, bipolar disorders and autism in France.
BMC Psychiatry.
2012 Dec; 12(1): 128. Publisher Full Text
- 37.
Bartlet JE, Kotrlik JW, Higgins C:
Organizational Research: Determining Appropriate Sample Size in Survey Research.
Inf. Technol. Learn. Perform. J.
2001; 19(1).
- 38.
Perry B, Felix E, Bolton M, et al.:
Public Stigma and Personal Networks: Confronting the Limitations of Unidimensional Measures of Social Contact.
J. Health Soc. Behav.
2022; 63(3): 428–445. PubMed Abstract
| Publisher Full Text
- 39.
Ride J, Goranitis I, Meng Y, et al.:
A Reporting Checklist for Discrete Choice Experiments in Health: The DIRECT Checklist.
PharmacoEconomics.
2024 Oct 1; 42(10): 1161–1175. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 40.
Phillips KA, Johnson FR, Maddala T:
Measuring What People Value: A Comparison of “Attitude” and “Preference” Surveys.
Health Serv. Res.
2002 Dec; 37(6): 1659–1679. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 41.
McGrady ME, Pai ALH, Prosser LA:
Using Discrete Choice Experiments to Develop and Deliver Patient-Centered Psychological Interventions: A Systematic Review.
Health Psychol. Rev.
2021 Jun; 15(2): 314–332. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 42.
Henderson J, Hawke LD, Iyer SN, et al.:
Youth Perspectives on Integrated Youth Services: A Discrete Choice Conjoint Experiment.
Can. J. Psychiatr.
2022 Jul 1; 67(7): 524–533. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 43.
Touitou-Burckard E, Gondek T, Isayeva U, et al.:
Protocol for a Delphi consensus study to identify priority characteristics of integrated care for individuals with severe mental illness and comorbid physical disorders in Europe.
PLoS One.
2026 Jul 9; 21(7): e0352089. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 44.
Kunst JR, Bierwiaczonek K:
Utilizing AI questionnaire translations in cross-cultural and intercultural research: Insights and recommendations.
Int. J. Intercult. Relat.
2023 Nov; 97(97): 101888. Publisher Full Text
- 45.
Greenacre M:
Correspondence analysis in medical research.
Stat. Methods Med. Res.
1992; 1(1): 97–117. PubMed Abstract
| Publisher Full Text
- 46.
Bridges JFP, Hauber AB, Marshall D, et al.:
Conjoint Analysis Applications in Health—a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force.
Value Health.
2011 Jun 1; 14(4): 403–413. PubMed Abstract
| Publisher Full Text
- 47.
Gonzalez JM:
A Guide to Measuring and Interpreting Attribute Importance.
Patient - Patient-Centered Outcomes Res.
2019 Jun 1; 12(3): 287–295. Publisher Full Text
- 48.
Boxebeld S, Mouter N, van Exel J :
Public preferences for skin cancer prevention policies: a discrete choice experiment in three European countries.
Soc. Sci. Med.
2025 Aug; 378: 118155. PubMed Abstract
| Publisher Full Text
- 49.
Ride J, Lancsar E, Ozols I:
The Role of Mental Health in Healthcare Choices: A Discrete Choice Experiment Examining Preferences for Primary Care.
Value Health.
2025 Jul 1; 28(7): 1100–1109. PubMed Abstract
| Publisher Full Text
- 50.
R Core Team:
R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Vienna, Austria;
2013.
Reference Source
- 51.
Quaife M, Terris-Prestholt F, Di Tanna GL, et al.:
How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity.
Eur. J. Health Econ.
2018 Nov; 19(8): 1053–1066. PubMed Abstract
| Publisher Full Text
- 52.
Soekhai V, de Bekker-Grob EW, Ellis AR, et al.:
Discrete Choice Experiments in Health Economics: Past, Present and Future.
PharmacoEconomics.
2019 Feb; 37(2): 201–226. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 53.
Lorant V, Nicaise P, Silva Ribeiro W, et al.:
The relative value of mental health for pandemic response: A discrete choice experiment with stakeholders and the general population in Europe.
Soc. Sci. Med.
2026 May; 396: 119067. PubMed Abstract
| Publisher Full Text
- 54.
Walsh S, O’Shea E, Pierse T, et al.:
Public preferences for home care services for people with dementia: A discrete choice experiment on personhood.
Soc. Sci. Med.
2020 Jan; 245: 112675. PubMed Abstract
| Publisher Full Text
- 55.
Teahan Á, Walsh S, Doherty E, et al.:
Supporting family carers of people with dementia: A discrete choice experiment of public preferences.
Soc. Sci. Med.
2021 Oct; 287: 114359. PubMed Abstract
| Publisher Full Text
- 56.
Morris S, Massou E, Magnusson J, et al.:
Integration of specialised services for eating disorders and functional symptom disorders in children and young people: discrete choice experiments and qualitative study.
Health Soc. Care Deliv. Res.
2025 Dec 17; 13: 1–19. PubMed Abstract
| Publisher Full Text
- 57.
Schellings R, Essers BAB, Kessels AG, et al.:
The development of quality indicators in mental healthcare: a discrete choice experiment.
BMC Psychiatry.
2012 Aug; 12(12): 103. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 58.
van Dijk P, Schellings R, Essers BAB, et al.:
A discrete choice experiment to identify the most efficient quality indicators for the supervision of psychiatric hospitals.
BMC Health Serv. Res.
2020 Mar; 20(20): 192. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 59.
Mercer A, Lau A:
Comparing Two Types of Online Survey Samples.2023.
- 60.
Richman WL, Kiesler S, Weisband S, et al.:
A meta-analytic study of social desirability distortion in computer-administered questionnaires, traditional questionnaires, and interviews.
J. Appl. Psychol.
1999 Oct; 84(5): 754–775. Publisher Full Text
- 61.
Gnambs T, Kaspar K:
Socially Desirable Responding in Web-Based Questionnaires: A Meta-Analytic Review of the Candor Hypothesis.
Assessment.
2017 Sep 1; 24(6): 746–762. PubMed Abstract
| Publisher Full Text
- 62.
Kelfve S, Kivi M, Johansson B, et al.:
Going web or staying paper? The use of web-surveys among older people.
BMC Med. Res. Methodol.
2020 Oct 8; 20(1): 252. PubMed Abstract
| Publisher Full Text
| Free Full Text
- 63.
Taylor O:
EU-Mind KAP-DCE Questionnaire English Draft.
Zenodo Online Repos.
2026 Aug 31. Publisher Full Text
