Psychometric Evaluation of the Persian Version of the Pandemic- Related Pregnancy Stress Scale

Psychometric Evaluation of the Persian Version of the Pandemic- Related Pregnancy Stress Scale | 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 Evaluation of the Persian Version of the Pandemic- Related Pregnancy Stress Scale Narjes Sadat Borghei, Roya Nikbakht, Artadokht Vosogh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6813404/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: The pandemic event introduced widespread challenges, with pregnant individuals experiencing heightened stress due to infection risks. Despite its potential relevance for future outbreaks, the Pandemic-Related Pregnancy Stress Scale (PREPS) has not undergone psychometric evaluation in Iran. This study assessed the psychometric properties of the Persian PREPS in a sample of Iranian pregnant women. Methods: This methodological study was conducted in the latter half of 2022. The translation and cultural adaptation were conducted in accordance with the World Health Organization's standardized protocol. A total of 300 pregnant women receiving care at comprehensive health service centers in Gorgan were recruited via convenience sampling, contingent upon meeting inclusion criteria and providing informed consent. Convergent validity was assessed using the Corona Disease Anxiety Scale and the Van den Bergh Pregnancy-Related Anxiety Questionnaire (PRAQ). Data analysis was performed using SPSS version 18 and AMOS version 24. Results : All 300 participants completed the study. The mean age was 28.05 ± 6.32 years, and the mean gestational age was 24.41 ± 6.10 weeks. Most participants were nulliparous housewives with moderate educational attainment and of Persian ethnicity. PREPS scores indicated that 3.0–10.7% of respondents experienced high stress across subscales. Internal consistency was strong (Cronbach α = 0.897). Exploratory factor analysis (n = 150) yielded a three-factor structure, accounting for 62% of the total variance after removing one item with a low factor loading. Confirmatory factor analysis (n = 150) validated the model, with all fit indices exceeding acceptable thresholds after refinement. Convergent validity was supported by significant positive correlations between PREPS subscales and related anxiety measures (total r = 0.44, p < 0.001), confirming satisfactory construct validity. Conclusion: The Persian PREPS demonstrated strong psychometric performance, supporting its use for measuring pandemic-related stress in pregnant populations. Anxiety COVID-19 perinatal outcomes Pandemic-Related Pregnancy Stress Scale psychometrics Figures Figure 1 Figure 2 Research Highlights Validated the Persian version of the PREPS Demonstrated high internal consistency (Cronbach α = 0.897) Factor analyses supported a three-factor structure Confirmed convergent validity with anxiety-related scales Identifies high-stress pregnant women for early intervention during pandemics 1. Introduction Psychological disorders, particularly antenatal anxiety, have been associated with adverse pregnancy outcomes [ 1 ]. Maternal anxiety and emotional dysregulation influence fetal development by activating the maternal nervous system and promoting the release of acetylcholine and epinephrine. These neurochemicals can cross the placenta and disrupt fetal physiological processes [ 2 ], potentially contributing to reduced birth weight, altered immune responses, and dysregulation in neurotransmitter secretion, including dopamine, serotonin, γ-aminobutyric acid (GABA), and norepinephrine [3]. Antenatal anxiety has been linked to a range of adverse perinatal outcomes [4], such as preeclampsia, prenatal depression, hyperemesis gravidarum, preterm birth, low birth weight, and diminished Apgar scores [5]. The cumulative evidence underscores the significant impact of maternal anxiety and stress on obstetric and neonatal health [ 6 ]. Pregnancy, even in non-crisis conditions, may be inherently stressful for some women. During the recent pandemic (COVID-19), this burden was magnified by infection-related fears, social distancing mandates, mobility restrictions, and quarantine measures [3,7]. These circumstances contributed to increased rates of depression, anxiety, and psychological distress among pregnant women [ 8 ]. Concerns over viral exposure led some individuals to postpone or cancel prenatal visits. In more extreme cases, anxiety prompted considerations of pregnancy termination or elective cesarean delivery [5]. Widespread reports indicated moderate to severe anxiety levels in pregnant populations during the pandemic, primarily due to fears surrounding maternal and neonatal infection and hesitancy about attending healthcare appointments [ 9 , 10 ]. While various scales exist for general stress assessment, few instruments specifically address stressors unique to pregnancy. The Pandemic-Related Pregnancy Stress Scale (PREPS), developed and validated by Heidi Preis, was the first tool designed to measure pandemic-specific stress in pregnant populations [11]. It has since been adapted and psychometrically validated in several languages, including Italian [12], German [13], Polish [14], and Spanish [15]. However, a validated Persian version remains unavailable. In light of the ongoing vulnerability of pregnant women during public health crises and the likelihood of future pandemics, this study aimed to validate the Persian version of the PREPS in an Iranian sample. Establishing a reliable instrument for pandemic-related pregnancy stress is essential for enhancing maternal care in emergency contexts. 2. Materials and Methods 2.1. Study Design This study employed a methodological design to evaluate the psychometric properties of the target instrument. 2.2. Study Population The study population comprised pregnant women attending urban comprehensive health centers in Gorgan, Iran. 2.3. Sample and Eligibility Criteria Participants were recruited from pregnant women presenting to the specified centers who met the following inclusion criteria: Iranian nationality, literacy in reading and writing, and singleton pregnancy. Exclusion criteria included unwillingness to participate; diagnosis of chronic conditions such as diabetes or hypertension; confirmed COVID-19 infection during the study period; a history of anxiety disorders or use of psychiatric medications prior to the pandemic; or recent exposure to a major stressful event such as the death of a first-degree relative from COVID-19 within the previous six months. 2.4. Sample Size Sample size determination for factor analysis followed the convention of 10 participants per item [16]. The PREPS consists of 15 items; therefore, 150 participants were enrolled for exploratory factor analysis (EFA), and 150 separate participants were included for confirmatory factor analysis (CFA), resulting in a total sample of 300. 2.5. Data Collection Procedure Following ethical approval from the Vice-Chancellor for Research and Technology at Golestan University of Medical Sciences, 300 eligible participants were recruited using convenience sampling from urban health centers in Gorgan. Data collection involved the use of self-administered instruments, including a demographic and reproductive history questionnaire, the Persian version of the PREPS, and the Coronavirus Disease Anxiety Scale. All procedures were conducted following the ethical standards of research. The researcher introduced herself to each participant, explained the study objectives in detail, and obtained written informed consent. Participation was entirely voluntary. 3. Instruments 3.1. Demographic and Obstetric Information Form This form comprised eight items evaluating demographic characteristics (age, literacy, occupation, and ethnicity) and obstetric history (gravidity, gestational age, number of living children, and history of abortion). 3.2. Corona Anxiety Scale The Corona Anxiety Scale (CAS), developed by Alipour et al. in 2019 (1398 in the Iranian calendar), was designed during the early phase of the COVID-19 pandemic. It consists of 18 items, divided into two subscales: psychological symptoms (items 1–9) and physical symptoms (items 10–18). Each item is rated on a 4-point Likert scale, ranging from 0 (never) to 3 (always), yielding a total score of 0 to 54. Higher scores indicate greater anxiety. Internal consistency coefficients were high (total: α = 0.919; psychological: α = 0.879; physical: α = 0.861). Validity has been confirmed through exploratory and confirmatory factor analyses [ 17 ]. 3.3. Pregnancy-Related Anxiety Questionnaire Developed by Van den Bergh in 1989, this 17-item scale assesses pregnancy-specific anxiety across five subscales: fear of childbirth (3 items), fear of delivering a child with disabilities (4 items), fear of changes in marital relationships (3 items), fear of emotional instability affecting the fetus (3 items), and fear of personal life disruption due to pregnancy (4 items). Validity and reliability were confirmed in studies by Sarmad et al. (2011) and Karamoziyan et al. (2016), with Cronbach α values exceeding 0.70 [ 18 ]. 3.4. Pandemic-Related Pregnancy Stress Scale The PREPS, developed by Preis et al. in 2021, was administered to 4,451 pregnant women in the United States between April and May 2020 via an online survey. It comprises 15 items measuring three dimensions: Preparedness Stress (PS; 7 items), Infection Stress (IS; 5 items), and Positive Appraisal (PA; 3 items). Responses are recorded on a 5-point Likert scale from 1 (very little) to 5 (very much). There are no reverse-coded items, and the total score is calculated as the mean of all items. Reported internal consistency ranged from α = 0.68 to 0.86 [11]. 4. Data Analysis and Psychometric Procedures 4.1. Data Analysis Data from each completed questionnaire were entered into SPSS version 18. Descriptive statistics were used to summarize demographic and obstetric variables. Structural analyses were conducted using AMOS version 24.0. 4.2. Psychometric Evaluation 4.2.1. Translation and Cultural Adaptation The original PREPS, developed in English, was translated and culturally adapted into Persian following formal permission from the original developer. The process adhered to the World Health Organization's standardized four-step protocol [ 19 ]. Face validity was evaluated qualitatively through structured interviews with 10 pregnant women. Items were assessed for clarity, relevance to the intended constructs, and potential ambiguity. Based on participant feedback, items were refined to ensure semantic clarity and cultural appropriateness. The finalized Persian version was pilot-tested and then administered to the full sample in the second half of 2022. 4.2.2. Reliability Assessment Internal consistency was assessed using Cronbach α, with a threshold of 0.70 considered acceptable [20]. Two additional diagnostics were applied: corrected item-total correlation and Cronbach's Alpha if an item is deleted. Corrected Item–Total Correlation examined the correlation between each item and the total score. Items with correlations below 0.30 were flagged for review, and those with correlations below 0.20 were excluded from further analysis. Cronbach's Alpha if Item Deleted was used to assess whether removing a specific item would enhance overall reliability [21,22]. 4.2.3. Construct Validity Construct validity was assessed through EFA using principal axis factoring with varimax rotation. Prior to EFA, assumptions were verified, including inter-item correlations (preferably ≥ 0.30) and sampling adequacy. The Kaiser–Meyer–Olkin (KMO) statistic and Bartlett's test of sphericity were used to determine suitability for factor analysis. A KMO value > 0.60 and a significant Bartlett's test indicated adequate factorability [23]. 4.2.4. Confirmatory Factor Analysis CFA was conducted following EFA to evaluate the proposed factor structure. Relationships between latent factors and their observed variables were examined using maximum likelihood estimation in AMOS version 24.0. 4.2.5. Convergent Validity Convergent validity was evaluated by examining correlations between the PREPS, the CAS [ 17 ], and the Pregnancy-Related Anxiety Questionnaire [ 18 ]. A p-value < 0.05 was considered statistically significant across all analyses [ 17 ]. 5. Results 5.1. Descriptive Findings A total of 300 pregnant women completed the study. The mean age was 28.05 ± 6.32 years, and the mean gestational age was 24.41 ± 6.10 weeks. As shown in Table 1 , most participants were housewives, had moderate educational levels, were nulliparous, and identified as Persian with regard to stress levels across the PREPS subscales. Specifically, 3.0% of participants reported high stress on the IS subscale, 7.0% on the PS subscale, and 10.7% on the PA subscale (Table 1 ). Table 1 Demographic and fertility characteristics of participant pregnant women Variables Number percent Employment status Not Employed 252 84 Employed 48 16 Literacy Low 128 42.7 Diploma& foghdiplom 109 36.3 Licence & higher 63 21 Ethnicity Fars 287 95.7 Turkmen 13 4.3 Gravid 1 123 41 2 81 27 More than 3 96 32 Living child 0 158 52.7 1 46 15.33 2 33 11 More than 3 63 21 Abortion history no 222 74 yes 78 26 Abortion type spontaneos 56 74 Induced 22 26 PREPS PIS ≥ 4 high 9 3 PS ≥ 4 high 21 7 PA 0.30. The overall Cronbach α was 0.897, reflecting strong internal consistency (Table 2 ). Table 2 Reliability coefficients for PREPS items Itemes Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted pereps1 30.1235 110.047 .413 .896 pereps2 30.2840 105.621 .648 .887 pereps3 30.4074 104.603 .667 .886 pereps4 30.3148 104.080 .672 .886 pereps5 30.5309 108.772 .555 .891 pereps6 30.4506 106.411 .592 .889 pereps7 30.6111 110.885 .546 .892 pereps8 30.1790 102.086 .766 .882 pereps9 30.1975 103.066 .762 .883 pereps10 29.9691 101.434 .733 .883 pereps11 30.5617 108.956 .589 .890 pereps12 29.8395 100.136 .681 .885 pereps13 28.6420 109.051 .360 .900 pereps14 29.5000 106.127 .440 .897 pereps15 28.1173 111.508 .321 .900 5.3. Construct Validity: Exploratory Factor Analysis EFA was performed on responses from 150 participants. No extreme outliers were detected. Inter-item correlations were adequate for most items (Table 3 ). All KMO values exceeded 0.63, with an overall KMO of 0.84, and Bartlett's test was statistically significant (p < 0.001), confirming sampling adequacy (Table 4 ). Table 3 Inter-item correlation matrix 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 pereps1 1.000 pereps2 .457 1.000 pereps3 .359 .615 1.000 pereps4 .453 .582 .643 1.000 pereps5 .239 .414 .632 .522 1.000 pereps6 .319 .496 .774 .521 .839 1.000 pereps7 .182 .406 .494 .371 .743 .615 1.000 pereps8 .346 .509 .454 .547 .382 .413 .416 1.000 pereps9 .348 .451 .436 .506 .342 .408 .371 .898 1.000 pereps10 .290 .494 .411 .569 .308 .353 .391 .849 .861 1.000 pereps11 .207 .405 .342 .277 .371 .359 .393 .605 .621 .540 1.000 pereps12 .303 .491 .378 .446 .269 .338 .356 .682 .736 .741 .555 1.000 pereps13 .123 .205 .171 .226 .004 .011 .034 .228 .288 .279 .168 .278 1.000 pereps14 .125 .304 .334 .247 .234 .200 .311 .295 .248 .238 .347 .332 .430 1.000 pereps15 .098 .079 .104 .193 .013 .002 .041 .234 .260 .238 .168 .220 .713 .387 1.000 Table 4 KMO and Bartlett's test results Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) 0.844 Bartlett's Test of Sphericity Approx. Chi-Square 1758.208 p-value < 0.001 The scree plot (Fig. 1 ) indicated a three-factor solution. Principal axis factoring with varimax rotation extracted three factors, explaining 62% of the total variance (Table 5 ). One item (Item 1) was excluded due to a factor loading < 0.40 (Table 6 ). Table 7 summarizes descriptive statistics for each subscale following EFA. Table 5 Rotated factor loadings Factor Rotation Sums of Squared Loadings Total % of Variance Cumulative % 1 3.895 25.967 25.967 2 3.617 24.112 50.079 3 1.813 12.087 62.167 Table 6 Final item loadings post-exclusion Items Factor 1 Factor 2 Factor 3 1-Won’t be prepared for birth - 2-Pandemic could ruin birth plans .521 3-Won’t be accompanied at delivery .772 4-Being separated from baby at delivery .547 5-Won’t get needed prenatal care .872 6-Won’t get help after delivery .889 7-Not getting enough healthy food or sleep or exercise .650 8-Baby could get Covid-19 at hospital .869 9-I might get Covid-19 at hospital .901 10-Covid-19 could harm my baby .877 11-Concerned about going to prenatal appointments .564 12-Covid-19 could harm my pregnancy .737 13-Being pregnant giving me strength .893 14-Covid-19 helping appreciate being a parent .467 15-Having baby helping me get though hardships .764 Table 7 Descriptive statistics for PREPS dimensions Factor Mean Std.D Min Max Perinatal Infection Stress scale (PIS: 2–7) 10.11 5.23 6 30 Preparedness Stress scale (ps: 8–12) 9.80 5.29 5 25 Positive Appraisal scale (PA:13–15) 9.76 3.29 3 15 Pandemic-Related Pregnancy Stress (total) 29.67 11.05 14 65 5.4. Confirmatory Factor Analysis CFA was conducted on a separate sample of 150 participants. The measurement model is illustrated in Fig. 2 , showing standardized regression weights and inter-factor correlations among the three latent variables. 5.5. Model Fit Indices Fit indices for the refined CFA model are presented in Table 8 . Final model metrics were within acceptable thresholds: χ²/df = 1–5, CFI > 0.90, PCFI > 0.50, RMSEA = 0.05–0.10, and SRMR < 0.10. The initial misfit was resolved through four iterative modifications, resulting in an acceptable overall model fit. Table 8 Goodness-of-fit indices for the final CFA model model \(\:{\chi\:}^{2}/df\) CFI PCFI RMSEA SRMR Initial 3.80 0.88 0.71 0.12 0.09 Final 2.59 0.93 0.72 0.09 0.08 5.6. Convergent Validity Convergent validity was evaluated via Pearson correlations between the subscales of CAS and PRAQ (Table 9 ). Most coefficients were positive and statistically significant (p < 0.05), indicating adequate convergence. The strongest correlation was observed between the PA subscale of the PRAQ and the psychological anxiety subscale of the CAS, suggesting an overlap in underlying constructs. A small number of coefficients below 0.30 may reflect nuanced conceptual divergence. The total correlation between PREPS and the two comparison instruments was r = 0.44 ( p < 0.001), reflecting a moderate, statistically significant association and supporting convergent validity. Table 9. Pearson correlations between PREPS, CAS, and PRAQ subscales 6. Discussion This study evaluated the psychometric properties of the Persian version of the PREPS in a sample of pregnant women attending urban health centers in Gorgan. Originally developed and validated by Preis et al. in the United States via an online survey [11], the instrument has since been adapted in multiple countries [13–15]. Consistent with previous validations, the Persian version demonstrated acceptable reliability and validity. Internal consistency was strong, with all items showing corrected item-total correlations above 0.30 and a total Cronbach α of 0.897. This reliability coefficient exceeds those reported in prior studies: α = 0.68 in the original U.S. sample [11], α = 0.71 in the German version [13], α = 0.691 in the Polish version [14], and α = 0.77 in the Spanish version [15], suggesting enhanced reliability in the Persian adaptation. The original instrument included three subscales— PA , PS , and IS [11]—a structure replicated in the current study. One item was excluded due to a factor loading < 0.40, whereas previous validations retained all original items. This discrepancy may reflect contextual differences, as earlier studies collected data during the initial phase of the pandemic, while the present study was conducted at a later stage. CFA confirmed the three-factor structure with an acceptable model fit (RMSEA = 0.09, CFI = 0.93, SRMR = 0.08). These values align with prior studies: Preis et al. [11] (CFI = 0.93, RMSEA = 0.07, SRMR = 0.057), Germany [13] (CFI = 0.92, RMSEA = 0.073), Poland [14] (CFI = 0.979, RMSEA = 0.054), Spain [15] (CFI = 0.90, RMSEA = 0.050), and Italy [12] (CFI = 0.991, RMSEA = 0.060). Correlations with CAS and PRAQ supported convergent validity. The PA subscale demonstrated a strong association with the psychological anxiety dimension of CAS, while IS also correlated with the same domain, indicating conceptual alignment. Additionally, both PA and IS were linked to PRAQ subscales related to fear of childbirth and lifestyle disruption. PS showed a moderate correlation with fear of childbirth, further supporting construct validity. Stress levels on PA in this study were comparable to those reported in the original U.S. sample [11], while reported stress in PS (27.2%) and IS (29.1%) was substantially higher in the U.S. cohort. In contrast, the Italian validation found that only 9% of participants reported high levels of PS [12], a finding more aligned with the current study. Such cross-national variability may reflect differences in cultural response or timing within the pandemic cycle. Notably, in most studies, the highest stress levels were observed in IS . However, in the present study—conducted during the later stages of the pandemic—stress related to infection was lower, likely due to a diminished perception of health risk. 7. Strengths, Limitations, and Future Directions One limitation of this study is its cross-sectional design, which precluded assessment of test-retest reliability. However, a key strength lies in the heterogeneity of the sample. Participants were recruited from all urban health centers in Gorgan and included both low- and high-risk pregnancies, covering various gestational stages during the final phase of the pandemic. This diversity enhances the generalizability of findings. Future research should adopt longitudinal designs to evaluate the temporal stability and predictive validity of the scale for outcomes such as preterm birth, postpartum depression, and neonatal health indicators. Expanding validation across culturally, geographically, and socioeconomically diverse populations within Iran and other Farsi-speaking communities would further strengthen external validity. Additional psychometric assessments, including discriminant and criterion validity, are recommended to provide a more comprehensive evaluation of the instrument's clinical utility. 8. Conclusion The Persian version of the PREPS demonstrated strong psychometric properties, including high internal consistency and confirmed construct and convergent validity. This instrument is appropriate for identifying pregnant women experiencing pandemic-related stress and can support early psychological intervention during future public health emergencies. Abbreviations AMOS – Analysis of Moment Structures CAS – Corona Anxiety Scale CFA – Confirmatory Factor Analysis CFI – Comparative Fit Index EFA – Exploratory Factor Analysis KMO – Kaiser–Meyer–Olkin ML – Maximum Likelihood PA – Positive Appraisal PCFI – Parsimonious Comparative Fit Index IS – Infection Stress PREPS – Pandemic-Related Pregnancy Stress Scale PS – Preparedness Stress PRAQ – Pregnancy-Related Anxiety Questionnaire RMSEA – Root Mean Square Error of Approximation SPSS – Statistical Package for the Social Sciences SRMR – Standardized Root Mean Square Residual Declarations Acknowledgments : The authors thank the Vice Chancellor for Research and Technology of Golestan University of Medical Sciences for approving this project. Special appreciation is extended to the pregnant women who participated in the study. Ethics approval and consent to participate: This study was approved by the Research and Technology Council of Golestan University of Medical Sciences and received ethical clearance from the Regional Committee on Medical Research Ethics (IR.GOUMS.REC.1401.404). All ethical consideration were taken into account in accordance with the Declaration of Helsinki .Written informed consent was obtained from all participants in the presence of a witness. Participants were informed of the study's objectives, procedures, potential benefits, and risks and assured that their participation was voluntary. Data confidentiality was maintained throughout the data collection, storage, and analysis processes. Consent for publication: All authors have consent for publication. Availability of data and materials: The datasets used and analyzed during the current study available from the corresponding author on reasonable request. Competing interests: The authors declare any conflict of interest. Funding: Research and technology deputy of Golestan University of medical sciences funding this research . Authors' contributions: "Author N.B and R.N participated in design of the work; analysis, interpretation of data; N.B and R.N and A.V have drafted the work and A.V did data sampling. All authors approved the submitted version; reviewed and revised the manuscript and are accountable for their contributions." Acknowledgements : This article is derived from a research project. The authors gratefully acknowledge the support of the Vice Chancellor for Research and Technology at Golestan University of Medical Sciences for approving the research protocol. The authors also sincerely thank all pregnant women participants for cooperating and engaging in this study. References Azh, N., et al., Relationship between maternal stress and pregnancy outcomes: A prospective study. The Iranian Journal of Obstetrics, Gynecology and Infertility, 2019. 22 (5): p. 27-36 . DOI:10.22038/IJOGI.2019.13579. 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DOI/URL: https://doi.org/10.2147/JMDH.S273217 Publisher page: https://www.dovepress.com/development-and-validation-of-psychological-contract-scale-for-hospital-peer-reviewed-article-JMDH Additional Declarations No competing interests reported. Supplementary Files floatimage1.png Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor invited by journal 11 Jun, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 03 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6813404","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484007080,"identity":"b7180f7a-f1bd-43a5-9536-59822cd845ce","order_by":0,"name":"Narjes Sadat Borghei","email":"","orcid":"","institution":"Counseling and Reproductive Health Research Center, School of Nursing and Midwifery,Golestan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Narjes","middleName":"Sadat","lastName":"Borghei","suffix":""},{"id":484007081,"identity":"911ce122-8241-478f-97d4-dddebf403c96","order_by":1,"name":"Roya Nikbakht","email":"data:image/png;base64,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","orcid":"","institution":"Department of Biostaticstics and Epidemiology, Faculty of Health, Mazandaran University of Medical sciences","correspondingAuthor":true,"prefix":"","firstName":"Roya","middleName":"","lastName":"Nikbakht","suffix":""},{"id":484007082,"identity":"d3a5912e-1e3c-456d-b7a1-0dc2bc2499a0","order_by":2,"name":"Artadokht Vosogh","email":"","orcid":"","institution":"Counseling and Reproductive Health Research Center, School of Nursing and Midwifery,Golestan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Artadokht","middleName":"","lastName":"Vosogh","suffix":""}],"badges":[],"createdAt":"2025-06-03 16:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6813404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6813404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86829339,"identity":"317c7e76-014c-4c02-8cef-9352570a3f57","added_by":"auto","created_at":"2025-07-16 05:45:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135022,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot identifying extracted factors (Eigen value)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6813404/v1/eea17e69704b6a01ff316893.png"},{"id":86829729,"identity":"3cb9f7ef-4b64-4cb5-b6d3-86a41ed13beb","added_by":"auto","created_at":"2025-07-16 05:53:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11863,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement model with standardized estimates\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6813404/v1/909e5da09bfe0c22a0be6199.png"},{"id":86831289,"identity":"d55e4758-68c6-491a-b2ef-a4e516a5f199","added_by":"auto","created_at":"2025-07-16 06:09:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1751128,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6813404/v1/ea083841-7f5f-47cc-ab8c-59585032b2fa.pdf"},{"id":86829730,"identity":"e6c79d6a-72a3-4598-856a-53824baf52cd","added_by":"auto","created_at":"2025-07-16 05:53:06","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1492647,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6813404/v1/e53fdb9f40e89ae9bc34d0b5.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric Evaluation of the Persian Version of the Pandemic- Related Pregnancy Stress Scale","fulltext":[{"header":"Research Highlights","content":"\u003cul\u003e\n \u003cli\u003eValidated the Persian version of the PREPS\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Demonstrated high internal consistency (Cronbach \u0026alpha; = 0.897)\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Factor analyses supported a three-factor structure\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Confirmed convergent validity with anxiety-related scales\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Identifies high-stress pregnant women for early intervention during pandemics\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003ePsychological disorders, particularly antenatal anxiety, have been associated with adverse pregnancy outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Maternal anxiety and emotional dysregulation influence fetal development by activating the maternal nervous system and promoting the release of acetylcholine and epinephrine. These neurochemicals can cross the placenta and disrupt fetal physiological processes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], potentially contributing to reduced birth weight, altered immune responses, and dysregulation in neurotransmitter secretion, including dopamine, serotonin, γ-aminobutyric acid (GABA), and norepinephrine [3]. Antenatal anxiety has been linked to a range of adverse perinatal outcomes [4], such as preeclampsia, prenatal depression, hyperemesis gravidarum, preterm birth, low birth weight, and diminished Apgar scores [5]. The cumulative evidence underscores the significant impact of maternal anxiety and stress on obstetric and neonatal health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePregnancy, even in non-crisis conditions, may be inherently stressful for some women. During the recent pandemic (COVID-19), this burden was magnified by infection-related fears, social distancing mandates, mobility restrictions, and quarantine measures [3,7]. These circumstances contributed to increased rates of depression, anxiety, and psychological distress among pregnant women [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Concerns over viral exposure led some individuals to postpone or cancel prenatal visits. In more extreme cases, anxiety prompted considerations of pregnancy termination or elective cesarean delivery [5]. Widespread reports indicated moderate to severe anxiety levels in pregnant populations during the pandemic, primarily due to fears surrounding maternal and neonatal infection and hesitancy about attending healthcare appointments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile various scales exist for general stress assessment, few instruments specifically address stressors unique to pregnancy. The Pandemic-Related Pregnancy Stress Scale (PREPS), developed and validated by Heidi Preis, was the first tool designed to measure pandemic-specific stress in pregnant populations [11]. It has since been adapted and psychometrically validated in several languages, including Italian [12], German [13], Polish [14], and Spanish [15]. However, a validated Persian version remains unavailable.\u003c/p\u003e\u003cp\u003eIn light of the ongoing vulnerability of pregnant women during public health crises and the likelihood of future pandemics, this study aimed to validate the Persian version of the PREPS in an Iranian sample. Establishing a reliable instrument for pandemic-related pregnancy stress is essential for enhancing maternal care in emergency contexts.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Design\u003c/h2\u003e\u003cp\u003eThis study employed a methodological design to evaluate the psychometric properties of the target instrument.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Study Population\u003c/h2\u003e\u003cp\u003eThe study population comprised pregnant women attending urban comprehensive health centers in Gorgan, Iran.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Sample and Eligibility Criteria\u003c/h2\u003e\u003cp\u003eParticipants were recruited from pregnant women presenting to the specified centers who met the following inclusion criteria: Iranian nationality, literacy in reading and writing, and singleton pregnancy. Exclusion criteria included unwillingness to participate; diagnosis of chronic conditions such as diabetes or hypertension; confirmed COVID-19 infection during the study period; a history of anxiety disorders or use of psychiatric medications prior to the pandemic; or recent exposure to a major stressful event such as the death of a first-degree relative from COVID-19 within the previous six months.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Sample Size\u003c/h2\u003e\u003cp\u003eSample size determination for factor analysis followed the convention of 10 participants per item [16]. The PREPS consists of 15 items; therefore, 150 participants were enrolled for exploratory factor analysis (EFA), and 150 separate participants were included for confirmatory factor analysis (CFA), resulting in a total sample of 300.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data Collection Procedure\u003c/h2\u003e\u003cp\u003e Following ethical approval from the Vice-Chancellor for Research and Technology at Golestan University of Medical Sciences, 300 eligible participants were recruited using convenience sampling from urban health centers in Gorgan. Data collection involved the use of self-administered instruments, including a demographic and reproductive history questionnaire, the Persian version of the PREPS, and the Coronavirus Disease Anxiety Scale.\u003c/p\u003e\u003cp\u003eAll procedures were conducted following the ethical standards of research. The researcher introduced herself to each participant, explained the study objectives in detail, and obtained written informed consent. Participation was entirely voluntary.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Instruments","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Demographic and Obstetric Information Form\u003c/h2\u003e\u003cp\u003eThis form comprised eight items evaluating demographic characteristics (age, literacy, occupation, and ethnicity) and obstetric history (gravidity, gestational age, number of living children, and history of abortion).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Corona Anxiety Scale\u003c/h2\u003e\u003cp\u003eThe Corona Anxiety Scale (CAS), developed by Alipour et al. in 2019 (1398 in the Iranian calendar), was designed during the early phase of the COVID-19 pandemic. It consists of 18 items, divided into two subscales: psychological symptoms (items 1\u0026ndash;9) and physical symptoms (items 10\u0026ndash;18). Each item is rated on a 4-point Likert scale, ranging from 0 (never) to 3 (always), yielding a total score of 0 to 54. Higher scores indicate greater anxiety. Internal consistency coefficients were high (total: α\u0026thinsp;=\u0026thinsp;0.919; psychological: α\u0026thinsp;=\u0026thinsp;0.879; physical: α\u0026thinsp;=\u0026thinsp;0.861). Validity has been confirmed through exploratory and confirmatory factor analyses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Pregnancy-Related Anxiety Questionnaire\u003c/h2\u003e\u003cp\u003eDeveloped by Van den Bergh in 1989, this 17-item scale assesses pregnancy-specific anxiety across five subscales: fear of childbirth (3 items), fear of delivering a child with disabilities (4 items), fear of changes in marital relationships (3 items), fear of emotional instability affecting the fetus (3 items), and fear of personal life disruption due to pregnancy (4 items). Validity and reliability were confirmed in studies by Sarmad et al. (2011) and Karamoziyan et al. (2016), with Cronbach α values exceeding 0.70 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Pandemic-Related Pregnancy Stress Scale\u003c/h2\u003e\u003cp\u003eThe PREPS, developed by Preis et al. in 2021, was administered to 4,451 pregnant women in the United States between April and May 2020 via an online survey. It comprises 15 items measuring three dimensions: Preparedness Stress (PS; 7 items), Infection Stress (IS; 5 items), and Positive Appraisal (PA; 3 items). Responses are recorded on a 5-point Likert scale from 1 (very little) to 5 (very much). There are no reverse-coded items, and the total score is calculated as the mean of all items. Reported internal consistency ranged from α\u0026thinsp;=\u0026thinsp;0.68 to 0.86 [11].\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Data Analysis and Psychometric Procedures","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Data Analysis\u003c/h2\u003e\u003cp\u003eData from each completed questionnaire were entered into SPSS version 18. Descriptive statistics were used to summarize demographic and obstetric variables. Structural analyses were conducted using AMOS version 24.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Psychometric Evaluation\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1. Translation and Cultural Adaptation\u003c/h2\u003e\u003cp\u003eThe original PREPS, developed in English, was translated and culturally adapted into Persian following formal permission from the original developer. The process adhered to the World Health Organization's standardized four-step protocol [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFace validity was evaluated qualitatively through structured interviews with 10 pregnant women. Items were assessed for clarity, relevance to the intended constructs, and potential ambiguity. Based on participant feedback, items were refined to ensure semantic clarity and cultural appropriateness. The finalized Persian version was pilot-tested and then administered to the full sample in the second half of 2022.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2. Reliability Assessment\u003c/h2\u003e\u003cp\u003eInternal consistency was assessed using Cronbach α, with a threshold of 0.70 considered acceptable [20]. Two additional diagnostics were applied: corrected item-total correlation and Cronbach's Alpha if an item is deleted.\u003c/p\u003e\u003cp\u003eCorrected Item\u0026ndash;Total Correlation examined the correlation between each item and the total score. Items with correlations below 0.30 were flagged for review, and those with correlations below 0.20 were excluded from further analysis.\u003c/p\u003e\u003cp\u003eCronbach's Alpha if Item Deleted was used to assess whether removing a specific item would enhance overall reliability [21,22].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3. Construct Validity\u003c/h2\u003e\u003cp\u003eConstruct validity was assessed through EFA using principal axis factoring with varimax rotation. Prior to EFA, assumptions were verified, including inter-item correlations (preferably\u0026thinsp;\u0026ge;\u0026thinsp;0.30) and sampling adequacy.\u003c/p\u003e\u003cp\u003eThe Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) statistic and Bartlett's test of sphericity were used to determine suitability for factor analysis. A KMO value\u0026thinsp;\u0026gt;\u0026thinsp;0.60 and a significant Bartlett's test indicated adequate factorability [23].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.2.4. Confirmatory Factor Analysis\u003c/h2\u003e\u003cp\u003eCFA was conducted following EFA to evaluate the proposed factor structure. Relationships between latent factors and their observed variables were examined using maximum likelihood estimation in AMOS version 24.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.2.5. Convergent Validity\u003c/h2\u003e\u003cp\u003eConvergent validity was evaluated by examining correlations between the PREPS, the CAS [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and the Pregnancy-Related Anxiety Questionnaire [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant across all analyses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1. Descriptive Findings\u003c/h2\u003e\n \u003cp\u003eA total of 300 pregnant women completed the study. The mean age was 28.05\u0026thinsp;\u0026plusmn;\u0026thinsp;6.32 years, and the mean gestational age was 24.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10 weeks. As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, most participants were housewives, had moderate educational levels, were nulliparous, and identified as Persian with regard to stress levels across the PREPS subscales. Specifically, 3.0% of participants reported high stress on the IS subscale, 7.0% on the PS subscale, and 10.7% on the PA subscale (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and fertility characteristics of participant pregnant women\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epercent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNot Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiteracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e128\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiploma\u0026amp; foghdiplom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e109\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLicence \u0026amp; higher\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFars\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e287\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurkmen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e123\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e81\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMore than 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving child\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e158\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMore than 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbortion history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eno\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e222\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eyes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbortion type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003espontaneos\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eInduced\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePREPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2. Reliability Assessment\u003c/h2\u003e\n \u003cp\u003eReliability indicators are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. All items demonstrated corrected item-total correlations\u0026thinsp;\u0026gt;\u0026thinsp;0.30. The overall Cronbach \u0026alpha; was 0.897, reflecting strong internal consistency (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eReliability coefficients for PREPS items\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItemes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScale Mean if Item Deleted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScale Variance if Item Deleted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrected Item-Total Correlation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCronbach\u0026apos;s Alpha if Item Deleted\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.2840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.4074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.3148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.5309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.4506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.6111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.9691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.5617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.8395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.6420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.1173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3. Construct Validity: Exploratory Factor Analysis\u003c/h2\u003e\n \u003cp\u003eEFA was performed on responses from 150 participants. No extreme outliers were detected. Inter-item correlations were adequate for most items (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). All KMO values exceeded 0.63, with an overall KMO of 0.84, and Bartlett\u0026apos;s test was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming sampling adequacy (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eInter-item correlation matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epereps15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKMO and Bartlett\u0026apos;s test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBartlett\u0026apos;s Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApprox. Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1758.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eThe scree plot (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) indicated a three-factor solution. Principal axis factoring with varimax rotation extracted three factors, explaining 62% of the total variance (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). One item (Item 1) was excluded due to a factor loading\u0026thinsp;\u0026lt;\u0026thinsp;0.40 (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes descriptive statistics for each subscale following EFA.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eRotated factor loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRotation Sums of Squared Loadings\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% of Variance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFinal item loadings post-exclusion\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-Won\u0026rsquo;t be prepared for birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-Pandemic could ruin birth plans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-Won\u0026rsquo;t be accompanied at delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-Being separated from baby at delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-Won\u0026rsquo;t get needed prenatal care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6-Won\u0026rsquo;t get help after delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7-Not getting enough healthy food or sleep or exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8-Baby could get Covid-19 at hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9-I might get Covid-19 at hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10-Covid-19 could harm my baby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11-Concerned about going to prenatal appointments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12-Covid-19 could harm my pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13-Being pregnant giving me strength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14-Covid-19 helping appreciate being a parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15-Having baby helping me get though hardships\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive statistics for PREPS dimensions\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd.D\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerinatal Infection Stress scale (PIS: 2\u0026ndash;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreparedness Stress scale (ps: 8\u0026ndash;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive Appraisal scale (PA:13\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePandemic-Related Pregnancy Stress (total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4. Confirmatory Factor Analysis\u003c/h2\u003e\n \u003cp\u003eCFA was conducted on a separate sample of 150 participants. The measurement model is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, showing standardized regression weights and inter-factor correlations among the three latent variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n \u003ch2\u003e5.5. Model Fit Indices\u003c/h2\u003e\n \u003cp\u003eFit indices for the refined CFA model are presented in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Final model metrics were within acceptable thresholds: \u0026chi;\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1\u0026ndash;5, CFI\u0026thinsp;\u0026gt;\u0026thinsp;0.90, PCFI\u0026thinsp;\u0026gt;\u0026thinsp;0.50, RMSEA\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.10, and SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.10. The initial misfit was resolved through four iterative modifications, resulting in an acceptable overall model fit.\u003c/p\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eGoodness-of-fit indices for the final CFA model\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emodel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}/df\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n \u003ch2\u003e5.6. Convergent Validity\u003c/h2\u003e\n \u003cp\u003eConvergent validity was evaluated via Pearson correlations between the subscales of CAS and PRAQ (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). Most coefficients were positive and statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating adequate convergence.\u003c/p\u003e\n \u003cp\u003eThe strongest correlation was observed between the PA subscale of the PRAQ and the psychological anxiety subscale of the CAS, suggesting an overlap in underlying constructs. A small number of coefficients below 0.30 may reflect nuanced conceptual divergence.\u003c/p\u003e\n \u003cp\u003eThe total correlation between PREPS and the two comparison instruments was r\u0026thinsp;=\u0026thinsp;0.44 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reflecting a moderate, statistically significant association and supporting convergent validity.\u003c/p\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;margin:0in;text-align:;;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003eTable 9.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size:15px;\"\u003e Pearson correlations between PREPS, CAS, and PRAQ subscales\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-right:0in;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;margin:0in;text-align:justify;line-height:12.0pt;'\u003e\u003cimg 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\" width=\"1287\" height=\"719\"\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis study evaluated the psychometric properties of the Persian version of the PREPS in a sample of pregnant women attending urban health centers in Gorgan. Originally developed and validated by Preis et al. in the United States via an online survey [11], the instrument has since been adapted in multiple countries [13\u0026ndash;15]. Consistent with previous validations, the Persian version demonstrated acceptable reliability and validity.\u003c/p\u003e\u003cp\u003eInternal consistency was strong, with all items showing corrected item-total correlations above 0.30 and a total Cronbach α of 0.897. This reliability coefficient exceeds those reported in prior studies: α\u0026thinsp;=\u0026thinsp;0.68 in the original U.S. sample [11], α\u0026thinsp;=\u0026thinsp;0.71 in the German version [13], α\u0026thinsp;=\u0026thinsp;0.691 in the Polish version [14], and α\u0026thinsp;=\u0026thinsp;0.77 in the Spanish version [15], suggesting enhanced reliability in the Persian adaptation.\u003c/p\u003e\u003cp\u003eThe original instrument included three subscales\u0026mdash;\u003cb\u003ePA\u003c/b\u003e, \u003cb\u003ePS\u003c/b\u003e, and \u003cb\u003eIS\u003c/b\u003e [11]\u0026mdash;a structure replicated in the current study. One item was excluded due to a factor loading\u0026thinsp;\u0026lt;\u0026thinsp;0.40, whereas previous validations retained all original items. This discrepancy may reflect contextual differences, as earlier studies collected data during the initial phase of the pandemic, while the present study was conducted at a later stage.\u003c/p\u003e\u003cp\u003eCFA confirmed the three-factor structure with an acceptable model fit (RMSEA\u0026thinsp;=\u0026thinsp;0.09, CFI\u0026thinsp;=\u0026thinsp;0.93, SRMR\u0026thinsp;=\u0026thinsp;0.08). These values align with prior studies: Preis et al. [11] (CFI\u0026thinsp;=\u0026thinsp;0.93, RMSEA\u0026thinsp;=\u0026thinsp;0.07, SRMR\u0026thinsp;=\u0026thinsp;0.057), Germany [13] (CFI\u0026thinsp;=\u0026thinsp;0.92, RMSEA\u0026thinsp;=\u0026thinsp;0.073), Poland [14] (CFI\u0026thinsp;=\u0026thinsp;0.979, RMSEA\u0026thinsp;=\u0026thinsp;0.054), Spain [15] (CFI\u0026thinsp;=\u0026thinsp;0.90, RMSEA\u0026thinsp;=\u0026thinsp;0.050), and Italy [12] (CFI\u0026thinsp;=\u0026thinsp;0.991, RMSEA\u0026thinsp;=\u0026thinsp;0.060).\u003c/p\u003e\u003cp\u003eCorrelations with CAS and PRAQ supported convergent validity. The \u003cb\u003ePA\u003c/b\u003e subscale demonstrated a strong association with the psychological anxiety dimension of CAS, while \u003cb\u003eIS\u003c/b\u003e also correlated with the same domain, indicating conceptual alignment. Additionally, both \u003cb\u003ePA\u003c/b\u003e and \u003cb\u003eIS\u003c/b\u003e were linked to PRAQ subscales related to fear of childbirth and lifestyle disruption. \u003cb\u003ePS\u003c/b\u003e showed a moderate correlation with fear of childbirth, further supporting construct validity.\u003c/p\u003e\u003cp\u003eStress levels on \u003cb\u003ePA\u003c/b\u003e in this study were comparable to those reported in the original U.S. sample [11], while reported stress in \u003cb\u003ePS\u003c/b\u003e (27.2%) and \u003cb\u003eIS\u003c/b\u003e (29.1%) was substantially higher in the U.S. cohort. In contrast, the Italian validation found that only 9% of participants reported high levels of \u003cb\u003ePS\u003c/b\u003e [12], a finding more aligned with the current study. Such cross-national variability may reflect differences in cultural response or timing within the pandemic cycle. Notably, in most studies, the highest stress levels were observed in \u003cb\u003eIS\u003c/b\u003e. However, in the present study\u0026mdash;conducted during the later stages of the pandemic\u0026mdash;stress related to infection was lower, likely due to a diminished perception of health risk.\u003c/p\u003e"},{"header":"7. Strengths, Limitations, and Future Directions","content":"\u003cp\u003eOne limitation of this study is its cross-sectional design, which precluded assessment of test-retest reliability. However, a key strength lies in the heterogeneity of the sample. Participants were recruited from all urban health centers in Gorgan and included both low- and high-risk pregnancies, covering various gestational stages during the final phase of the pandemic. This diversity enhances the generalizability of findings.\u003c/p\u003e\u003cp\u003eFuture research should adopt longitudinal designs to evaluate the temporal stability and predictive validity of the scale for outcomes such as preterm birth, postpartum depression, and neonatal health indicators. Expanding validation across culturally, geographically, and socioeconomically diverse populations within Iran and other Farsi-speaking communities would further strengthen external validity. Additional psychometric assessments, including discriminant and criterion validity, are recommended to provide a more comprehensive evaluation of the instrument's clinical utility.\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThe Persian version of the PREPS demonstrated strong psychometric properties, including high internal consistency and confirmed construct and convergent validity. This instrument is appropriate for identifying pregnant women experiencing pandemic-related stress and can support early psychological intervention during future public health emergencies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMOS \u0026ndash; Analysis of Moment Structures\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CAS \u0026ndash; Corona Anxiety Scale\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CFA \u0026ndash; Confirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CFI \u0026ndash; Comparative Fit Index\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;EFA \u0026ndash; Exploratory Factor Analysis\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;KMO \u0026ndash; Kaiser\u0026ndash;Meyer\u0026ndash;Olkin\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ML \u0026ndash; Maximum Likelihood\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PA \u0026ndash; Positive Appraisal\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PCFI \u0026ndash; Parsimonious Comparative Fit Index\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IS \u0026ndash; Infection Stress\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PREPS \u0026ndash; Pandemic-Related Pregnancy Stress Scale\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PS \u0026ndash; Preparedness Stress\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PRAQ \u0026ndash; Pregnancy-Related Anxiety Questionnaire\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;RMSEA \u0026ndash; Root Mean Square Error of Approximation\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SPSS \u0026ndash; Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SRMR \u0026ndash; Standardized Root Mean Square Residual\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments :\u0026nbsp;\u003c/strong\u003e The authors thank the Vice Chancellor for Research and Technology of Golestan University of Medical Sciences for approving this project. Special appreciation is extended to the pregnant women who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This study was approved by the Research and Technology Council of Golestan University of Medical Sciences and received ethical clearance from the Regional Committee on Medical Research Ethics (IR.GOUMS.REC.1401.404). All ethical consideration were taken into account in accordance with the Declaration of Helsinki .Written informed consent was obtained from all participants in the presence of a witness. Participants were informed of the study\u0026apos;s objectives, procedures, potential benefits, and risks and assured that their participation was voluntary. Data confidentiality was maintained throughout the data collection, storage, and analysis processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors have consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets used and analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare any conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Research and technology deputy of Golestan University of medical sciences funding this research\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003e\u0026quot;Author N.B and R.N participated in design of the work; analysis, interpretation of data; N.B and R.N and A.V have drafted the work\u0026nbsp;and\u0026nbsp;A.V did\u0026nbsp;data sampling. All authors approved the submitted version; reviewed\u0026nbsp;and revised\u0026nbsp;the manuscript and are accountable for their contributions.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: This article is derived from a research project. The authors gratefully acknowledge the support of the Vice Chancellor for Research and Technology at \u003cem\u003eGolestan University of Medical Sciences\u003c/em\u003e for approving the research protocol. The authors also sincerely thank all pregnant women participants for cooperating and engaging in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAzh, N., et al., Relationship between maternal stress and pregnancy outcomes: A prospective study. The Iranian Journal of Obstetrics, Gynecology and Infertility, 2019. \u003cstrong\u003e22\u003c/strong\u003e(5): p. 27-36\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e DOI:10.22038/IJOGI.2019.13579. 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Journal of Multidisciplinary Healthcare, 2020. \u003cstrong\u003e13\u003c/strong\u003e: p. 1433\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e DOI/URL: https://doi.org/10.2147/JMDH.S273217\u003cbr\u003e Publisher page: https://www.dovepress.com/development-and-validation-of-psychological-contract-scale-for-hospital-peer-reviewed-article-JMDH\u003c/li\u003e\n\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":"Anxiety, COVID-19, perinatal outcomes, Pandemic-Related Pregnancy Stress Scale, psychometrics","lastPublishedDoi":"10.21203/rs.3.rs-6813404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6813404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eThe pandemic event introduced widespread challenges, with pregnant individuals experiencing heightened stress due to infection risks. Despite its potential relevance for future outbreaks, the Pandemic-Related Pregnancy Stress Scale (PREPS) has not undergone psychometric evaluation in Iran. This study assessed the psychometric properties of the Persian PREPS in a sample of Iranian pregnant women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis methodological study was conducted in the latter half of 2022. The translation and cultural adaptation were conducted in accordance with the World Health Organization's standardized protocol. A total of 300 pregnant women receiving care at comprehensive health service centers in Gorgan were recruited via convenience sampling, contingent upon meeting inclusion criteria and providing informed consent. Convergent validity was assessed using the Corona Disease Anxiety Scale and the Van den Bergh Pregnancy-Related Anxiety Questionnaire (PRAQ). Data analysis was performed using SPSS version 18 and AMOS version 24.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: All 300 participants completed the study. The mean age was 28.05 ± 6.32 years, and the mean gestational age was 24.41 ± 6.10 weeks. Most participants were nulliparous housewives with moderate educational attainment and of Persian ethnicity. PREPS scores indicated that 3.0–10.7% of respondents experienced high stress across subscales. Internal consistency was strong (Cronbach α = 0.897). Exploratory factor analysis (n = 150) yielded a three-factor structure, accounting for 62% of the total variance after removing one item with a low factor loading. Confirmatory factor analysis (n = 150) validated the model, with all fit indices exceeding acceptable thresholds after refinement. Convergent validity was supported by significant positive correlations between PREPS subscales and related anxiety measures (total r = 0.44, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), confirming satisfactory construct validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe Persian PREPS demonstrated strong psychometric performance, supporting its use for measuring pandemic-related stress in pregnant populations.\u003c/p\u003e","manuscriptTitle":"Psychometric Evaluation of the Persian Version of the Pandemic- Related Pregnancy Stress Scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 05:45:01","doi":"10.21203/rs.3.rs-6813404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"143961650100521712159102696033433831165","date":"2025-07-16T13:55:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T08:36:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236885448323673958453222153779039581349","date":"2025-07-11T08:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T08:31:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-11T10:02:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T00:34:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T00:31:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-06-03T16:31:17+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":"acafba9a-b3b5-4a20-8c93-81022fbbf708","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-16T05:45:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-16 05:45:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6813404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6813404","identity":"rs-6813404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}