Psychometric properties of the Armenian and Georgian versions of the PHQ-9, GAD-7, and WHO-5 Well-Being Index

Psychometric properties of the Armenian and Georgian versions of the PHQ-9, GAD-7, and WHO-5 Well-Being Index | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychometric properties of the Armenian and Georgian versions of the PHQ-9, GAD-7, and WHO-5 Well-Being Index Vicente Arrona, Jesús Godino-Cruz, Roberto Mediavilla, Ana M. Tijerino-Inestroza, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8521613/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Depressive and anxiety disorders are highly prevalent on healthcare personnel. A survey was conducted in Armenia, Georgia, Moldova, and Ukraine to map their mental health as an extension of a previous study to improve sustainability of health systems. The Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder scale (GAD-7), and the WHO 5-item well-being index (WHO-5) were used as screening tools. These were not available nor validated in Armenian nor Georgian. Assessment of their psychometric properties is needed to validate their applicability. Methods We did a cross-sectional study. Translations coordinated by the WHO Regional and Country Offices were done. A pilot study helped identify translation errors. The survey was open for three months, results were used to test for internal consistency, construct validity, discriminant validity, and measurement invariance. We analysed 3,353 valid responses. Results Analyses indicated overall good internal consistency with Cronbach’s α and McDonald’s ω between 0.8–0.9. The factor loadings ranged between 0.38 and 0.76, and the CFI, TLI, RMSEA, and SRMR indices were appropriate even through measurement invariance tests. The strong positive correlation (r = 0.75–0.78) between the PHQ-9 and GAD-7 scores suggested convergent validity, whereas a negative correlation (r = − 0.59 to -0.51) between these and the WHO-5 score indicated divergent validity. Conclusions Applicability of the PHQ-9, GAD-7 and WHO-5 in Armenia and Georgia was proved. These need adequate translation and assessment for use in the clinical practice. Two main limitations were present, the sample comprised only healthcare professionals, and no validation against a gold standard was conducted. Psychometrics Armenia Georgia (Republic) Surveys and questionnaires Health personnel. Background Mental health has emerged as a critical public health priority, with especially growing recognition of its impact on individuals and societies after the COVID-19 pandemic. Depressive and anxiety disorders are highly prevalent and represent significant burden across the life course, with approximately 4% of the global population having an anxiety disorder, and more than 6% of the European population living with a depressive disorder ( 1 , 2 ). While less has been done in this area to understand prevalence among healthcare workers (HCWs), some studies suggest that the prevalence for this group is estimated to be even greater, as one in four reported symptoms compatible with a depressive or anxiety disorder in 2020 ( 3 , 4 ). This represents a high burden, as these conditions have seen the biggest increases of Disability-Adjusted Life Years (DALYs) during recent years (2010–2023)( 5 ). Reports on the mentioned findings have shown that the previous trends have not changed and have even risen in some regions ( 6 , 7 ). The risk of several conditions in various countries was assessed in one study, including mood and anxiety disorders, finding increased risk after COVID-19 ( 8 ). This suggests that mental health disorders’ symptomatology did not lower after the pandemic peak, which is considered an important stressor for this sector. This, along with other factors, has led to a health workforce crisis in Europe ( 9 – 11 ) as healthcare systems face personnel shortages, exacerbating the situation. Also, an increased demand for health services overwhelms health systems’ capacity, exacerbating the situation ( 12 , 13 ). Improving the retention of health workers has become a central strategy to address the health workforce crisis in Europe ( 9 , 14 ). Enhancing the mental health and well-being of health workers is a critical component of strengthening retention efforts. In October 2024, the World Health Organization (WHO) Regional Office for Europe conducted a web-based survey of doctors and nurses from the European Union, Iceland, and Norway. The objective was to map the mental health situation of this workforce and develop policy guidance to better protect mental health of HCWs ( 6 , 7 ). The questionnaire includes the 9-item Patient Health Questionnaire (PHQ-9), the 7-item anxiety scale (GAD-7), and the WHO-5 well-being index (WHO-5). In September 2024, The WHO Regional Office for Europe signed a contribution agreement with the European Commission at the Directorate-General for Neighbourhood and Enlargement Negotiations (DG ENEST) to implement the WHO component “Supporting Resilience to Health Emergencies in the Eastern Partnership” ( 15 ). Countries in the DG ENEST agreed to get a similar survey of that in EU and this was added in the outputs of the collaboration agreement. It aims at enhancing health systems in Eastern Europe, strengthening health workforce capacities and resilience to ensure quality health services. In May 2025, the survey was implemented in Armenia, Georgia, the Republic of Moldova, and Ukraine as an extension to the original study. One of the challenges was adapting the mentioned screening tools to the official languages of these countries. Ukraine and Moldova have validated translations, but this was not the case for Armenia and Georgia, so adaptations should also be validated psychometrically to ensure their applicability. No studies have explored this before, so we found it worthwhile to use our data for an initial approach. The objective of this study was to analyse the validity of the Armenian and Georgian translations of the PHQ-9, GAD-7 and WHO-5 screening tools through their psychometric properties. We intend to extend the availability of these in clinical practice to assess mental health conditions. Methods Overview The screening tools were back translated by qualified translators and tests were run on a small pilot group of health personnel for feedback before launch. A web-based survey was disseminated through Ministries of Health (MoH) in each country as part of a cross-sectional study conducted in Armenia, Georgia, Moldova, and Ukraine. A STROBE checklist is provided in the Supplementary Material for results’ reporting ( 16 ). After finalizing the data collection process from the survey, psychometric analyses were conducted to explore internal consistency, factorial structure, validity, and invariance. Translation We used back-translation ( 17 ) using the original English versions ( 18 , 19 ). The final translated instruments and details on the process can be found in the Supplementary Material. The questions in both versions of the questionnaire follow a similar structure to measure the symptoms of mental health problems and well-being in a way that is adapted to the context of healthcare workers. Each WHO Country Office coordinated the national dissemination strategy with MoH. They monitored the translation process performed by professional translators through a master file with instructions for each step. After confirming the final versions, a health professional reviewed that the tools were clear for use. The WHO Regional Office for Europe along with the WHO Collaborating Centre for Mental Health Services Research and Training of the Autonomous University of Madrid reviewed adequate completion of files before implementation. The WHO Country Offices were asked to share the survey with at least four doctors and four nurses from their regions for assessment. A total of 11 doctors and nurses from Armenia and 31 from Georgia tested the platform to check functionality and translation of each item and the full questionnaire. After testing by local HCWs, they gave feedback to the Country Offices and the WHO Collaborating Centre to address modifications. These checked by the country offices one last time before the survey was launched to ensure clarity and appropriateness to each language. The final decision was undertaken by Country Offices and the WHO Regional Office. Population and settings The survey was launched on the 20th of May 2025 and collected answers from doctors and nurses from Armenia, Georgia, Moldova and Ukraine until the 16th of September 2025. The study design was cross-sectional. We reached out to participants by convenience sampling, through MoH supported by national professional associations. During the study, digital communications were sent to health facilities and from national associations to personal e-mails of their members. We provided a link to the web platform, where they could choose their local language or English to participate. We retrieved more than 31,000 responses. Among these, 8,000 were participants from Armenia and Georgia, where a final sample of 3,353 respondents (Armenia: 1,388; Georgia: 1,965) resulted after invalid observations were excluded. HCWs who were licenced doctors or nurses composed the target population; no other health professionals were included, surveys with > 90% missing values were excluded. These had to be actively working in the country’s health system, and participants with incomplete screening tools were excluded as well. The health workforce is composed mainly of female professionals, which is reflected in our sample, consisting predominantly of this subgroup (90%), whereas professional categories are balanced, with doctors representing 54% of participants. Age is somewhat normally distributed, with groups of 41–45 and 51–55 years being the most common and representing 12.9% and 13.1%, respectively. We found < 1% of missing values except for the household composition in Armenia (1.3%). More information is provided in Table 1 . Table 1 Sociodemographic characteristics of the sample Characteristic Armenia N = 1 388 1 Georgia N = 1 965 1 Age group Less than 20 years old 2 (0·1%) 3 (0·2%) 20–25 67 (4·8%) 63 (3·2%) 26–30 145 (10%) 51 (2·6%) 31–35 206 (15%) 95 (4·8%) 36–40 225 (16%) 145 (7·4%) 41–45 230 (17%) 200 (10%) 46–50 132 (9·5%) 238 (12%) 51–55 147 (11%) 292 (15%) 56–60 93 (6·7%) 258 (13%) 61–65 102 (7·4%) 287 (15%) 66–70 25 (1·8%) 182 (9·3%) Over 70 11 (0·8%) 147 (7·5%) Missing values (%) 0·2 0·2 Gender (Binary) Female 1 205 (88%) 1 780 (91%) Male 170 (12%) 178 (9·1%) Missing values (%) 0·9 0·4 Profession Doctor 529 (38%) 1 283 (65%) Nurse 859 (62%) 682 (35%) Household composition Couple with children 455 (33%) 1 170 (60%) Couple without children 82 (6·0%) 112 (5·7%) Other type of household with children 394 (29%) 154 (7·9%) Other type of household without children 136 (9·9%) 136 (6·9%) Single adult with children 144 (11%) 225 (11%) Single adult without children 159 (12%) 161 (8·2%) Missing values (%) 1·3 0·4 Making ends meet with total household income With great difficulty 199 (14%) 149 (7·6%) With difficulty 328 (24%) 319 (16%) With some difficulty 695 (50%) 1 189 (61%) Fairly easily 94 (6·8%) 114 (5·8%) Easily 59 (4·3%) 178 (9·1%) Very easily 4 (0·3%) 13 (0·7%) Missing values (%) 0·6 0·2 Reported exposure to violence 1 085 (79%) 1 121 (58%) Missing values (%) 0·9 0·9 1 n (%) [Table 1 , see at the end of this document] We performed data cleaning before the final dataset; missing data were not imputed; our observations had the information needed for our aims. To account for possible misrepresentation of the population, we applied inverse probability weighting (IPW) to assign weights to each participant ( 20 ). Measures The survey was offered in four local languages (Armenian, Georgian, Romanian and Ukrainian). For the mental health section, validated tools were included: the PHQ-9, GAD-7, CAGE, WHO-5, and Cantril Ladder. No questions on previous mental health conditions, diagnoses, or treatments were included. The full methodology for the survey can be consulted elsewhere ( 21 ). Patient Health Questionnaire (PHQ-9) Developed in 1999 by Spitzer et al. ( 22 ), the PHQ-9 is a self-administered tool to screen for depression symptoms. It is based on the diagnostic criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and assessed against an independently structured mental health professional (MHP) interview. It consists of nine questions regarding the occurrence of different symptoms through the last 2 weeks. The response options are “not at all”, “several days”, “more than half of the days”, and “nearly every day”, and each item has a possible score of 0–3 points. Higher scores are more indicative of depressive disorder symptomatology, with a maximum possible score of 27. A score of 10 points is often recommended as a cut-off to define a probable case of major depressive disorder (MDD). A meta-analysis found 8–11 to be an acceptable range for cut-off scores at distinct circumstances ( 23 ). For this study, we use the standard 10-point score as a threshold. 7-item Anxiety Scale (GAD-7) General anxiety disorder (GAD) screening is targeted through the GAD-7 questionnaire designed by Spitzer et al. in 2006 ( 24 ). It is a one-dimensional instrument intended to detect symptoms of GAD, also guided by the DSM-IV criteria and MHP interviews as a standard comparison. The seven questions are related to presenting symptoms during the past 2 weeks, and the four possible answers are the same as for the PHQ-9. Scores range from 0 to 3 points each and have a maximum possible score of 21, higher scores suggest more severe symptomatology. We use a score ≥ 10 as a cut-off point for identifying cases of GAD, which is normally applied. A systematic review identified studies validating this scale along with the GAD-2 against recognized gold-standard instruments ( 25 ). These included the Structured Clinical Interview for Diagnostic Statistical Manual (SCID), and the Revised Clinical Schedule (CIS-R) for the International Classification of Disease, among others (CIDI, MINI). The authors concluded that scores of 7–10 have acceptable properties as cut-off scores for identifying GAD with the GAD-7. World Health Organization-Five Well-Being Index (WHO-5) Derived from other scales and studies by the WHO Regional Office for Europe, the WHO-5 was developed in the late 1990s ( 19 ). It contains five statements related to subjective psychological well-being and a 0-to-5-point Likert scale to indicate the respondent’s agreement with each, with answers ranging from “At no time” to “All of the time”. A maximum of 25 points is possible as a raw score and can be multiplied by four to compute a percentage score ranging from 0-100. Higher scores represent better possible well-being, with cut-offs below 13 (raw score) and 50 (percentage score) suggesting a poor mental well-being and indicating a possible mental health condition. It is widely used in different settings, thus being useful for clinical practice and research studies to assess well-being over time and even screen for possible MDD compared with index depression tools. Statistical analyses We characterize our sample via descriptive statistics. We report response distributions are as frequencies and percentages for every item. We calculated Cronbach’s α and McDonald’s ω reliability coefficients for each scale for internal consistency analyses. McDonald’s ω has been suggested as an alternative to account for assumptions that are not always met with the former. It is interpreted similarly to alpha, and comparable results suggest that the assumptions mentioned before are met ( 26 ). We generated correlation matrices within scales and used Pearson’s r to describe inter-item correlations. We assessed the explanatory power for each item through item-total statistics, also determining the change in α when removed. Afterwards, we performed construct validity analyses for factorial structure. Before these, we applied Kaiser-Meyer-Olkin criterion (KMO) and Bartlett’s test of sphericity to assess the adequacy of the samples for factor analyses. For Exploratory Factor Analysis (EFA) we used the maximum likelihood (ML) method to explore the underlying structure of the data, extract factor loadings and decide which factors to keep through parallel analyses. Additionally, we used Confirmatory Factor Analyses (CFAs) to test the one-factor model of each instrument, thus confirming or weakening its theoretical structure. Our sensitivity analyses consisted of measurement invariance analysis to check for variation between subgroups. We applied Multigroup CFA (MG-CFA) subgroups to assess consistency across them through configural, metric, and scalar invariance levels. Finally, we carried out validity analyses to assess whether the scales were related to each other as expected. We tested for convergent and divergent validity analyses with Spearman’s (ρ) correlation over sum scores. Every descriptive and statistical analysis was performed via R v4.5.1 ( 27 ) and RStudio v2025.09.0.387 ( 28 ). The used packages can be consulted in the Supplementary Material. Results Psychometrics and item characteristics We computed sum mean scores for each country, descriptive statistics are reported in Table 2 . Table 2 Descriptive statistics for each tool by country Armenia Georgia Mean (SE) Skewness Kurtosis Mean (SE) Skewness Kurtosis PHQ-9 5.33 (0.14) 1.277 1.589 3.74 (0.11) 1.881 4.024 GAD-7 4.25 (0.12) 1.284 1.369 2.93 (0.09) 1.939 4.175 WHO-5 65.87 (0.67) -0.525 -0.675 62.62 (0.55) -0.553 -0.610 SE: Standard Error. For the PHQ-9, Armenia had a 5.3-point mean score (standard error [SE] of 0.14), whereas it was 3.74 for Georgia (SE of 0.11); both exhibited left-skewed distributions. For the GAD-7, the sum mean scores for Armenia were 4.25 (SE: 0.12) and 2.93 for Georgia (SE: 0.09), which also displayed a similar distribution to that of the PHQ-9. The WHO-5 sum mean scores were > 60, with a right skewed distribution. Figures S1 .1 to S1.3 of the Supplementary Material shows the distribution of responses for each item. Reliability Reliability indices are shown in Table 3 . The values for Cronbach’s α are suggested to be ≥ 0.9 or 0.95 for clinical application, whereas values above 0.8 are considered satisfactory ( 29 ). The PHQ-9 had a Cronbach’s α = 0.83 (McDonald’s ω = 0.84); these values were α = 0.85 (ω = 0.84) for the GAD-7 and α = 0.89 (ω = 0.89) for the WHO-5 in Armenia. Overall, Pearson’s r ranged from 0.12–0.57 for inter-item correlations. These were smallest for the PHQ-9, and highest for the WHO-5. Table 3 Internal consistency for each scale in both countries. Cronbach's α McDonald's ω Average inter-item r PHQ-9 Armenia 0.834 0.843 0.376 Georgia 0.828 0.844 0.371 GAD-7 Armenia 0.847 0.844 0.453 Georgia 0.830 0.830 0.432 WHO-5 Armenia 0.888 0.888 0.615 Georgia 0.819 0.820 0.482 For Georgia, α = 0.83 (ω = 0.84) for the PHQ-9, α = 0.83 (ω = 0.83) for the GAD-7, and α = 0.82 (ω = 0.82) for the WHO-5. The correlation values between items ranged between 0.15 to 0.65 for the three instruments. The correlation matrices are included in Figures S2 .1 and S2.2 of the Supplementary Material. We observed a positive contribution of every item to the total score through corrected item-total correlations, with the lowest values for items 1 and 9 of the PHQ-9 in Armenia and item 1 of the same scale in Georgia. The resulting ranges of r were > 0.3 for all scales in both countries. Cronbach’s α did not undergo any major changes except for the WHO-5 in Georgia, where variations were greater. This proves a stable global reliability of the scales when deleting an item, indicating that the internal consistency is not dependent on an individual item. Construct validity We tested for sample adequacy through the KMO test, with values for every scale between 0.8 and 0.9 (0.83–0.89). On the other hand, Bartlett’s sphericity tests were statistically significant. Both justify proceeding to the EFA; the details on these can be found in the Table S1 of the Supplementary Material. The eigenvalues obtained in the exploratory factor analyses for the first factor were all above 2, which explained 51% or more of the total variance. The parallel analyses suggested two or three factors measured for the PHQ-9 and GAD-7 but did not surpass the accepted threshold > 1. This supports the one-dimensional solution; results are detailed in the Supplementary Material ( Tables S2.1 and S2.2 ). We performed confirmatory factor analyses to test the unidimensionality of the scales. When the items were specified as indicators of a single factor, the model fitted the data correctly, resulting in robust goodness-of-fit indices with high χ² values and p values < 0.001. Comparative fit indices (CFI) and Tucker-Lewis indices (TLI) were above 0.9 overall, with the sole exception of TLI = 0.883 for the PHQ-9 in Armenia. The Root-mean-square error of approximation (RMSEA) is less than 0.08 only for the GAD-7 in both countries, although their 90% confidence intervals on the lower side generally reach this value. The residuals, assessed by the standardized root-mean-squared residual (SRMR), presented values below 0.08 for every scale. The literature suggests looking for CFI and TLI values are > 0.90, RMSEA < 0.06 (some authors consider < 0.10 as reasonable), and SRMR < 0.08 for a goodness-of-fit ( 30 ). These are fulfilled overall in our analyses, and the results of the indices are shown in Table 4 . Table 4 Confirmatory Factor Analyses (CFA) Scale χ² Df p-value CFI TLI RMSEA 90% CI of RMSEA SRMR Armenia PHQ-9 371.29 27 < 0.001 0.912 0.883 0.096 0.088–0.105 0.048 GAD-7 127.25 14 < 0.001 0.967 0.951 0.077 0.065–0.09 0.029 WHO-5 159.89 5 < 0.001 0.959 0.917 0.152 0.132–0.172 0.035 Georgia PHQ-9 366.85 27 < 0.001 0.938 0.917 0.081 0.073–0.088 0.041 GAD-7 170.75 14 < 0.001 0.964 0.946 0.076 0.066–0.086 0.034 WHO-5 72.07 5 < 0.001 0.981 0.961 0.084 0.067–0.102 0.028 Df = degrees of freedom; CFI = comparative fit index; TLI: Tucker-Lewis Index; RMSEA = root-mean-square error of approximation; 90% CI of RMSEA = 90% confidence interval for RMSEA; SRMR: standardized root-mean-square residual. The factor loadings for each item, scale, and country are presented in Table S3 (Supplementary Material), along with their item-total correlation coefficients and changes in Cronbach’s α when it is deleted. We performed analyses with increasing numbers of factors. These are available in Table S4 (Supplementary Material). No model was shown to be better than the one-dimensional models, although the two-factor model for the PHQ-9 showed slightly better indices. When comparing the two- and three-factor models, we observed no significant improvement. We performed Multi-Group CFA (MG-CFA) to assess invariance in five subgroups: gender (male, female), age (< 55 years, ≥ 55 years), having children (yes, no), making ends meet financially (yes, no), and exposure to violence at the workplace (yes, no). Most models suggested scalar invariance across groups. A detailed description of the results can be found in the S5 Appendix (Supplementary Material). Validity We looked at correlations between scales to assess their validity. The PHQ-9 and GAD-7 showed convergent validity through their correlation coefficients ( r > 0.75, p < 0.05). Both should have negative coefficients compared to the WHO-5, which we proved ( r < -0.51, p < 0.05), confirming divergent validity. The confirmatory evaluation of convergent and divergent validity can be seen in Figures S6.1 and S6.2 (Supplementary Material) through the adjustment of an SEM (Structural equation modelling) of the CFA. Discussion Studies validating these instruments are well-documented in other countries, languages, settings, and even ethnic groups ( 31 – 34 ). We aimed to assess the psychometric properties of the PHQ-9, GAD-7 and WHO-5 screening tools in Armenian and Georgian. These were applied as part of a broader health survey in healthcare workers, along with Moldova and Ukraine, which have validated translations in their official languages. Our evidence shows good reliability of the three instruments, as assessed through Cronbach’s alpha and McDonald’s omega. Internal consistency was also met, with inter-item correlation coefficients lying within a range where collinearity and incongruence were discarded. The item-total correlations exhibited a positive contribution to the total score and suggested that two items from the PHQ-9 (1 and 9) may be components of a second and even a third dimension. Nevertheless, the one-dimensional solution was supported through EFA, where total variance was explained above 50% for the main factors. We tested this subsequently tested this unidimensionality, resulting in fit indices that follow the suggested values. Also, a change is only seen in the CFI and not in the TLI, which indicates that, although it could have a better fit, for parsimony, the model continues to fit better to a single factor. Our MG-CFA analyses revealed overall invariance of the scales between different subgroups, with some exceptions. Finally, convergent validity was evidenced by the moderately strong correlation coefficients between the PHQ-9 and GAD-7. This has practical implications, as both disorders frequently coexist are and therefore correlated ( 35 ). Divergent validity with the WHO-5 scale also demonstrated this. We did not validate our results against a gold standard, such as a structured clinical interview or other screening tools. Additionally, we did not collect information regarding previous or current mental health issues. Nevertheless, we ensured that the process from the translation to the completion of the analyses was methodologically adequate. Our study has several limitations, and the results should be interpreted considering these. Despite having a large sample size, the nature of the nonprobability sampling strategy poses a possibility for selection bias. Additionally, the survey was self-reported, which could lead to response bias. Underreporting may also be present due to embarrassment and social stigma reported as reasons for not seeking psychological help ( 36 ). Doctors and nurses are familiar with these tools; we are unaware if a general population approach may need further cultural adaptation. This implies that the results cannot be generalised at this point. We believe that our process is robust enough to proceed with further studies validating these instruments in Armenia and Georgia. Our analyses show that the translations are adequate from a cultural perspective, and the screening tools are both reliable and valid for application in a broader context. Further validation is needed against a gold standard, as well as application to a broader sample, cultural adaptation with focus groups, and the definition of specific cut-offs in these countries. To our knowledge, this is the first study that translates and implements the PHQ-9, GAD-7, and WHO-5 in Armenia and Georgia. This is relevant due to their sociopolitical and cultural contexts; there is practical clinical value when health screening instruments are culturally adapted ( 37 ). This study aims to extend the availability and application of the PHQ-9, GAD-7 and WHO-5 to more countries and cultures. We believe that our analyses provide useful insights into the use of these tools and hope that they will aid in future validation studies. Abbreviations PHQ-9 Patient Health Questionnaire-9 GAD-7 7-item Generalized Anxiety Disorder scale WHO-5 World Health Organization-Five Well-Being Index WHO World Health Organization CFI Comparative Fit Index TLI Tucker-Lewi Index RMSEA Root-mean-square error of approximation SRMR Standardized root-mean-squared residual COVID-19 Coronavirus Disease of 2019 HCW Healthcare Worker DALY Disability-Adjusted Life Year DG ENEST Directorate-General for Enlargement and the Eastern Neighbourhood EU European Union MoH Ministry of Health STROBE STrengthening the Reporting of OBservational studies in Epidemiology. IPW Inverse Probability Weighting DSM-IV Diagnostic and Statistical Manual of Mental Disorders IV MHP Mental Health Professional MDD Major Depressive Disorder GAD Generalized Anxiety Disorder CIS-R Revised Clinical Schedule for the International Classification of Disease SCID Structured Clinical Interview for Diagnostic Statistical Manual CIDI Composite International Composite Interview MINI Mini International Neuropsychiatric Interview KMO Kaiser-Meyer-Olkin criterion EFA Exploratory Factor Analysis ML Maximum Likelihood CFA Confirmatory Factor Analysis MG-CFA Multigroup Confirmatory Factor Analysis Declarations Ethics approval The study was given ethical approval by the Ethics Committee at the Universidad Autónoma de Madrid (Identifier: CEI-141-3158/2). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. All collected data were anonymised. The need to collect informed consent was waived by the Ethics Committee at Universidad Autónoma de Madrid (Spain). Consent for publication Not applicable. Funding This paper has been produced with the financial assistance of the European Union. The contents are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Union. Author Contribution JLAM was the lead of this project. MK and DR coordinated translations and dissemination of the survey in Armenia and Georgia, respectively. JGC and VAG conducted the analyses and wrote the initial draft. JLAM, RT, TZ, LL, CR, ATI, GC, MK, DR and ML reviewed the draft. JLAM and RT edited and approved the final manuscript. Acknowledgement The authors would like to acknowledge the members of the WHO Regional Office for Europe who helped with coordination between the WHO Collaborating Centre and the WHO Country Offices. The representatives of the WHO country Offices in Armenia and Georgia, who assisted with the dissemination of the survey, along with the translators who helped with the adaptation of the questionnaires, should also be mentioned. Data Availability The data and code underlying this article are available in Open Science Framework, at [https://doi.org/10.17605/OSF.IO/DC5JB](https:/doi.org/10.17605/OSF.IO/DC5JB) . References Arias-de la Torre J, Vilagut G, Ronaldson A, Serrano-Blanco A, Martín V, Peters M, et al. Prevalence and variability of current depressive disorder in 27 European countries: a population-based study. Lancet Public Health. 2021;6(10):e729–38. https://doi.org/10.1016/S2468-2667(21)00047-5 . Javaid SF, Hashim IJ, Hashim MJ, Stip E, Samad MA, Ahbabi AA. Epidemiology of anxiety disorders: global burden and sociodemographic associations. Middle East Curr Psychiatry. 2023;30(1):44. https://doi.org/10.1186/s43045-023-00315-3 . 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Enlargement and Eastern Neighbourhood . https://commission.europa.eu/about/departments-and-executive-agencies/enlargement-and-eastern-neighbourhood_en [Accessed 11th December 2025]. STROBE. STROBE. https://www.strobe-statement.org/ [Accessed 3rd September 2025]. Valdez D, Montenegro MS, Crawford BL, Turner RC, Lo WJ, Jozkowski KN. Translation frameworks and questionnaire design approaches as a component of health research and practice: A discussion and taxonomy of popular translation frameworks and questionnaire design approaches. Soc Sci Med. 2021;278:113931. https://doi.org/10.1016/j.socscimed.2021.113931 . phqscreeners . https://www.phqscreeners.com/index.html [Accessed 11th September 2025]. The World Health Organization-Five Well-Being Index (WHO-5) . https://www.who.int/publications/m/item/WHO-UCN-MSD-MHE-2024.01 [Accessed 11th September 2025]. Mansournia MA, Altman DG. Inverse probability weighting. BMJ. 2016;352:i189. https://doi.org/10.1136/bmj.i189 . Arrona Gómez VA, Mediavilla R, Langins M, Tijerino AM, Redlich C, Zapata T et al. Addressing mental health challenges in Eastern Europe: Armenia, Azerbaijan, Georgia, Moldova, and Ukraine. A survey study on healthcare workers. 2025; https://doi.org/10.17605/OSF.IO/DC5JB Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16(9):606–13. https://doi.org/10.1046/j.1525-1497.2001.016009606.x . Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ. 2012;184(3):E191–6. https://doi.org/10.1503/cmaj.110829 . Spitzer RL, Kroenke K, Williams JBW, Löwe B. A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Arch Intern Med. 2006;166(10):1092–7. https://doi.org/10.1001/archinte.166.10.1092 . Plummer F, Manea L, Trepel D, McMillan D. Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. Gen Hosp Psychiatry. 2016;39:24–31. https://doi.org/10.1016/j.genhosppsych.2015.11.005 . McNeish D. Thanks coefficient alpha, we’ll take it from here. Psychol Methods. 2018;23(3):412–33. https://doi.org/10.1037/met0000144 . {R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2025. https://www.R-project.org/ . Posit team, Boston. RStudio: Integrated Development Environment for R. WA: Posit Software, PBC; 2025. http://www.posit.co/ . Bland JM, Altman DG. Cronbach’s alpha. BMJ (Clinical research ed.) . 1997;314(7080): 572. https://doi.org/10.1136/bmj.314.7080.572 Hu Ltze, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equation Modeling: Multidisciplinary J. 1999. https://doi.org/10.1080/10705519909540118 . Bazo-Alvarez JC, Aparicio ARO, Robles-Mariños R, Julca-Guerrero F, Gómez H, Bazo-Alvarez O, et al. Cultural adaptation to Bolivian Quechua and psychometric analysis of the Patient Health Questionnaire PHQ-9. BMC Public Health. 2024;24(1):129. https://doi.org/10.1186/s12889-023-17566-8 . Pheh KS, Tan CS, Lee KW, Tay KW, Ong HT, Yap SF. Factorial structure, reliability, and construct validity of the Generalized Anxiety Disorder 7-item (GAD-7): Evidence from Malaysia. PLoS ONE. 2023;18(5):e0285435. https://doi.org/10.1371/journal.pone.0285435 . Müller F, Hansen A, Kube M, Arnetz JE, Alshaarawy O, Achtyes ED, et al. Translation, cultural adaptation, and validation of the PHQ-9 and GAD-7 in Kinyarwanda for primary care in the United States. PLoS ONE. 2024;19(10):e0302953. https://doi.org/10.1371/journal.pone.0302953 . Kozel D, Zakotnik JM, Grum AT, Kersnik J, Pavlič DR, Tomori MŽ et al. Applicability of systematic screening for signs and symptoms of depression in family practice patients in Slovenia. Slovenian Med J. 2012;81(12). https://vestnik.szd.si/index.php/ZdravVest/article/view/611 Lamers F, van Oppen P, Comijs HC, Smit JH, Spinhoven P, van Balkom AJLM, et al. Comorbidity patterns of anxiety and depressive disorders in a large cohort study: the Netherlands Study of Depression and Anxiety (NESDA). J Clin Psychiatry. 2011;72(3):341–8. https://doi.org/10.4088/JCP.10m06176blu . Clement S, Schauman O, Graham T, Maggioni F, Evans-Lacko S, Bezborodovs N, et al. What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychol Med. 2015;45(1):11–27. https://doi.org/10.1017/S0033291714000129 . Bhui K, Mohamud S, Warfa N, Craig TJ, Stansfeld SA. Cultural adaptation of mental health measures: improving the quality of clinical practice and research. Br J Psychiatry: J Mental Sci. 2003;183:184–6. https://doi.org/10.1192/bjp.183.3.184 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials1STROBEchecklist.pdf Supplementary information Supplementary Materials 1: STROBE checklist of observational studies’ reporting. SupplementaryMaterials2ArmenianandGeorgianversionsforPHQ9GAD7WHO5.pdf Supplementary Materials 2: Armenian and Georgian versions for PHQ-9, GAD-7, and WHO-5. SupplementaryMaterials3Additionaltablesandfigures.docx Supplementary Materials 3: Additional tables and figures to aid results reported in this study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8521613","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591972248,"identity":"d7b44d9a-1195-4cc2-b5f4-cbb405c2757a","order_by":0,"name":"Vicente Arrona","email":"","orcid":"","institution":"Autonomous University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Vicente","middleName":"","lastName":"Arrona","suffix":""},{"id":591972249,"identity":"0ab216d7-2eaf-46a8-89cd-416cb6d5d97b","order_by":1,"name":"Jesús Godino-Cruz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACxgYY6zAQfzAgVQvjDGK0IMABBgZmHmIUMre3P5P8wVArx3ec9+Bjm4I7iQ0SyU83MFTU4XZYzxkzaR6G48aSh/mSjXMMngG1pJndYDhzGLeWGTls0gwMxxI3HOYxk84xOGzMIJFgdoOx7QBuLfOfgxwG1WIB1pL+7QbjPzwOm8FgJsHDUAPRwmBwWI5BIgdoSwMzHr/kGFvzGBwA+oXH2LAHqIWN503ZjYRjuP1i2H784c0fFXVyfOfPGD748ecwDz97+rYbH2pwO8ywAUQaIJnJBiIScGpgYJCHULjNHAWjYBSMglHAAAA60VB5dondcgAAAABJRU5ErkJggg==","orcid":"","institution":"Autonomous University of Madrid","correspondingAuthor":true,"prefix":"","firstName":"Jesús","middleName":"","lastName":"Godino-Cruz","suffix":""},{"id":591972250,"identity":"3fe27792-2b75-48f9-92e4-1a2c41d35398","order_by":2,"name":"Roberto Mediavilla","email":"","orcid":"","institution":"Centro de Investigación Biomédica en Red de Salud Mental","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Mediavilla","suffix":""},{"id":591972251,"identity":"1d52d14e-0564-4da5-b5ae-174b95d73879","order_by":3,"name":"Ana M. 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12:54:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8521613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8521613/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106401416,"identity":"9f9195b7-48eb-4e8c-a4df-5690c62b218c","added_by":"auto","created_at":"2026-04-08 08:49:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":973140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8521613/v1/59d691de-8b66-4a8c-a5ff-31b272cf8ef7.pdf"},{"id":102869979,"identity":"40b3cebd-fd29-428a-819f-a1195076dbd6","added_by":"auto","created_at":"2026-02-17 17:49:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":239056,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials 1: \u003c/strong\u003eSTROBE checklist of observational studies’ reporting.\u003c/p\u003e","description":"","filename":"SupplementaryMaterials1STROBEchecklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8521613/v1/caf5439dc3b96c7361050035.pdf"},{"id":102963341,"identity":"88993f45-1614-4689-ab1c-d25abb9508a9","added_by":"auto","created_at":"2026-02-19 04:15:46","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":307888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials 2: \u003c/strong\u003eArmenian and Georgian versions for PHQ-9, GAD-7, and WHO-5.\u003c/p\u003e","description":"","filename":"SupplementaryMaterials2ArmenianandGeorgianversionsforPHQ9GAD7WHO5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8521613/v1/6b8df75ce6af0cdbeb9dc662.pdf"},{"id":102869981,"identity":"fde44c63-be6a-43fb-81b3-7b0df1491175","added_by":"auto","created_at":"2026-02-17 17:49:03","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10256456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials 3: \u003c/strong\u003eAdditional tables and figures to aid results reported in this study.\u003c/p\u003e","description":"","filename":"SupplementaryMaterials3Additionaltablesandfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8521613/v1/383f167f190686fb9c2298e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric properties of the Armenian and Georgian versions of the PHQ-9, GAD-7, and WHO-5 Well-Being Index","fulltext":[{"header":"Background","content":"\u003cp\u003eMental health has emerged as a critical public health priority, with especially growing recognition of its impact on individuals and societies after the COVID-19 pandemic. Depressive and anxiety disorders are highly prevalent and represent significant burden across the life course, with approximately 4% of the global population having an anxiety disorder, and more than 6% of the European population living with a depressive disorder (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). While less has been done in this area to understand prevalence among healthcare workers (HCWs), some studies suggest that the prevalence for this group is estimated to be even greater, as one in four reported symptoms compatible with a depressive or anxiety disorder in 2020 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This represents a high burden, as these conditions have seen the biggest increases of Disability-Adjusted Life Years (DALYs) during recent years (2010\u0026ndash;2023)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Reports on the mentioned findings have shown that the previous trends have not changed and have even risen in some regions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The risk of several conditions in various countries was assessed in one study, including mood and anxiety disorders, finding increased risk after COVID-19 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This suggests that mental health disorders\u0026rsquo; symptomatology did not lower after the pandemic peak, which is considered an important stressor for this sector.\u003c/p\u003e \u003cp\u003eThis, along with other factors, has led to a health workforce crisis in Europe (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) as healthcare systems face personnel shortages, exacerbating the situation. Also, an increased demand for health services overwhelms health systems\u0026rsquo; capacity, exacerbating the situation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Improving the retention of health workers has become a central strategy to address the health workforce crisis in Europe (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Enhancing the mental health and well-being of health workers is a critical component of strengthening retention efforts. In October 2024, the World Health Organization (WHO) Regional Office for Europe conducted a web-based survey of doctors and nurses from the European Union, Iceland, and Norway. The objective was to map the mental health situation of this workforce and develop policy guidance to better protect mental health of HCWs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe questionnaire includes the 9-item Patient Health Questionnaire (PHQ-9), the 7-item anxiety scale (GAD-7), and the WHO-5 well-being index (WHO-5). In September 2024, The WHO Regional Office for Europe signed a contribution agreement with the European Commission at the Directorate-General for Neighbourhood and Enlargement Negotiations (DG ENEST) to implement the WHO component \u0026ldquo;Supporting Resilience to Health Emergencies in the Eastern Partnership\u0026rdquo; (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Countries in the DG ENEST agreed to get a similar survey of that in EU and this was added in the outputs of the collaboration agreement. It aims at enhancing health systems in Eastern Europe, strengthening health workforce capacities and resilience to ensure quality health services. In May 2025, the survey was implemented in Armenia, Georgia, the Republic of Moldova, and Ukraine as an extension to the original study.\u003c/p\u003e \u003cp\u003eOne of the challenges was adapting the mentioned screening tools to the official languages of these countries. Ukraine and Moldova have validated translations, but this was not the case for Armenia and Georgia, so adaptations should also be validated psychometrically to ensure their applicability. No studies have explored this before, so we found it worthwhile to use our data for an initial approach.\u003c/p\u003e \u003cp\u003eThe objective of this study was to analyse the validity of the Armenian and Georgian translations of the PHQ-9, GAD-7 and WHO-5 screening tools through their psychometric properties. We intend to extend the availability of these in clinical practice to assess mental health conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eOverview\u003c/p\u003e \u003cp\u003eThe screening tools were back translated by qualified translators and tests were run on a small pilot group of health personnel for feedback before launch. A web-based survey was disseminated through Ministries of Health (MoH) in each country as part of a cross-sectional study conducted in Armenia, Georgia, Moldova, and Ukraine. A STROBE checklist is provided in the Supplementary Material for results\u0026rsquo; reporting (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter finalizing the data collection process from the survey, psychometric analyses were conducted to explore internal consistency, factorial structure, validity, and invariance.\u003c/p\u003e \u003cp\u003eTranslation\u003c/p\u003e \u003cp\u003eWe used back-translation (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) using the original English versions (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The final translated instruments and details on the process can be found in the Supplementary Material.\u003c/p\u003e \u003cp\u003eThe questions in both versions of the questionnaire follow a similar structure to measure the symptoms of mental health problems and well-being in a way that is adapted to the context of healthcare workers. Each WHO Country Office coordinated the national dissemination strategy with MoH. They monitored the translation process performed by professional translators through a master file with instructions for each step. After confirming the final versions, a health professional reviewed that the tools were clear for use. The WHO Regional Office for Europe along with the WHO Collaborating Centre for Mental Health Services Research and Training of the Autonomous University of Madrid reviewed adequate completion of files before implementation.\u003c/p\u003e \u003cp\u003eThe WHO Country Offices were asked to share the survey with at least four doctors and four nurses from their regions for assessment. A total of 11 doctors and nurses from Armenia and 31 from Georgia tested the platform to check functionality and translation of each item and the full questionnaire.\u003c/p\u003e \u003cp\u003eAfter testing by local HCWs, they gave feedback to the Country Offices and the WHO Collaborating Centre to address modifications. These checked by the country offices one last time before the survey was launched to ensure clarity and appropriateness to each language. The final decision was undertaken by Country Offices and the WHO Regional Office.\u003c/p\u003e \u003cp\u003ePopulation and settings\u003c/p\u003e \u003cp\u003eThe survey was launched on the 20th of May 2025 and collected answers from doctors and nurses from Armenia, Georgia, Moldova and Ukraine until the 16th of September 2025. The study design was cross-sectional. We reached out to participants by convenience sampling, through MoH supported by national professional associations.\u003c/p\u003e \u003cp\u003eDuring the study, digital communications were sent to health facilities and from national associations to personal e-mails of their members. We provided a link to the web platform, where they could choose their local language or English to participate.\u003c/p\u003e \u003cp\u003eWe retrieved more than 31,000 responses. Among these, 8,000 were participants from Armenia and Georgia, where a final sample of 3,353 respondents (Armenia: 1,388; Georgia: 1,965) resulted after invalid observations were excluded. HCWs who were licenced doctors or nurses composed the target population; no other health professionals were included, surveys with \u0026gt;\u0026thinsp;90% missing values were excluded. These had to be actively working in the country\u0026rsquo;s health system, and participants with incomplete screening tools were excluded as well.\u003c/p\u003e \u003cp\u003e The health workforce is composed mainly of female professionals, which is reflected in our sample, consisting predominantly of this subgroup (90%), whereas professional categories are balanced, with doctors representing 54% of participants. Age is somewhat normally distributed, with groups of 41\u0026ndash;45 and 51\u0026ndash;55 years being the most common and representing 12.9% and 13.1%, respectively.\u003c/p\u003e \u003cp\u003eWe found\u0026thinsp;\u0026lt;\u0026thinsp;1% of missing values except for the household composition in Armenia (1.3%). More information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArmenia\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1\u0026thinsp;388\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeorgia\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1\u0026thinsp;965\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 20 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (4\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (3\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (2\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (4\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (7\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (9\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (6\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (7\u0026middot;4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (1\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (9\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOver 70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (0\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (7\u0026middot;5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing values (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (Binary)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 205 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 780 (91%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (9\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing values (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfession\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 283 (65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e859 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682 (35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold composition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 170 (60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (6\u0026middot;0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (5\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther type of household with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e394 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (7\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther type of household without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (9\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (6\u0026middot;9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle adult with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle adult without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (8\u0026middot;2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing values (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026middot;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaking ends meet with total household income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith great difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (7\u0026middot;6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith some difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e695 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 189 (61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFairly easily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (6\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (5\u0026middot;8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEasily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (4\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (9\u0026middot;1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery easily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0\u0026middot;3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (0\u0026middot;7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing values (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReported exposure to violence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 085 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 121 (58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing values (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003en (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, see at the end of this document]\u003c/h2\u003e \u003cp\u003eWe performed data cleaning before the final dataset; missing data were not imputed; our observations had the information needed for our aims. To account for possible misrepresentation of the population, we applied inverse probability weighting (IPW) to assign weights to each participant (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003cp\u003eThe survey was offered in four local languages (Armenian, Georgian, Romanian and Ukrainian). For the mental health section, validated tools were included: the PHQ-9, GAD-7, CAGE, WHO-5, and Cantril Ladder. No questions on previous mental health conditions, diagnoses, or treatments were included. The full methodology for the survey can be consulted elsewhere (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient Health Questionnaire (PHQ-9)\u003c/h3\u003e\n\u003cp\u003eDeveloped in 1999 by Spitzer et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), the PHQ-9 is a self-administered tool to screen for depression symptoms. It is based on the diagnostic criteria from the \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders\u003c/em\u003e (DSM-IV) and assessed against an independently structured mental health professional (MHP) interview. It consists of nine questions regarding the occurrence of different symptoms through the last 2 weeks. The response options are \u0026ldquo;not at all\u0026rdquo;, \u0026ldquo;several days\u0026rdquo;, \u0026ldquo;more than half of the days\u0026rdquo;, and \u0026ldquo;nearly every day\u0026rdquo;, and each item has a possible score of 0\u0026ndash;3 points. Higher scores are more indicative of depressive disorder symptomatology, with a maximum possible score of 27.\u003c/p\u003e \u003cp\u003eA score of 10 points is often recommended as a cut-off to define a probable case of major depressive disorder (MDD). A meta-analysis found 8\u0026ndash;11 to be an acceptable range for cut-off scores at distinct circumstances (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). For this study, we use the standard 10-point score as a threshold.\u003c/p\u003e \u003cp\u003e \u003cem\u003e7-item Anxiety Scale (GAD-7)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGeneral anxiety disorder (GAD) screening is targeted through the GAD-7 questionnaire designed by Spitzer et al. in 2006 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). It is a one-dimensional instrument intended to detect symptoms of GAD, also guided by the DSM-IV criteria and MHP interviews as a standard comparison. The seven questions are related to presenting symptoms during the past 2 weeks, and the four possible answers are the same as for the PHQ-9. Scores range from 0 to 3 points each and have a maximum possible score of 21, higher scores suggest more severe symptomatology.\u003c/p\u003e \u003cp\u003eWe use a score\u0026thinsp;\u0026ge;\u0026thinsp;10 as a cut-off point for identifying cases of GAD, which is normally applied. A systematic review identified studies validating this scale along with the GAD-2 against recognized gold-standard instruments (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These included the Structured Clinical Interview for Diagnostic Statistical Manual (SCID), and the Revised Clinical Schedule (CIS-R) for the International Classification of Disease, among others (CIDI, MINI). The authors concluded that scores of 7\u0026ndash;10 have acceptable properties as cut-off scores for identifying GAD with the GAD-7.\u003c/p\u003e\n\u003ch3\u003eWorld Health Organization-Five Well-Being Index (WHO-5)\u003c/h3\u003e\n\u003cp\u003eDerived from other scales and studies by the WHO Regional Office for Europe, the WHO-5 was developed in the late 1990s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It contains five statements related to subjective psychological well-being and a 0-to-5-point Likert scale to indicate the respondent\u0026rsquo;s agreement with each, with answers ranging from \u0026ldquo;At no time\u0026rdquo; to \u0026ldquo;All of the time\u0026rdquo;. A maximum of 25 points is possible as a raw score and can be multiplied by four to compute a percentage score ranging from 0-100. Higher scores represent better possible well-being, with cut-offs below 13 (raw score) and 50 (percentage score) suggesting a poor mental well-being and indicating a possible mental health condition.\u003c/p\u003e \u003cp\u003eIt is widely used in different settings, thus being useful for clinical practice and research studies to assess well-being over time and even screen for possible MDD compared with index depression tools.\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eWe characterize our sample via descriptive statistics. We report response distributions are as frequencies and percentages for every item.\u003c/p\u003e \u003cp\u003eWe calculated Cronbach\u0026rsquo;s α and McDonald\u0026rsquo;s ω reliability coefficients for each scale for internal consistency analyses. McDonald\u0026rsquo;s ω has been suggested as an alternative to account for assumptions that are not always met with the former. It is interpreted similarly to alpha, and comparable results suggest that the assumptions mentioned before are met (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). We generated correlation matrices within scales and used Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e to describe inter-item correlations. We assessed the explanatory power for each item through item-total statistics, also determining the change in α when removed.\u003c/p\u003e \u003cp\u003eAfterwards, we performed construct validity analyses for factorial structure. Before these, we applied Kaiser-Meyer-Olkin criterion (KMO) and Bartlett\u0026rsquo;s test of sphericity to assess the adequacy of the samples for factor analyses. For Exploratory Factor Analysis (EFA) we used the maximum likelihood (ML) method to explore the underlying structure of the data, extract factor loadings and decide which factors to keep through parallel analyses. Additionally, we used Confirmatory Factor Analyses (CFAs) to test the one-factor model of each instrument, thus confirming or weakening its theoretical structure. Our sensitivity analyses consisted of measurement invariance analysis to check for variation between subgroups. We applied Multigroup CFA (MG-CFA) subgroups to assess consistency across them through configural, metric, and scalar invariance levels.\u003c/p\u003e \u003cp\u003eFinally, we carried out validity analyses to assess whether the scales were related to each other as expected. We tested for convergent and divergent validity analyses with Spearman\u0026rsquo;s (ρ) correlation over sum scores.\u003c/p\u003e \u003cp\u003eEvery descriptive and statistical analysis was performed via R v4.5.1 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and RStudio v2025.09.0.387 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The used packages can be consulted in the Supplementary Material.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePsychometrics and item characteristics\u003c/p\u003e \u003cp\u003eWe computed sum mean scores for each country, descriptive statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for each tool by country\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eArmenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGeorgia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePHQ-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.33 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.74 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAD-7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.25 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.93 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHO-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.87 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.62 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSE: Standard Error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the PHQ-9, Armenia had a 5.3-point mean score (standard error [SE] of 0.14), whereas it was 3.74 for Georgia (SE of 0.11); both exhibited left-skewed distributions. For the GAD-7, the sum mean scores for Armenia were 4.25 (SE: 0.12) and 2.93 for Georgia (SE: 0.09), which also displayed a similar distribution to that of the PHQ-9. The WHO-5 sum mean scores were \u0026gt;\u0026thinsp;60, with a right skewed distribution. \u003cb\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.1\u003c/b\u003e to \u003cb\u003eS1.3\u003c/b\u003e of the Supplementary Material shows the distribution of responses for each item.\u003c/p\u003e \u003cp\u003eReliability\u003c/p\u003e \u003cp\u003eReliability indices are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The values for Cronbach\u0026rsquo;s α are suggested to be \u0026ge;\u0026thinsp;0.9 or 0.95 for clinical application, whereas values above 0.8 are considered satisfactory (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The PHQ-9 had a Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.83 (McDonald\u0026rsquo;s ω\u0026thinsp;=\u0026thinsp;0.84); these values were α\u0026thinsp;=\u0026thinsp;0.85 (ω\u0026thinsp;=\u0026thinsp;0.84) for the GAD-7 and α\u0026thinsp;=\u0026thinsp;0.89 (ω\u0026thinsp;=\u0026thinsp;0.89) for the WHO-5 in Armenia. Overall, Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e ranged from 0.12\u0026ndash;0.57 for inter-item correlations. These were smallest for the PHQ-9, and highest for the WHO-5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInternal consistency for each scale in both countries.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach's α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMcDonald's ω\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage inter-item r\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArmenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeorgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAD-7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArmenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeorgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHO-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArmenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeorgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Georgia, α\u0026thinsp;=\u0026thinsp;0.83 (ω\u0026thinsp;=\u0026thinsp;0.84) for the PHQ-9, α\u0026thinsp;=\u0026thinsp;0.83 (ω\u0026thinsp;=\u0026thinsp;0.83) for the GAD-7, and α\u0026thinsp;=\u0026thinsp;0.82 (ω\u0026thinsp;=\u0026thinsp;0.82) for the WHO-5. The correlation values between items ranged between 0.15 to 0.65 for the three instruments. The correlation matrices are included in \u003cb\u003eFigures \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.1\u003c/b\u003e and \u003cb\u003eS2.2\u003c/b\u003e of the Supplementary Material.\u003c/p\u003e \u003cp\u003eWe observed a positive contribution of every item to the total score through corrected item-total correlations, with the lowest values for items 1 and 9 of the PHQ-9 in Armenia and item 1 of the same scale in Georgia. The resulting ranges of \u003cem\u003er\u003c/em\u003e were \u0026gt;\u0026thinsp;0.3 for all scales in both countries. Cronbach\u0026rsquo;s α did not undergo any major changes except for the WHO-5 in Georgia, where variations were greater. This proves a stable global reliability of the scales when deleting an item, indicating that the internal consistency is not dependent on an individual item.\u003c/p\u003e \u003cp\u003eConstruct validity\u003c/p\u003e \u003cp\u003eWe tested for sample adequacy through the KMO test, with values for every scale between 0.8 and 0.9 (0.83\u0026ndash;0.89). On the other hand, Bartlett\u0026rsquo;s sphericity tests were statistically significant. Both justify proceeding to the EFA; the details on these can be found in the \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e of the Supplementary Material.\u003c/p\u003e \u003cp\u003e The eigenvalues obtained in the exploratory factor analyses for the first factor were all above 2, which explained 51% or more of the total variance. The parallel analyses suggested two or three factors measured for the PHQ-9 and GAD-7 but did not surpass the accepted threshold\u0026thinsp;\u0026gt;\u0026thinsp;1. This supports the one-dimensional solution; results are detailed in the Supplementary Material (\u003cb\u003eTables S2.1\u003c/b\u003e and \u003cb\u003eS2.2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe performed confirmatory factor analyses to test the unidimensionality of the scales. When the items were specified as indicators of a single factor, the model fitted the data correctly, resulting in robust goodness-of-fit indices with high \u003cem\u003eχ\u0026sup2;\u003c/em\u003e values and p values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Comparative fit indices (CFI) and Tucker-Lewis indices (TLI) were above 0.9 overall, with the sole exception of TLI\u0026thinsp;=\u0026thinsp;0.883 for the PHQ-9 in Armenia. The Root-mean-square error of approximation (RMSEA) is less than 0.08 only for the GAD-7 in both countries, although their 90% confidence intervals on the lower side generally reach this value. The residuals, assessed by the standardized root-mean-squared residual (SRMR), presented values below 0.08 for every scale. The literature suggests looking for CFI and TLI values are \u0026gt;\u0026thinsp;0.90, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.06 (some authors consider\u0026thinsp;\u0026lt;\u0026thinsp;0.10 as reasonable), and SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.08 for a goodness-of-fit (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). These are fulfilled overall in our analyses, and the results of the indices are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfirmatory Factor Analyses (CFA)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eχ\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90% CI of RMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eArmenia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.088\u0026ndash;0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.065\u0026ndash;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.132\u0026ndash;0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeorgia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.073\u0026ndash;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.066\u0026ndash;0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.067\u0026ndash;0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eDf\u0026thinsp;=\u0026thinsp;degrees of freedom; CFI\u0026thinsp;=\u0026thinsp;comparative fit index; TLI: Tucker-Lewis Index; RMSEA\u0026thinsp;=\u0026thinsp;root-mean-square error of approximation; 90% CI\u0026nbsp;of RMSEA =\u0026nbsp;90% confidence interval for RMSEA; SRMR: standardized root-mean-square residual.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe factor loadings for each item, scale, and country are presented in \u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e (Supplementary Material), along with their item-total correlation coefficients and changes in Cronbach\u0026rsquo;s α when it is deleted.\u003c/p\u003e \u003cp\u003eWe performed analyses with increasing numbers of factors. These are available in \u003cb\u003eTable S4\u003c/b\u003e (Supplementary Material). No model was shown to be better than the one-dimensional models, although the two-factor model for the PHQ-9 showed slightly better indices. When comparing the two- and three-factor models, we observed no significant improvement.\u003c/p\u003e \u003cp\u003eWe performed Multi-Group CFA (MG-CFA) to assess invariance in five subgroups: gender (male, female), age (\u0026lt;\u0026thinsp;55 years, \u0026ge;\u0026thinsp;55 years), having children (yes, no), making ends meet financially (yes, no), and exposure to violence at the workplace (yes, no).\u003c/p\u003e \u003cp\u003eMost models suggested scalar invariance across groups. A detailed description of the results can be found in the S5 Appendix (Supplementary Material).\u003c/p\u003e \u003cp\u003eValidity\u003c/p\u003e \u003cp\u003eWe looked at correlations between scales to assess their validity. The PHQ-9 and GAD-7 showed convergent validity through their correlation coefficients (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Both should have negative coefficients compared to the WHO-5, which we proved (\u003cem\u003er\u003c/em\u003e \u0026lt; -0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming divergent validity.\u003c/p\u003e \u003cp\u003eThe confirmatory evaluation of convergent and divergent validity can be seen in \u003cb\u003eFigures S6.1\u003c/b\u003e and \u003cb\u003eS6.2\u003c/b\u003e (Supplementary Material) through the adjustment of an SEM (Structural equation modelling) of the CFA.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies validating these instruments are well-documented in other countries, languages, settings, and even ethnic groups (\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). We aimed to assess the psychometric properties of the PHQ-9, GAD-7 and WHO-5 screening tools in Armenian and Georgian. These were applied as part of a broader health survey in healthcare workers, along with Moldova and Ukraine, which have validated translations in their official languages.\u003c/p\u003e \u003cp\u003eOur evidence shows good reliability of the three instruments, as assessed through Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega. Internal consistency was also met, with inter-item correlation coefficients lying within a range where collinearity and incongruence were discarded. The item-total correlations exhibited a positive contribution to the total score and suggested that two items from the PHQ-9 (1 and 9) may be components of a second and even a third dimension. Nevertheless, the one-dimensional solution was supported through EFA, where total variance was explained above 50% for the main factors. We tested this subsequently tested this unidimensionality, resulting in fit indices that follow the suggested values. Also, a change is only seen in the CFI and not in the TLI, which indicates that, although it could have a better fit, for parsimony, the model continues to fit better to a single factor.\u003c/p\u003e \u003cp\u003eOur MG-CFA analyses revealed overall invariance of the scales between different subgroups, with some exceptions. Finally, convergent validity was evidenced by the moderately strong correlation coefficients between the PHQ-9 and GAD-7. This has practical implications, as both disorders frequently coexist are and therefore correlated (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Divergent validity with the WHO-5 scale also demonstrated this.\u003c/p\u003e \u003cp\u003eWe did not validate our results against a gold standard, such as a structured clinical interview or other screening tools. Additionally, we did not collect information regarding previous or current mental health issues. Nevertheless, we ensured that the process from the translation to the completion of the analyses was methodologically adequate.\u003c/p\u003e \u003cp\u003eOur study has several limitations, and the results should be interpreted considering these. Despite having a large sample size, the nature of the nonprobability sampling strategy poses a possibility for selection bias. Additionally, the survey was self-reported, which could lead to response bias. Underreporting may also be present due to embarrassment and social stigma reported as reasons for not seeking psychological help (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Doctors and nurses are familiar with these tools; we are unaware if a general population approach may need further cultural adaptation. This implies that the results cannot be generalised at this point.\u003c/p\u003e \u003cp\u003eWe believe that our process is robust enough to proceed with further studies validating these instruments in Armenia and Georgia. Our analyses show that the translations are adequate from a cultural perspective, and the screening tools are both reliable and valid for application in a broader context. Further validation is needed against a gold standard, as well as application to a broader sample, cultural adaptation with focus groups, and the definition of specific cut-offs in these countries.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study that translates and implements the PHQ-9, GAD-7, and WHO-5 in Armenia and Georgia. This is relevant due to their sociopolitical and cultural contexts; there is practical clinical value when health screening instruments are culturally adapted (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to extend the availability and application of the PHQ-9, GAD-7 and WHO-5 to more countries and cultures. We believe that our analyses provide useful insights into the use of these tools and hope that they will aid in future validation studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePHQ-9\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Patient Health Questionnaire-9\u003c/p\u003e\n\u003cp\u003eGAD-7\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;7-item Generalized Anxiety Disorder scale\u003c/p\u003e\n\u003cp\u003eWHO-5\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;World Health Organization-Five Well-Being Index\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;World Health Organization\u003c/p\u003e\n\u003cp\u003eCFI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Comparative Fit Index\u003c/p\u003e\n\u003cp\u003eTLI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Tucker-Lewi Index\u003c/p\u003e\n\u003cp\u003eRMSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;Root-mean-square error of approximation\u003c/p\u003e\n\u003cp\u003eSRMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Standardized root-mean-squared residual\u003c/p\u003e\n\u003cp\u003eCOVID-19 \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;Coronavirus Disease of 2019\u003c/p\u003e\n\u003cp\u003eHCW \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Healthcare Worker\u003c/p\u003e\n\u003cp\u003eDALY \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;Disability-Adjusted Life Year\u003c/p\u003e\n\u003cp\u003eDG ENEST\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Directorate-General for Enlargement and the Eastern Neighbourhood\u003c/p\u003e\n\u003cp\u003eEU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;European Union \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ministry of Health\u003c/p\u003e\n\u003cp\u003eSTROBE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;STrengthening the Reporting of OBservational studies in Epidemiology.\u003c/p\u003e\n\u003cp\u003eIPW\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Inverse Probability Weighting\u003c/p\u003e\n\u003cp\u003eDSM-IV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diagnostic and Statistical Manual of Mental Disorders IV\u003c/p\u003e\n\u003cp\u003eMHP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mental Health Professional\u003c/p\u003e\n\u003cp\u003eMDD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Major Depressive Disorder\u003c/p\u003e\n\u003cp\u003eGAD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Generalized Anxiety Disorder\u003c/p\u003e\n\u003cp\u003eCIS-R\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Revised Clinical Schedule for the International Classification of Disease\u003c/p\u003e\n\u003cp\u003eSCID\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Structured Clinical Interview for Diagnostic Statistical Manual\u003c/p\u003e\n\u003cp\u003eCIDI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Composite International Composite Interview\u003c/p\u003e\n\u003cp\u003eMINI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mini International Neuropsychiatric Interview\u003c/p\u003e\n\u003cp\u003eKMO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kaiser-Meyer-Olkin criterion\u003c/p\u003e\n\u003cp\u003eEFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Exploratory Factor Analysis\u003c/p\u003e\n\u003cp\u003eML\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Maximum Likelihood\u003c/p\u003e\n\u003cp\u003eCFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Confirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003eMG-CFA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multigroup Confirmatory Factor Analysis\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e The study was given ethical approval by the Ethics Committee at the Universidad Aut\u0026oacute;noma de Madrid (Identifier: CEI-141-3158/2). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. All collected data were anonymised. The need to collect informed consent was waived by the Ethics Committee at Universidad Aut\u0026oacute;noma de Madrid (Spain).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis paper has been produced with the financial assistance of the European Union. The contents are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Union.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJLAM was the lead of this project. MK and DR coordinated translations and dissemination of the survey in Armenia and Georgia, respectively. JGC and VAG conducted the analyses and wrote the initial draft. JLAM, RT, TZ, LL, CR, ATI, GC, MK, DR and ML reviewed the draft. JLAM and RT edited and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the members of the WHO Regional Office for Europe who helped with coordination between the WHO Collaborating Centre and the WHO Country Offices. The representatives of the WHO country Offices in Armenia and Georgia, who assisted with the dissemination of the survey, along with the translators who helped with the adaptation of the questionnaires, should also be mentioned.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data and code underlying this article are available in Open Science Framework, at [https://doi.org/10.17605/OSF.IO/DC5JB](https:/doi.org/10.17605/OSF.IO/DC5JB) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArias-de la Torre J, Vilagut G, Ronaldson A, Serrano-Blanco A, Mart\u0026iacute;n V, Peters M, et al. Prevalence and variability of current depressive disorder in 27 European countries: a population-based study. 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Cultural adaptation of mental health measures: improving the quality of clinical practice and research. Br J Psychiatry: J Mental Sci. 2003;183:184\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/bjp.183.3.184\u003c/span\u003e\u003cspan address=\"10.1192/bjp.183.3.184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Psychometrics, Armenia, Georgia (Republic), Surveys and questionnaires, Health personnel.","lastPublishedDoi":"10.21203/rs.3.rs-8521613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8521613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepressive and anxiety disorders are highly prevalent on healthcare personnel. A survey was conducted in Armenia, Georgia, Moldova, and Ukraine to map their mental health as an extension of a previous study to improve sustainability of health systems. The Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder scale (GAD-7), and the WHO 5-item well-being index (WHO-5) were used as screening tools. These were not available nor validated in Armenian nor Georgian. Assessment of their psychometric properties is needed to validate their applicability.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe did a cross-sectional study. Translations coordinated by the WHO Regional and Country Offices were done. A pilot study helped identify translation errors. The survey was open for three months, results were used to test for internal consistency, construct validity, discriminant validity, and measurement invariance. We analysed 3,353 valid responses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalyses indicated overall good internal consistency with Cronbach\u0026rsquo;s α and McDonald\u0026rsquo;s ω between 0.8\u0026ndash;0.9. The factor loadings ranged between 0.38 and 0.76, and the CFI, TLI, RMSEA, and SRMR indices were appropriate even through measurement invariance tests. The strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.75\u0026ndash;0.78) between the PHQ-9 and GAD-7 scores suggested convergent validity, whereas a negative correlation (r = \u0026minus;\u0026thinsp;0.59 to -0.51) between these and the WHO-5 score indicated divergent validity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eApplicability of the PHQ-9, GAD-7 and WHO-5 in Armenia and Georgia was proved. These need adequate translation and assessment for use in the clinical practice. Two main limitations were present, the sample comprised only healthcare professionals, and no validation against a gold standard was conducted.\u003c/p\u003e","manuscriptTitle":"Psychometric properties of the Armenian and Georgian versions of the PHQ-9, GAD-7, and WHO-5 Well-Being Index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 17:48:56","doi":"10.21203/rs.3.rs-8521613/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T09:02:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T19:55:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319385690161412575572806260838793657373","date":"2026-02-15T19:18:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172413483506403142215731385728936766330","date":"2026-02-15T13:45:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T07:44:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82032247965020961953899205969753561112","date":"2026-02-12T09:40:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T07:26:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T06:44:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-22T10:55:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T15:44:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-01-21T15:31:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"327eecd9-3b0c-4e37-9524-a4119ced35a0","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T21:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 17:48:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8521613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8521613","identity":"rs-8521613","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}