Preliminary validation of a Turkish version of the Comprehensive Assessment of Acceptance and Commitment Therapy Processes in students of the emergency aid and disaster management

Preliminary validation of a Turkish version of the Comprehensive Assessment of Acceptance and Commitment Therapy Processes in students of the emergency aid and disaster management | 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 Article Preliminary validation of a Turkish version of the Comprehensive Assessment of Acceptance and Commitment Therapy Processes in students of the emergency aid and disaster management Öznur Çınar, Vildan Oral, Melikşah Turan, Ekrem Cengiz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7535464/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background: This study aimed to perform an across-cultural adaptation of the English version of The Comprehensive Assessment of Acceptance and Commitment Therapy Processes (CompACT) into Turkish and to evaluate its psychometric properties in emergency aid and disaster management students. Methods: The study was conducted in two phases. Phase 1 involved the translation and cross-cultural adaptation of the CompACT. In phase 2, a study was conducted to assess the validity and reliability of the CompACT. It was conducted among 302 emergency aid and disaster management students. Results: The exploratory factor analysis showed that the CompACT consisted of 3 factors explaining 75.848% of the total variance, comprising 20 items. Communality values varied between 0.537-0.959, and values of the items under each factor varied between 0.731-0.974. CFA analysis was performed with three possible model tests. It was determined that the three-factor model showed the best goodness of fit values. The general fit indices of the model were found to be appropriate, as indicated by the following values: CMIN/df: 2.590; GFI: 0.882; AGFI: 0.848; NFI: 0.941; CFI: 0.963; IFI: 0.9963; TLI: 0.956; RMSEA: 0.073. The heterotrait-monotrait ratio of correlations was used for convergent and divergent validity in the context of the factors of the scale, and the scale met the convergent and divergent validity conditions within its structure. Besides this, divergent validity was performed to test the comparative validity of the scale with different scales. It was found that a negative correlation between CompACT and Acceptance and Action Questionnaire (AAQ II) (r=‒0.365) and Depression Anxiety and Stress Scales (DASS-21) (r=‒0.403). CompACT had high internal consistency (all alpha and omega values ​​were found to be above 0.70) and test-retest reliability (r= 0.748). Conclusions: The results suggest that the 20-item CompACT is reliable and valid for measuring psychological flexibility in Turkish emergency aid and disaster management students. Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Comprehensive Assessment of Acceptance and Commitment Therapy Processes psychological flexibility reliability validity Turkey Figures Figure 1 Introduction Psychological flexibility was developed within the contextual behavioral science framework, a research paradigm that forms the basis of the recent development of process-based cognitive behavioral therapy [1]. Psychological flexibility is the rigid dominance of a person's values and psychological reactions among situational possibilities in guiding their actions [2]. It is defined as a person consciously establishing contact with the present time entirely and accurately, without using defense mechanisms, considering the situational context, and being consistent in their behaviors while serving their chosen values [3]. An alternative definition posits that psychological flexibility is how an individual employs specific strategies to recognize and manage diverse situational demands. These strategies include the following: providing an appropriate mentality change in case of situational differences, creating a balance between valued life areas, creating behaviors compatible with adopted values, and adhering to them [4]. In a broader sense, Williams et al. (2012) [5] defined psychological flexibility as the ability to manage a changing environment and interaction network through dynamic processes. These processes include being aware of the moment, changing perspectives, adapting to situational demands, balancing conflicting needs, and changing behaviors necessary for valuable goals and making them sustainable. Psychological flexibility significantly influences individuals' emotional, mental, and behavioral outcomes by means of certain concepts. Psychological flexibility has been demonstrated to facilitate effective coping with stressful situations [6]. Flexible individuals can accept stressful thoughts and feelings, disengage from them without becoming overwhelmed, and take actions that are consistent with their values [7]. Research has demonstrated that psychological flexibility functions as a protective factor against adverse life occurrences [8, 9]. Wersebe et al. (2018) [10] have demonstrated that psychological flexibility enhances an individual's capacity to cope with stress, thereby positively influencing their psychological well-being. Psychological flexibility fosters prolonged psychological well-being by empowering individuals to act according to their values in challenging circumstances [11]. Psychological flexibility is closely related to general psychological health. Individuals who demonstrate psychological flexibility exhibit reduced levels of depression, anxiety, and psychological distress [12, 13, 14, 15]. Furthermore, Wang et al. (2023) [9] have demonstrated that psychological flexibility facilitates more efficacious management of mental health disorders, such as depression and anxiety. The mechanisms by which psychological flexibility exerts these effects include increased tolerance for emotional experiences, as indicated by Wang et al. (2023) [9]. A growing body of research has identified psychological flexibility as a contributing factor to the positive management of social anxiety [16] and borderline personality disorder [17]. Furthermore, psychological flexibility has been demonstrated to substantially influence individuals' overall well-being [18, 19]. Guerrini et al. (2021) [20] have demonstrated that psychological flexibility enhances the psychological well-being of individuals. Coping with challenges more effectively has increased satisfaction and healthier emotional well-being [21, 22]. Psychological flexibility can be conceptualized as an integral component of one's repertoire of psychological resources [21, 22]. Psychological flexibility makes individuals more resilient to traumatic or stressful situations [23, 24, 25]. Kashdan and Ciarrochi (2013) [26] stated that psychological flexibility increases the ability of individuals to adopt healthy lifestyles, which leads to better physical health conditions. Individuals with high psychological flexibility are more flexible in dealing with emotional states [27]. Flexible individuals can accept and express their negative emotions in healthy ways rather than suppressing or avoiding them [28]. Psychological flexibility allows individuals to identify and act on their values. Flexible individuals can remain committed to their values and live meaningful lives despite difficulties [29, 30]. Psychological flexibility helps individuals to have a broader repertoire of behaviors and to adapt more quickly to changing conditions [4, 31]. Psychological flexibility is strongly related to self-compassion [32]. Flexible people can be kinder and more understanding, accept their mistakes and shortcomings, and support themselves without judgment [33]. More psychologically flexible people show higher levels of positive and lower levels of negative affect, resulting in higher quality social relationships [34, 35]. Psychological flexibility also increases life satisfaction [36]. Individuals engaged in disaster relief work are required to manage considerable stress, exposure to traumatic events, and perpetual uncertainty, a consequence of the nature of their profession. This necessitates that they possess elevated levels of psychological flexibility. Psychological flexibility is a salient characteristic that can facilitate disaster workers' ability to cope with challenging experiences, enhance their adaptability to fluctuating and demanding living conditions, safeguard their mental well-being, and maintain functionality. Disaster response teams and organizations must provide training and support to their employees to cultivate psychological flexibility skills. To this end, it is imperative to assess the psychological flexibility of disaster response workers, evaluate the results, and implement appropriate precautions based on the findings. A multitude of psychological flexibility scales have been documented in the extant literature. While these scales have inherent limitations for disaster workers, the differences between them and the contributions of each scale provide a comprehensive perspective on measuring this concept. While each scale addresses psychological flexibility from a distinct perspective, they exhibit deficiencies for groups operating under elevated stress, such as disaster workers. Consequently, there is a necessity for developing a more comprehensive and situation-specific scale, particularly for disaster workers who are routinely exposed to traumatic events. The development of such a scale could facilitate the design of effective professional support strategies by accurately assessing disaster workers' workload and capacity to cope with traumatic events. This scale could also facilitate a more profound understanding of the challenges encountered in the work environment, thereby enabling the development of more effective support strategies. In their study on psychological flexibility, Cherry et al. (2021) [37] identified 27 scales and subjected them to a quality rating. As a result of the quality rating, they stated that the CompACT scale was in the top three. CompACT is a 23-item, 3-factor (Openness to experience, valued action, and behavioral awareness) scale to assess ACT processes. The willingness of a human to experience internal events without attempting to control or avoid them is known as openness to experience, and it symbolizes the processes of "acceptance" and "cognitive defusion." Behavior awareness, which embodies the concepts of "self-as-context" and "contact with the present moment," is the ability to pay attention to one's behaviors. Valued action, which stands for the procedures of "value clarification" and "committed action," describes one's ability to perform valued acts. CompACT evaluates psychological flexibility in several ways [38]. This study aims to make a cross-cultural adaptation of the English version of CompACT in the context of Turkey and to evaluate its psychometric properties by adapting it to emergency aid and disaster management students. In this way, it is to present a scale that can accurately measure the psychological flexibility of disaster workers. The unique contribution of this study is that it pioneers the adaptation of a scale appropriate for the psychological flexibility assessment needs of disaster workers. Developing a scale sensitive to disaster workers' unique psychological needs will support the creation of effective interventions to protect their job performance and psychological well-being. In this way, the psychological flexibility levels of disaster workers can be assessed more accurately, and the necessary psychosocial support can be provided to increase their functionality. In addition, psychological flexibility may depend on specific cultural contexts. Therefore, examining the psychometric properties of translated psychological flexibility measures in different contexts is vital. In addition, this study will provide a better understanding of CompACT items from a Turkish cultural perspective, the discovery of potential new findings, and researchers' better understanding of CompACT. Methodology This study used a cross-sectional data collection method to test the results of the psychometric properties of the Turkish version of The Comprehensive Assessment of Acceptance and Commitment Therapy Process (CompACT) scale on emergency aid and disaster management students. Population and sample of the study The universe of this study is the students studying in the emergency aid and disaster management undergraduate departments at universities in Turkey. In total, 13,848 students are enrolled in this department as of 2025. It aimed to apply the survey to 350 samples determined by the convenience sampling method from this universe. For this purpose, students in three state universities' emergency aid and disaster management departments were reached, and the survey was applied. A total of 381 students were reached, and the research was conducted using the electronic and face-to-face survey application methods. Of the collected surveys, 79 were not included in the analysis because they contained incomplete data and appeared to be filled out randomly. Therefore, a total of 302 surveys were included in the analysis. Although there are different opinions on the adequacy of the sample size to be used in multivariate analyses, there is generally an opinion that more than 5 times the number of items is sufficient [39, 40]. The number of items used in this study was 23, and considering the total number of 302 subjects, a subject number slightly more than 13 times the number of items was deemed sufficient. Ethical Statement This study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. Ethical approval for this research was obtained from the XXX University Ethics Committee (Code: BS9BNDY28C, Issue: E45796069-204.01.07-266356, Date: 14.03.2025). Informed consent was obtained from all participants prior to their participation in the study. Participation was entirely voluntary, and participants were informed about the study’s purpose, procedures, potential risks, and their right to withdraw at any time without penalty. Written informed consent was collected from each participant on (15 March 2025-30 April 2025), at (classroom setting or online platform). No identifying information of participants was collected, and data were used solely for research purposes. All participants were adults, and no minors or legally dependent individuals were included. 2.3. Measures The present study used three different scales. The scales used are listed below. Comprehensive Assessment of Acceptance and Commitment Therapy (CompACT) The CompACT scale, developed by Francis et al. (2016) [38], is a 23-item, 3-factor scale (Openness to experience, behavioral awareness, valued action) designed to assess ACT processes. The original scale developed by Francis et al. (2016) [38] was used in this study. In the study conducted by Francis et al. (2016) [38], the total Internal reliability value (Cronbach alpha) of the scale was found to be 0.91. In the present study, items on the CompACT are scored from 1 (Strongly Disagree) to 7 (Strongly Agree) with total scores ranging from 0 to 161, where higher scores indicate greater levels of psychological flexibility. Acceptance and Action Questionnaire-II (AAQ-II) Bond et al. (2011) [2] developed AAQ-II, the most widely used scale to measure psychological flexibility. AAQ-II is a single-dimensional 7-item scale that measures only the experiential avoidance sub-dimension of psychological flexibility [31]. It has been observed that AAQ-II has a higher correlation with psychological distress and negative emotions than measuring psychological flexibility [41]. The original scale developed by Bond et al. (2011) [2] was used in the present study. In the original study, the scale's average Internal reliability value (Cronbach alpha) was found to be 0.84. In the present study, the internal consistency value of AAQ-II was 0.979. In the present study, a Likert-type scale that ran from 1 (never true) to 7 (always true) was used, with higher scores indicating greater levels of psychological inflexibility. Depression Anxiety Stress Scale-21 (DASS-21) This study used the DASS-21 scale developed by Henry and Crawford (2005) [42]. This scale is a distress measure assessing three separate constructs (Depression, anxiety, and stress). In the study conducted by Henry and Crawford (2005) [42], the total internal reliability value (Cronbach alpha) of the scale was found to be 0.93. In the present study, the internal consistency value of DASS-21 was 0.971. The scale contains 21 items, 7 of which measure stress, 7 of which measure anxiety, and 7 of which measure depression. In the present study, each item is scored from 1 (did not apply to me at all over the past week) to 4 (applied to me very much or most of the time over the past week). Higher scores indicate greater levels of distress. 2.4. Data analysis SPSS v.21 (Statistical Package for the Social Sciences) and AMOS v.22 (Analysis of Moment Structures) programs were used to analyze this research. Frequency and percentage were used for the demographic characteristics of the participants, skewness and kurtosis coefficients for the distribution of the data, explanatory factor analysis (EFA) was used to determine the factor structure, and confirmatory factor analysis (CFA) was used to verify the factor structure. In addition, test-retest (Pearson correlation), composite reliability (CR), average variance extracted (AVE), maximum shared variance (MSV), and heterotrait-monotrait (HTMT) ratio, Cronbach alpha (α) and McDonald's omega (ω) were used for validity and reliability. The Pearson correlation coefficient was examined for the divergent and convergent validity of the scale with other scales. The confidence interval in the analyses was determined as 95%. A maximum of 5% missing data per survey was allowed to be included in the analysis. 2.5. Translation process The original scale was independently translated into Turkish by three academics fluent in English (two in the emergency aid and disaster management department, one in the English language and literature department). Then, these three academics came together and combined the three different translations into a single translation in a coordinated manner. The research team evaluated the translation in terms of conceptual equivalence. The same three academics translated the text back into English. The research team members examined the scale translated into English in terms of its conformity with the original. In order to evaluate the understandability and cognitive equivalence of the resulting Turkish draft, a session was held with 15 students studying undergraduate education in emergency aid and disaster management. All participants were asked to express the meaning of each item in their own words and to provide the reasons for their responses. In light of the feedback from the session, the necessary adjustments were made to the scale draft, and the scale was finalized. The translation of the DASS-21 and AAQ-II scales used for reliability testing in the study was also carried out within the aforementioned process. 2.6. Pilot study Before starting the actual field research of the draft scale, it is necessary to conduct a pilot study to prevent possible problems by trying the sample-finding method, survey application methodology, data collection, and entry method in advance [43]. For this reason, a pilot study was conducted on a small sample representing the targeted universe. The convenience sampling method was applied to determine the sample, and a draft survey was applied to 40 emergency aid and disaster management students. The survey was conducted by the researchers in person and face-to-face. Thanks to the pilot study and the fact that this study was conducted in person by the researchers, problems that could arise under field conditions were determined in advance. In the pilot study, the difficulties encountered by the participants, the incomprehensible or misunderstood questions, and the appropriateness of the survey duration were evaluated. Immediate feedback was received from the participants, and errors in the survey draft and application were corrected. The time it took for each participant to complete the survey varied between 5 and 11 minutes. Results 3.1. Participant characteristics Table 1 shows the demographic characteristics of 302 participants. The majority of the participants were male (50.7%), senior grade (28.5%), unemployed status (90.1%), urban area residence (73.5%), and family income 23,001-76,000 TL (65.6%). Table 1. Demographical Characteristics of the Participants Gender Male: 153 (50.7%), Female: 149 (49.3%) Grade Freshmen: 70 (23.2%), Sophomores: 66 (21.9%), Junior: 80 (26.5%), Senior: 86 (28.5%) Student employment status Employed: 30 (9.9%), Unemployed: 272 (90.1%) Family residence Urban: 222 (73.5%), Rural: 80 (26.5%) Family income 0-23.000 TL: 71 (23.5%), 23.001-76.000 TL: 198 (65.6%), 76.001 TL and above: 33 (10.9%) 3.2. Distribution analysis Whether the data in the study show normal distribution can be tested using skewness and kurtosis coefficients [44]. According to Joanes & Gill (1998) [45], a value between ‒3 and +3 is sufficient for normal distribution. Outliers were examined in the study's data set, and no outliers were found. As a result of the skewness (0.382‒2.253) and kurtosis (0.010‒2.024) coefficients, it was found that the data showed normal distribution. 3.3. Exploratory factor analysis In the research, EFA was carried out to see the scale's factor structure (construct validity), and two tests were performed to determine the suitability of the data. The Kaiser-Meyer-Olkin (KMO) sample adequacy test shows the adequacy of the number of participants and should be over 0.50 [46, 47, 48, 49]. The Bartlett Sphericity test indicates whether the data show diversity, and the p-value should be less than 0.05 [50, 51]. In EFA, factors with eigenvalues greater than one were considered in determining the factor structure. It was deemed appropriate for the total variance rate explained in EFA to be over 50% [52, 53, 54]. In EFA, the direct oblimin method was preferred to the principal component analysis and oblique rotation methods for data extraction. The strategies used for item removal from the EFA analysis are as follows: Items with a factor loading value below 0.5 were removed. Attention was paid to having at least three items for each factor. Items with high cross-loadings (greater than 0.3 in more than one factor) and minimum difference in factor loading (<0.1) were removed from the analysis [55, 56, 57]. Those with a common factor variance value above 0.50 were considered [46]. Some assumptions were tested before proceeding to EFA in the study. As a result of the test, it was seen that there were no outliers in the data set, that the data showed a normal distribution, and that the variables were in a linear relationship with each other. It was also seen that there was no multicollinearity problem. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy value was determined as 0.894, and the Bartlett's Test of Sphericity value was determined as 7853.035 (df: 253; p: 0.000). When the total variance explained table was examined, it was seen that four factors were formed. The pattern matrix table determined that the OE3 and OE9 items were collected under the 4th factor. In addition, it was seen that the factor loading of the VA1 item was below 0.50. Therefore, these three items were removed from the analysis, and EFA was performed again. As a result of the second EFA, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy value was determined as 0.908, and the Bartlett's Test of Sphericity value was determined as 6935.438 (df: 190; p: 0.000). When the total variance explained table was examined, it was seen that there were three factors with eigenvalues greater than 1 explaining 75.848% of the total variance. The first factor explained 36.393% of the variance, the second factor 25.194%, and the third factor 14.260%. Communalities values varied between 0.537-0.959. When the pattern matrix was examined, it was seen that the values of the items under each factor varied between 0.731-0.974. At this stage, the reliability of the scale whose structural validity was tested was also examined. For this, Cronbach alpha (α) and item-total statistics values were examined. Item-total correlation shows the relationship between the scores obtained from the questionnaire items and the test's total score. A positive and high item-total correlation value emphasizes that the items exemplify similar characteristics. According to Büyüköztürk (2007) [58], items with item-total correlations below 0.20 are removed from the analysis. Cronbach alpha (α) measures internal consistency and is desired to be above 0.70 [59, 60]. The overall α value of the scale was found to be 0.843, 0.978 for the openness to experience subscale, 0.824 for the behavioral awareness subscale, and 0.937 for the valued action subscale. Item-total values were between 0.572 and 972 (Table 2). Table 2 . CompACT factor loadings, items descriptive statistics and reliability statistics Items Factors and factor loadings Descriptive 1 2 3 h 2 I-T α M SD OE 1 5.98 1.820 .918 -.032 .017 .851 .898 OE 2 5.88 1.999 .969 -.032 .001 .947 .963 OE 4 5.61 1.894 .955 -.006 -.018 .914 .940 OE 5 5.66 1.904 .974 -.033 -.017 .959 .972 .978 OE 6 5.54 1.888 .944 -.034 -.028 .901 .932 OE 7 5.48 1.894 .935 -.009 -.016 .877 .916 OE 8 5.42 1.984 .929 -.016 -.046 .870 .911 OE 10 6.07 1.631 .802 .118 .085 .638 .736 BA 1 2.16 .889 -.065 .029 .731 .537 .572 BA 2 2.63 1.086 -.029 -.023 .773 .603 .626 BA 3 2.84 1.479 .011 -.148 .808 .704 .703 .824 BA 4 2.97 1.225 .103 .061 .746 .557 .587 BA 5 3.08 1.278 -.021 .025 .786 .614 .655 VA 2 2.70 1.542 .009 .753 .006 .564 .687 VA 3 3.19 1.834 -.044 .781 .070 .612 .714 VA 4 3.04 1.576 -.006 .845 -.102 .747 .811 .937 VA 5 3.24 1.468 .031 .936 -.010 .872 .887 VA 6 3.29 1.479 .037 .906 .052 .806 .834 VA 7 3.40 1.417 .050 .891 -.044 .796 .828 VA 8 2.86 1.676 -.103 .875 -.016 .802 .844 Eigenvalue Variance Explained (%) 7.279 5.039 2.852 36.393 25.194 14.260 Total Variance (%) 75.848 OE: Openness to experience, BA: Behavioral awareness, VA: Valued action, M: Mean, SD:Standart Deviation, h 2 : Communality, I-T: Corrected item-total correlations, α: Subscale Cronbach alpha 3.4. Confirmatory factor analysis The study performed CFA analysis after EFA to ensure the scale's construct validity. CFA is a statistical method used to verify the estimated relationships. The scale's general goodness of fit indexes are examined in CFA, then the factor loadings (standardized regression coefficients) are evaluated [40]. General fit indexes must comply with the following values. χ²/sd (Chi-Square Statistic)≤5, GFI (Goodness of Fit Index) 0.90≤GFI≤0.95, AGFI (Adjusted Goodness of Fit Index) 0.85≤AGFI≤0.90, CFI (Comparative Fit Index) 0.90≤CFI≤0.95, NFI (Normed Fit Index) 0.90≤NFI≤0.95, IFI (Incremental Fit Index) 0.90≤IFI≤0.95, TLI (Tucker Lewis Index) 0.90≤TLI≤0.95, RMSEA (Root Mean Square Error of Approximation) 0.05≤RMSEA≤0.08 [61, 62, 63]. Standardized factor loading values must exceed 0.50 [61]. CFA analysis was performed with three possible model tests. All observed variables were evaluated under a single factor in the first model. Before the second model was developed, EFA was performed on the sample, and the number of factors was limited to 2 in this analysis. As a result of this analysis, it was determined that the items of the openness to experience subscale were collected under the first factor, and the items of the behavioral awareness and valued action subscales were collected under the second factor. Therefore, a two-factor CFA was performed with these results in the second CFA model. In the third model, a three-factor CFA analysis was performed, using the results of the three-factor EFA analysis that had been performed before. It was determined that the three-factor model showed the best goodness of fit values. After the modifications suggested for the three-factor model were implemented, the goodness of fit values obtained was higher (Final model) (Table 3). In the final model, GFI and AGFI values did not show good fit values and were below 0.90. However, values above 0.800 can be considered as 'acceptable' goodness of fit values [64, 65]. When the standardized factor loadings of the final model were examined, it was determined that they varied between 0.573 and 0.991 (Table 4). The AMOS output of the results of the final model is seen in Figure 1. Table 3. CompACT confirmatory factor analysis model fit indices Table 4. Regression weights of CFA results Std Estimate Estimate S.E. C.R. P OE 1 <--- Openness to Experience .892 1.000 - - - OE 2 <--- Openness to Experience .970 1.195 .039 30.503 *** OE 4 <--- Openness to Experience .944 1.101 .039 27.994 *** OE 5 <--- Openness to Experience .991 1.162 .035 32.747 *** OE 6 <--- Openness to Experience .947 1.102 .039 28.312 *** OE 7 <--- Openness to Experience .928 1.083 .041 26.665 - OE 8 <--- Openness to Experience .924 1.078 .041 26.334 *** OE 10 <--- Openness to Experience .727 .731 .039 18.662 *** BA 1 <--- Behavioral Awareness .660 1.000 - - - BA 2 <--- Behavioral Awareness .686 1.309 .136 9.643 *** BA 3 <--- Behavioral Awareness .814 2.116 .197 10.758 - BA 4 <--- Behavioral Awareness .637 1.372 .151 9.109 *** BA 5 <--- Behavioral Awareness .729 1.639 .162 10.084 *** VA 2 <--- Valued Action .573 1.000 - - - VA 3 <--- Valued Action .597 1.238 .101 12.260 *** VA 4 <--- Valued Action .719 1.281 .127 10.051 *** VA 5 <--- Valued Action 1.000 1.660 .137 12.103 *** VA 6 <--- Valued Action .937 1.568 .133 11.748 *** VA 7 <--- Valued Action .926 1.485 .127 11.676 *** VA 8 <--- Valued Action .805 1.525 .141 10.786 *** *p<.0001, S.E.: Standart error, C.R.: Critical ratio, p: Significance value 3.5. Other validity and reliability analyses In the study, the heterotrait-monotrait (HTMT) ratio of correlations was used for convergent and divergent validity in the context of the factors of the scale [66]. Convergent validity shows the relationships of the variables belonging to a factor with each other and with the factor they belong to. In order to ensure convergent validity, the CR values must be greater than 0.70, the AVE values must be greater than 0.50, and the CR values must be greater than the AVE values [67, 68]. Divergent validity shows that the relationship of the variables with the factor they belong to is higher than their relationship with other factors. If the correlation values of a factor with other factors are lower than the square root of the AVE of this factor [67, 69], and if the MSV values is smaller than the AVE values [70], then divergent validity is achieved [71]. In addition, for divergent validity, HTMT correlations must be less than 1.00 [72, 73]. Cronbach alpha, McDonald's omega, and CR values were examined for reliability. These values must be greater than 0.70 [59, 60]. The reliability and validity results of the structural measurements of the CompACT scale are shown in Table 5. In order to evaluate the internal consistency reliability in the study, Cronbach alpha and McDonald's omega values were examined separately for the entire scale and its three subscales. All alpha and omega values were determined to be above 0.70. It was also observed that CR values were above 0.80. When Table 5 is examined, it is seen that CR values are greater than 0.70, AVE values are greater than 0.50, and CR values are greater than the relevant AVE values. Therefore, convergent validity is provided. In Table 5, it is also determined that the correlation values of each factor with other factors are lower than the square root value of the AVE of the relevant factor (Values in bold on the diagonal), and its MSV is smaller than its AVE. In addition, it was observed that the HTMT correlations of the factors are less than 1.00. Therefore, divergent validity is provided. Test-retest reliability analysis shows the degree of consistency of the survey measurement results applied to the study participants at different periods [74]. In other words, it measures the consistency of the research results in the context of time. In this study, the same survey was applied to 30 participants randomly selected from 302 participants 14 days after the application of the first survey. Pearson correlation coefficient was used in the test-retest analysis. It was found that r=0.746 (p<0.01) for the openness to experience subscale, r=0.723 (p<0.01) for the behavioral awareness subscale, and r=0.781 (p<0.01) for the valued action subscale. It was found that r=0.748 (p<0.01) for global scale. Therefore, the test-retest reliability of the scale was ensured, and strong temporal stability was observed. In order to determine the divergent validity of the CompACT scale, which investigates the relationship between different scales, the Pearson (r) test was used. The relationship between CompACT and two psychometric scales (AAQ-II and DASS-21) was investigated. It was found that the global CompACT was negatively correlated with AAQ-II (r=‒0.365, p<0.01) and negatively correlated with DASS-21 (r=‒0.403, p<0.01). In addition, it was found that the subscales of the CompACT scale (Openness to experience, behavioral awareness, valued action) were negatively correlated with DASS-21 and AAQ-II (Table 6). While AAQ-II measures experiential avoidance and psychological inflexibility, DASS-21 measures negative emotions (Depression, anxiety, and stress). The negative correlations between CompACT and AAQ-II, DASS-21 shows that the divergent validity condition is met. Table 6 . Correlations of the CompACT with other scales Variables 1 2 3 4 5 6 7 8 9 1 Global scale 1 2 Openness to Experience .718** 1 3 Behavioral Awareness .331** -.007 1 4 Valued Action .448** -.138* -.148** 1 5 AAQ-II -.365** -.201** -.099 -.258** 1 6 DASS-21 -.403** -.123* -.239** -.322** .244** 1 7 DASS-21 Depression -.388** -.107 -.234** -.322** .223** .982** 1 8 DASS-21 Anxiety -.433** -.154** -.248** -.325** .267** .975** .934** 1 9 DASS-21 Stress -.359** -.098 -.219** -.298** .223** .978** .948** .922** 1 Note: Global scale is the CompACT total scores; AAQ-II is the Acceptance and Action Questionnaire-II; DASS-21 is the Depression Anxiety Stress Scale-21, **. Correlation is significant at the 0.01 level (2-tailed), *. Correlation is significant at the 0.05 level (2-tailed). Discussion The concept of psychological flexibility, one of the concepts that can increase disaster workers' ability to make urgent decisions about human life under high stress to adapt to variable and unpredictable disaster environments and focus on their duties, has attracted attention in recent years. Psychological flexibility is defined as the ability of an individual to restructure himself/herself in coping with stressful or challenging situations, adapt to events, and react flexibly [4]. This concept means that an individual can achieve his/her goals despite the negativities in his/her life by choosing appropriate thought and behavior patterns for the current situation [31]. Psychological flexibility enables a person to cope with challenging situations more effectively through emotional regulation, cognitive reframing, and awareness [75]. Therefore, psychological flexibility includes managing stressful events and individual development by learning from such events [4]. Although there are different scales in the literature that attempt to measure psychological flexibility, one of the most notable rating scores was The Comprehensive Assessment of Acceptance and Commitment Therapy Process (CompACT) scale developed by Francis et al. (2016) [37]. This study aims to make a cross-cultural adaptation of the English version of The Comprehensive Assessment of Acceptance and Commitment Therapy Process scale into Turkish and to evaluate its psychometric properties by adapting it to emergency aid and disaster management students. In this way, it is to present a scale that can accurately measure the psychological flexibility of disaster workers. In the study, EFA was first performed to determine the construct validity, and then CFA was performed to verify the construct. As a result of EFA, four factors with eigenvalues above one were identified. OE3 and OE9 items were observed to be grouped under the 4th factor, and the factor loading of the VA1 item was below 0.50. These items were removed, and EFA was performed again, resulting in a 3-factor structure. These three factors explained 75.848% of the total variance. The obtained factors (Openness to experience, behavioral awareness, and valued action) and the items under these factors were obtained like the original CompACT scale created by Francis et al. (2016) [38]. The global Cronbach alpha value of the scale performed at this stage was determined as 0.843. In the second stage of construct validity, CFA analysis was performed to verify the 3-factor structure obtained from EFA. Single-factor, two-factor, and three-factor models were completed, and it was determined that the three-factor model had the highest goodness of fit values. Some general goodness of fit values were good, and some were acceptable. All of the standardized regression coefficients were found to be above 0.600. Due to some relationships between the error terms in the three-factor CFA model, modifications were made, and the final model was obtained. The assumed structure was confirmed as a result of the CFA. In the study, the heterotrait-monotrait (HTMT) ratio of correlations was used for convergent and divergent validity in the context of the factors of the scale [66]. Cronbach alpha (α), McDonald's omega (ω), and composite reliability (CR) values were examined for reliability. It was determined that the CR values were greater than 0.70, the AVE values were greater than 0.50, and the CR values were greater than the relevant AVE values. Therefore, convergent validity was achieved between the factors of the scale. For the inter-factor divergent validity, it was determined that the correlation values of each factor with other factors were lower than the square root value of the AVE of the relevant factor, and its MSV was smaller than its AVE. However, the HTMT correlations of the factors were observed to be less than 1.00. Therefore, divergent validity was achieved. In order to evaluate the internal consistency reliability in the study, Cronbach alpha and McDonald's omega values were examined separately for the entire scale and its three subscales. It was observed that all alpha, omega, and CR values were above 0.70. In this study, the same survey was applied to 30 randomly selected participants out of 302 participants 14 days after the first survey was applied. Both global scale and subscales Pearson correlation coefficients were found to be greater than 0.70. Therefore, the test-retest reliability of the scale was ensured, and strong temporal stability was observed. In order to determine the divergent validity that investigates the relationship between the CompACT scale and different scales, the relationships between CompACT and two psychometric scales (AAQ-II and DASS-21) were investigated. It was found that there were negative relationships between global CompACT and AAQ-II, DASS-21. In addition, it was found that the subscales of the CompACT scale (Openness to experience, behavioral awareness, valued action) were negatively related to DASS-21 and AAQ-II. The negative relationships between CompACT and AAQ-II, DASS-21 showed that the divergent validity condition was met. 4.1.Contributions of CompACT Disaster workers may experience negative emotions for many reasons, such as the vital importance of their work and the time constraints. These negative emotions may cause psychological disorders after a certain period. Psychological flexibility is a practical feature in coping with negative emotions and can be learned. Measuring the psychological flexibility values of disaster workers and providing employees with low levels with a specific training process will be beneficial for their psychological health and for them to do their jobs more effectively and efficiently. CompACT scale, whose validity was tested in this study, was found to be a tool that can accurately measure psychological flexibility. Disaster workers receive regular training related to their jobs to do their jobs better. The compACT scale can be used as an objective tool to monitor the development of disaster workers in terms of psychological flexibility in the training provided. The effectiveness of training programs can be evaluated through measurements to be carried out before and after the training. The results obtained can guide the revision of training content and training processes. Psychosocial support activities are carried out for disaster response workers after the disaster response. These activities aim to identify psychological and social changes in employees and help them maintain their work efficiency. After the disaster response, the CompACT scale can be applied to employees, and special training and therapy programs can be organized for those with low scores. Instead of applying psychosocial support activities to all employees, focus can be placed on those who need it, and the waste of resources can be prevented. The organizational structure and leadership in disaster response teams can determine the outcome of the disaster response. The psychological flexibility levels of team leaders can also change the dynamics of the team and the effectiveness of disaster response management. The CompACT scale can help determine leaders' evaluation of leadership development programs' effectiveness and identify new strategies to increase resilience. Considering that psychological flexibility is an element of success in tasks under high stress and pressure, it can be stated that CompACT can be an objective criterion tool in recruiting and promoting disaster response team members. By improving team members' and leaders' psychological flexibility levels, more effective communication, coordination, and decision-making processes can be established in crises, and harmony and solidarity within the team can be strengthened. By using this scale, disaster response teams can be enabled to use their emotional and cognitive resources better. At the same time, in-group solidarity and moral support can be strengthened. 5.Limitations In adapting the CompACT scale to Turkey, participants were selected from among emergency aid and disaster management students using the convenience sampling method, which is one of the non-random sampling methods. Therefore, the research results cannot be generalized. Beside this, testing the scale test on emergency aid and disaster workers will provide more accurate results. This study is cross-sectional, so it needs to be supported by longitudinal studies. Re-measuring the same sample at different times helps address the reasons for changes over time. In the study, AAQ-II and DASS-21 scales were used to determine the convergent and divergent validity of the adapted scale. Due to the increase in the number of questions, the voluntary nature of participation, and the fact that the research was conducted without using funds, only two scales were used compared to other scales. Comparison with many similar and different scales will help obtain more accurate validity results. Conclusion When looking at the temporal development of psychotherapeutic interventions, views that manage and support change from diagnosis and treatment of symptoms have come to the fore. One of these views is psychological flexibility. Psychological flexibility refers to people's ability to adapt to complex and stressful situations and manage their emotional and cognitive reactions. Individuals with high psychological flexibility can adapt to changing demands, restructure their mental resources, change their perspectives on life, and balance competing desires, needs, and living spaces. Disaster response workers are individuals who work in difficult and stressful environments and are highly vulnerable to psychological disorders. In addition, since their work is about saving human lives, they need emotional and mental clarity. Otherwise, the consequences will be fatal if they have difficulty focusing on their work. Considering the benefits of high psychological flexibility, it would be beneficial to determine and develop the psychological flexibility levels of disaster workers. Although different scales measure psychological flexibility, the CompACT scale is at the forefront of quality ratings. The CompACT scale is still in its infancy and needs further evaluation and cross-cultural validation. In this study, the Turkish adaptation of the CompACT scale was tested. As a result of the analysis, it was determined that the Turkish adaptation of CompACT is a practical, valid, and consistent tool suitable for the Turkish cultural context. Abbreviations AAQ II: Acceptance and Action Questionnaire AGFI: Adjusted Goodness of Fit Index AMOS: Analysis of Moment Structures AVE: Average Variance Extracted BA: Behavioral awareness CFA: Confirmatory Factor Analysis CFI: Comparative Fit Index CMIN/df: Chi-Square Statistic CompACT: Comprehensive Assessment of Acceptance and Commitment Therapy Processes CR: Composite Reliability DASS-21: Depression Anxiety and Stress Scales EFA: Explanatory Factor Analysis GFI: Goodness of Fit Index h 2 : Communality HTMT: Heterotrait-monotrait IFI: Incremental Fit Index I-T: Corrected item-total correlations KMO: Kaiser-Meyer-Olkin M: Mean, MSV: Maximum Shared Variance NFI: Normed Fit Index OE: Openness to experience RMSEA: Root Mean Square Error of Approximation SD: Standart Deviation SE: Standart error SPSS: Statistical Package for the Social Sciences TL: Turkiye Liras TLI: Tucker Lewis Index VA: Valued action α: Cronbach alpha ω: McDonald's omega References Hayes SC, Hofmann SG. 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J Contextual Behav Sci. 2014;3:155-63. doi:10.1016/j.jcbs.2014.06.003 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 May, 2026 Reviews received at journal 16 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 25 Nov, 2025 Editor invited by journal 10 Oct, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 30 Sep, 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-7535464","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594855863,"identity":"59c0ced5-5eb7-4907-9d91-d9ab436acd9c","order_by":0,"name":"Öznur Çınar","email":"","orcid":"","institution":"Bayburt University","correspondingAuthor":false,"prefix":"","firstName":"Öznur","middleName":"","lastName":"Çınar","suffix":""},{"id":594855864,"identity":"f8762bed-48a3-41ee-b36c-faf00ee71931","order_by":1,"name":"Vildan Oral","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACZuYGCSir8QFCmA2fFkaYFsZmAxQtPDj1ILS0SRClxeA4Y+ONnzsY5MzbD7ZVfNxTl7h9RnYCw4eywwz20gewaznM2GzZe4bBWOZMYtvNGc/YEufcyN3AOOPcYQYevgSsWiSbge7hbWNInMGQ2Hab5wBP4gyJ3A3MvG1ALThcBtIi+Rekhf9hW/GfAxIQLX/xaOFnZmyTBtsikdjGzHDAAKKFEb+WZmvZNgljCYmHzZI9BxKMZ/C83XCw51w6D88Z7FrY+A8fvPm2zUZOgj/54IcfB+pkZ7Dnbnzwo8xajr0HuxYokEDlHmDAF5OjYBSMglEwCggCACScVpzTvx07AAAAAElFTkSuQmCC","orcid":"","institution":"Erzurum Technical University","correspondingAuthor":true,"prefix":"","firstName":"Vildan","middleName":"","lastName":"Oral","suffix":""},{"id":594855865,"identity":"6ceaf26e-1ef6-426b-b24a-9c821960f176","order_by":2,"name":"Melikşah Turan","email":"","orcid":"","institution":"Erzurum Technical University","correspondingAuthor":false,"prefix":"","firstName":"Melikşah","middleName":"","lastName":"Turan","suffix":""},{"id":594855866,"identity":"45d1d310-98c2-4a84-9c7a-0bed73ab1b25","order_by":3,"name":"Ekrem Cengiz","email":"","orcid":"","institution":"Gümüşhane University","correspondingAuthor":false,"prefix":"","firstName":"Ekrem","middleName":"","lastName":"Cengiz","suffix":""}],"badges":[],"createdAt":"2025-09-04 11:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7535464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7535464/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103260751,"identity":"a6c4e13a-229d-4ac9-9799-55309f4ddb23","added_by":"auto","created_at":"2026-02-23 17:59:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":661524,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7535464/v1/dacf244f2ecb494be1e801d0.png"},{"id":103260753,"identity":"16cd2f28-7ae3-4e54-944d-5e73d8b470ea","added_by":"auto","created_at":"2026-02-23 17:59:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1856566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7535464/v1/8b5c6eab-acb9-450e-b6b6-66bbff3ed848.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preliminary validation of a Turkish version of the Comprehensive Assessment of Acceptance and Commitment Therapy Processes in students of the emergency aid and disaster management","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychological flexibility was developed within the contextual behavioral science framework, a research paradigm that forms the basis of the recent development of process-based cognitive behavioral therapy [1]. Psychological flexibility is the rigid dominance of a person\u0026apos;s values and psychological reactions among situational possibilities in guiding their actions [2]. It is defined as a person consciously establishing contact with the present time entirely and accurately, without using defense mechanisms, considering the situational context, and being consistent in their behaviors while serving their chosen values [3]. An alternative definition posits that psychological flexibility is how an individual employs specific strategies to recognize and manage diverse situational demands. These strategies include the following: providing an appropriate mentality change in case of situational differences, creating a balance between valued life areas, creating behaviors compatible with adopted values, and adhering to them [4]. In a broader sense, Williams et al. (2012) [5] defined psychological flexibility as the ability to manage a changing environment and interaction network through dynamic processes. These processes include being aware of the moment, changing perspectives, adapting to situational demands, balancing conflicting needs, and changing behaviors necessary for valuable goals and making them sustainable.\u003c/p\u003e\n\u003cp\u003ePsychological flexibility significantly influences individuals\u0026apos; emotional, mental, and behavioral outcomes by means of certain concepts. Psychological flexibility has been demonstrated to facilitate effective coping with stressful situations [6]. Flexible individuals can accept stressful thoughts and feelings, disengage from them without becoming overwhelmed, and take actions that are consistent with their values [7]. Research has demonstrated that psychological flexibility functions as a protective factor against adverse life occurrences [8, 9]. Wersebe et al. (2018) [10] have demonstrated that psychological flexibility enhances an individual\u0026apos;s capacity to cope with stress, thereby positively influencing their psychological well-being. Psychological flexibility fosters prolonged psychological well-being by empowering individuals to act according to their values in challenging circumstances [11]. Psychological flexibility is closely related to general psychological health. Individuals who demonstrate psychological flexibility exhibit reduced levels of depression, anxiety, and psychological distress [12, 13, 14, 15]. Furthermore, Wang et al. (2023) [9] have demonstrated that psychological flexibility facilitates more efficacious management of mental health disorders, such as depression and anxiety. The mechanisms by which psychological flexibility exerts these effects include increased tolerance for emotional experiences, as indicated by Wang et al. (2023) [9]. A growing body of research has identified psychological flexibility as a contributing factor to the positive management of social anxiety [16] and borderline personality disorder [17]. Furthermore, psychological flexibility has been demonstrated to substantially influence individuals\u0026apos; overall well-being [18, 19]. Guerrini et al. (2021) [20] have demonstrated that psychological flexibility enhances the psychological well-being of individuals. Coping with challenges more effectively has increased satisfaction and healthier emotional well-being [21, 22]. Psychological flexibility can be conceptualized as an integral component of one\u0026apos;s repertoire of psychological resources [21, 22]. Psychological flexibility makes individuals more resilient to traumatic or stressful situations [23, 24, 25]. Kashdan and Ciarrochi (2013) [26] stated that psychological flexibility increases the ability of individuals to adopt healthy lifestyles, which leads to better physical health conditions. Individuals with high psychological flexibility are more flexible in dealing with emotional states [27]. Flexible individuals can accept and express their negative emotions in healthy ways rather than suppressing or avoiding them [28]. Psychological flexibility allows individuals to identify and act on their values. Flexible individuals can remain committed to their values and live meaningful lives despite difficulties [29, 30]. Psychological flexibility helps individuals to have a broader repertoire of behaviors and to adapt more quickly to changing conditions [4, 31]. Psychological flexibility is strongly related to self-compassion [32]. Flexible people can be kinder and more understanding, accept their mistakes and shortcomings, and support themselves without judgment [33]. More psychologically flexible people show higher levels of positive and lower levels of negative affect, resulting in higher quality social relationships [34, 35]. Psychological flexibility also increases life satisfaction [36].\u003c/p\u003e\n\u003cp\u003eIndividuals engaged in disaster relief work are required to manage considerable stress, exposure to traumatic events, and perpetual uncertainty, a consequence of the nature of their profession. This necessitates that they possess elevated levels of psychological flexibility. Psychological flexibility is a salient characteristic that can facilitate disaster workers\u0026apos; ability to cope with challenging experiences, enhance their adaptability to fluctuating and demanding living conditions, safeguard their mental well-being, and maintain functionality. Disaster response teams and organizations must provide training and support to their employees to cultivate psychological flexibility skills. To this end, it is imperative to assess the psychological flexibility of disaster response workers, evaluate the results, and implement appropriate precautions based on the findings. A multitude of psychological flexibility scales have been documented in the extant literature. While these scales have inherent limitations for disaster workers, the differences between them and the contributions of each scale provide a comprehensive perspective on measuring this concept. While each scale addresses psychological flexibility from a distinct perspective, they exhibit deficiencies for groups operating under elevated stress, such as disaster workers. Consequently, there is a necessity for developing a more comprehensive and situation-specific scale, particularly for disaster workers who are routinely exposed to traumatic events. The development of such a scale could facilitate the design of effective professional support strategies by accurately assessing disaster workers\u0026apos; workload and capacity to cope with traumatic events. This scale could also facilitate a more profound understanding of the challenges encountered in the work environment, thereby enabling the development of more effective support strategies.\u003c/p\u003e\n\u003cp\u003eIn their study on psychological flexibility, Cherry et al. (2021) [37] identified 27 scales and subjected them to a quality rating. As a result of the quality rating, they stated that the CompACT scale was in the top three. CompACT is a 23-item, 3-factor (Openness to experience, valued action, and behavioral awareness) scale to assess ACT processes. The willingness of a human to experience internal events without attempting to control or avoid them is known as openness to experience, and it symbolizes the processes of \u0026quot;acceptance\u0026quot; and \u0026quot;cognitive defusion.\u0026quot; Behavior awareness, which embodies the concepts of \u0026quot;self-as-context\u0026quot; and \u0026quot;contact with the present moment,\u0026quot; is the ability to pay attention to one\u0026apos;s behaviors. Valued action, which stands for the procedures of \u0026quot;value clarification\u0026quot; and \u0026quot;committed action,\u0026quot; describes one\u0026apos;s ability to perform valued acts. CompACT evaluates psychological flexibility in several ways [38]. \u003c/p\u003e\n\u003cp\u003eThis study aims to make a cross-cultural adaptation of the English version of CompACT in the context of Turkey and to evaluate its psychometric properties by adapting it to emergency aid and disaster management students. In this way, it is to present a scale that can accurately measure the psychological flexibility of disaster workers.\u003c/p\u003e\n\u003cp\u003eThe unique contribution of this study is that it pioneers the adaptation of a scale appropriate for the psychological flexibility assessment needs of disaster workers. Developing a scale sensitive to disaster workers\u0026apos; unique psychological needs will support the creation of effective interventions to protect their job performance and psychological well-being. In this way, the psychological flexibility levels of disaster workers can be assessed more accurately, and the necessary psychosocial support can be provided to increase their functionality. In addition, psychological flexibility may depend on specific cultural contexts. Therefore, examining the psychometric properties of translated psychological flexibility measures in different contexts is vital. In addition, this study will provide a better understanding of CompACT items from a Turkish cultural perspective, the discovery of potential new findings, and researchers\u0026apos; better understanding of CompACT.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study used a cross-sectional data collection method to test the results of the psychometric properties of the Turkish version of The Comprehensive Assessment of Acceptance and Commitment Therapy Process (CompACT) scale on emergency aid and disaster management students.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\u003cstrong\u003ePopulation and sample of the study\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe universe of this study is the students studying in the emergency aid and disaster management undergraduate departments at universities in Turkey. In total, 13,848 students are enrolled in this department as of 2025. It aimed to apply the survey to 350 samples determined by the convenience sampling method from this universe. For this purpose, students in three state universities\u0026apos; emergency aid and disaster management departments were reached, and the survey was applied. A total of 381 students were reached, and the research was conducted using the electronic and face-to-face survey application methods. Of the collected surveys, 79 were not included in the analysis because they contained incomplete data and appeared to be filled out randomly. Therefore, a total of 302 surveys were included in the analysis. Although there are different opinions on the adequacy of the sample size to be used in multivariate analyses, there is generally an opinion that more than 5 times the number of items is sufficient [39, 40]. The number of items used in this study was 23, and considering the total number of 302 subjects, a subject number slightly more than 13 times the number of items was deemed sufficient. \u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. Ethical approval for this research was obtained from the XXX University Ethics Committee (Code: BS9BNDY28C, Issue: E45796069-204.01.07-266356, Date: 14.03.2025). Informed consent was obtained from all participants prior to their participation in the study. Participation was entirely voluntary, and participants were informed about the study\u0026rsquo;s purpose, procedures, potential risks, and their right to withdraw at any time without penalty. Written informed consent was collected from each participant on (15 March 2025-30 April 2025), at (classroom setting or online platform). No identifying information of participants was collected, and data were used solely for research purposes. All participants were adults, and no minors or legally dependent individuals were included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study used three different scales. The scales used are listed below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive Assessment of Acceptance and Commitment Therapy (CompACT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CompACT scale, developed by Francis et al. (2016) [38], is a 23-item, 3-factor scale (Openness to experience, behavioral awareness, valued action) designed to assess ACT processes. The original scale developed by Francis et al. (2016) [38] was used in this study. In the study conducted by Francis et al. (2016) [38], the total Internal reliability value (Cronbach alpha) of the scale was found to be 0.91. In the present study, items on the CompACT are scored from 1 (Strongly Disagree) to 7 (Strongly Agree) with total scores ranging from 0 to 161, where higher scores indicate greater levels of psychological flexibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcceptance and Action Questionnaire-II (AAQ-II)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBond et al. (2011) [2] developed AAQ-II, the most widely used scale to measure psychological flexibility. AAQ-II is a single-dimensional 7-item scale that measures only the experiential avoidance sub-dimension of psychological flexibility [31]. It has been observed that AAQ-II has a higher correlation with psychological distress and negative emotions than measuring psychological flexibility [41]. The original scale developed by Bond et al. (2011) [2] was used in the present study. In the original study, the scale\u0026apos;s average Internal reliability value (Cronbach alpha) was found to be 0.84. In the present study, the internal consistency value of AAQ-II was 0.979. In the present study, a Likert-type scale that ran from 1 (never true) to 7 (always true) was used, with higher scores indicating greater levels of psychological inflexibility. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepression Anxiety Stress Scale-21 (DASS-21)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used the DASS-21 scale developed by Henry and Crawford (2005) [42]. This scale is a distress measure assessing three separate constructs (Depression, anxiety, and stress). In the study conducted by Henry and Crawford (2005) [42], the total internal reliability value (Cronbach alpha) of the scale was found to be 0.93. In the present study, the internal consistency value of DASS-21 was 0.971. The scale contains 21 items, 7 of which measure stress, 7 of which measure anxiety, and 7 of which measure depression. In the present study, each item is scored from 1 (did not apply to me at all over the past week) to 4 (applied to me very much or most of the time over the past week). Higher scores indicate greater levels of distress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS v.21 (Statistical Package for the Social Sciences) and AMOS v.22 (Analysis of Moment Structures) programs were used to analyze this research. Frequency and percentage were used for the demographic characteristics of the participants, skewness and kurtosis coefficients for the distribution of the data, explanatory factor analysis (EFA) was used to determine the factor structure, and confirmatory factor analysis (CFA) was used to verify the factor structure. In addition, test-retest (Pearson correlation), composite reliability (CR), average variance extracted (AVE), maximum shared variance (MSV), and heterotrait-monotrait (HTMT) ratio, Cronbach alpha (\u0026alpha;) and McDonald\u0026apos;s omega (\u0026omega;) were used for validity and reliability. The Pearson correlation coefficient was examined for the divergent and convergent validity of the scale with other scales. The confidence interval in the analyses was determined as 95%. A maximum of 5% missing data per survey was allowed to be included in the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Translation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original scale was independently translated into Turkish by three academics fluent in English (two in the emergency aid and disaster management department, one in the English language and literature department). Then, these three academics came together and combined the three different translations into a single translation in a coordinated manner. The research team evaluated the translation in terms of conceptual equivalence. The same three academics translated the text back into English. The research team members examined the scale translated into English in terms of its conformity with the original. In order to evaluate the understandability and cognitive equivalence of the resulting Turkish draft, a session was held with 15 students studying undergraduate education in emergency aid and disaster management. All participants were asked to express the meaning of each item in their own words and to provide the reasons for their responses. In light of the feedback from the session, the necessary adjustments were made to the scale draft, and the scale was finalized. The translation of the DASS-21 and AAQ-II scales used for reliability testing in the study was also carried out within the aforementioned process. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. Pilot study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore starting the actual field research of the draft scale, it is necessary to conduct a pilot study to prevent possible problems by trying the sample-finding method, survey application methodology, data collection, and entry method in advance [43]. For this reason, a pilot study was conducted on a small sample representing the targeted universe. The convenience sampling method was applied to determine the sample, and a draft survey was applied to 40 emergency aid and disaster management students. The survey was conducted by the researchers in person and face-to-face. Thanks to the pilot study and the fact that this study was conducted in person by the researchers, problems that could arise under field conditions were determined in advance. In the pilot study, the difficulties encountered by the participants, the incomprehensible or misunderstood questions, and the appropriateness of the survey duration were evaluated. Immediate feedback was received from the participants, and errors in the survey draft and application were corrected. The time it took for each participant to complete the survey varied between 5 and 11 minutes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Participant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the demographic characteristics of 302 participants. The majority of the participants were male (50.7%), senior grade (28.5%), unemployed status (90.1%), urban area residence (73.5%), and family income 23,001-76,000 TL (65.6%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eDemographical Characteristics of the Participants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale: 153 (50.7%), Female: 149 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFreshmen: 70 (23.2%), Sophomores: 66 (21.9%), Junior: 80 (26.5%), Senior: 86 (28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudent employment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEmployed: 30 (9.9%), Unemployed: 272 (90.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban: 222 (73.5%), Rural: 80 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-23.000 TL: 71 (23.5%), 23.001-76.000 TL: 198 (65.6%), 76.001 TL and above: 33 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Distribution analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhether the data in the study show normal distribution can be tested using skewness and kurtosis coefficients [44]. According to Joanes \u0026amp; Gill (1998) [45], a value between ‒3 and +3 is sufficient for normal distribution. Outliers were examined in the study\u0026apos;s data set, and no outliers were found. As a result of the skewness (0.382‒2.253) and kurtosis (0.010‒2.024) coefficients, it was found that the data showed normal distribution. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Exploratory factor analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the research, EFA was carried out to see the scale\u0026apos;s factor structure (construct validity), and two tests were performed to determine the suitability of the data. The Kaiser-Meyer-Olkin (KMO) sample adequacy test shows the adequacy of the number of participants and should be over 0.50 [46, 47, 48, 49]. The Bartlett Sphericity test indicates whether the data show diversity, and the p-value should be less than 0.05 [50, 51]. In EFA, factors with eigenvalues greater than one were considered in determining the factor structure. It was deemed appropriate for the total variance rate explained in EFA to be over 50% [52, 53, 54]. In EFA, the direct oblimin method was preferred to the principal component analysis and oblique rotation methods for data extraction. The strategies used for item removal from the EFA analysis are as follows: Items with a factor loading value below 0.5 were removed. Attention was paid to having at least three items for each factor. Items with high cross-loadings (greater than 0.3 in more than one factor) and minimum difference in factor loading (\u0026lt;0.1) were removed from the analysis [55, 56, 57]. Those with a common factor variance value above 0.50 were considered [46]. \u003c/p\u003e\n\u003cp\u003eSome assumptions were tested before proceeding to EFA in the study. As a result of the test, it was seen that there were no outliers in the data set, that the data showed a normal distribution, and that the variables were in a linear relationship with each other. It was also seen that there was no multicollinearity problem. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy value was determined as 0.894, and the Bartlett\u0026apos;s Test of Sphericity value was determined as 7853.035 (df: 253; p: 0.000). When the total variance explained table was examined, it was seen that four factors were formed. The pattern matrix table determined that the OE3 and OE9 items were collected under the 4th factor. In addition, it was seen that the factor loading of the VA1 item was below 0.50. Therefore, these three items were removed from the analysis, and EFA was performed again.\u003c/p\u003e\n\u003cp\u003eAs a result of the second EFA, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy value was determined as 0.908, and the Bartlett\u0026apos;s Test of Sphericity value was determined as 6935.438 (df: 190; p: 0.000). When the total variance explained table was examined, it was seen that there were three factors with eigenvalues greater than 1 explaining 75.848% of the total variance. The first factor explained 36.393% of the variance, the second factor 25.194%, and the third factor 14.260%. Communalities values varied between 0.537-0.959. When the pattern matrix was examined, it was seen that the values of the items under each factor varied between 0.731-0.974. At this stage, the reliability of the scale whose structural validity was tested was also examined. For this, Cronbach alpha (\u0026alpha;) and item-total statistics values were examined. Item-total correlation shows the relationship between the scores obtained from the questionnaire items and the test\u0026apos;s total score. A positive and high item-total correlation value emphasizes that the items exemplify similar characteristics. According to B\u0026uuml;y\u0026uuml;k\u0026ouml;zt\u0026uuml;rk (2007) [58], items with item-total correlations below 0.20 are removed from the analysis. Cronbach alpha (\u0026alpha;) measures internal consistency and is desired to be above 0.70 [59, 60]. The overall \u0026alpha; value of the scale was found to be 0.843, 0.978 for the openness to experience subscale, 0.824 for the behavioral awareness subscale, and 0.937 for the valued action subscale. Item-total values were between 0.572 and 972 (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. CompACT factor loadings, items descriptive statistics and reliability statistics\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"470\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e Factors and factor loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e Descriptive \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e h\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e I-T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e M\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.918\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 2 \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.969\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n 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\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.944\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n 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\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.808\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.746\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.786\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.753\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.781\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.845\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.936\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.906\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.891\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e.875\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvalue\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariance Explained (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Variance (%) \u003c/strong\u003e75.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eOE: Openness to experience, BA: Behavioral awareness, VA: Valued action, M: Mean, SD:Standart Deviation, h\u003csup\u003e2\u003c/sup\u003e: Communality, I-T: Corrected item-total correlations, \u0026alpha;: Subscale Cronbach alpha\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.4. Confirmatory factor analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe study performed CFA analysis after EFA to ensure the scale\u0026apos;s construct validity. CFA is a statistical method used to verify the estimated relationships. The scale\u0026apos;s general goodness of fit indexes are examined in CFA, then the factor loadings (standardized regression coefficients) are evaluated [40]. General fit indexes must comply with the following values. \u0026chi;\u0026sup2;/sd (Chi-Square Statistic)\u0026le;5, GFI (Goodness of Fit Index) 0.90\u0026le;GFI\u0026le;0.95, AGFI (Adjusted Goodness of Fit Index) 0.85\u0026le;AGFI\u0026le;0.90, CFI (Comparative Fit Index) 0.90\u0026le;CFI\u0026le;0.95, NFI (Normed Fit Index) 0.90\u0026le;NFI\u0026le;0.95, IFI (Incremental Fit Index) 0.90\u0026le;IFI\u0026le;0.95, TLI (Tucker Lewis Index) 0.90\u0026le;TLI\u0026le;0.95, RMSEA (Root Mean Square Error of Approximation) 0.05\u0026le;RMSEA\u0026le;0.08 [61, 62, 63]. Standardized factor loading values must exceed 0.50 [61].\u003c/p\u003e\n \u003cp\u003eCFA analysis was performed with three possible model tests. All observed variables were evaluated under a single factor in the first model. Before the second model was developed, EFA was performed on the sample, and the number of factors was limited to 2 in this analysis. As a result of this analysis, it was determined that the items of the openness to experience subscale were collected under the first factor, and the items of the behavioral awareness and valued action subscales were collected under the second factor. Therefore, a two-factor CFA was performed with these results in the second CFA model. In the third model, a three-factor CFA analysis was performed, using the results of the three-factor EFA analysis that had been performed before. It was determined that the three-factor model showed the best goodness of fit values. After the modifications suggested for the three-factor model were implemented, the goodness of fit values obtained was higher (Final model) (Table 3). In the final model, GFI and AGFI values did not show good fit values and were below 0.90. However, values above 0.800 can be considered as \u0026apos;acceptable\u0026apos; goodness of fit values [64, 65]. When the standardized factor loadings of the final model were examined, it was determined that they varied between 0.573 and 0.991 (Table 4). The AMOS output of the results of the final model is seen in Figure 1.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. \u003c/strong\u003eCompACT confirmatory factor analysis model fit indices\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eRegression weights of CFA results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Std\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.E.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.R.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOE 10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOpenness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA 1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBehavioral Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBehavioral Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBehavioral Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBehavioral Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBehavioral Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValued Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValued Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValued Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValued Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValued Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValued Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVA 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValued Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;.0001, S.E.: Standart error, C.R.: Critical ratio, p: Significance value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Other validity and reliability analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the study, the heterotrait-monotrait (HTMT) ratio of correlations was used for convergent and divergent validity in the context of the factors of the scale [66]. Convergent validity shows the relationships of the variables belonging to a factor with each other and with the factor they belong to. In order to ensure convergent validity, the CR values must be greater than 0.70, the AVE values must be greater than 0.50, and the CR values must be greater than the AVE values [67, 68]. Divergent validity shows that the relationship of the variables with the factor they belong to is higher than their relationship with other factors. If the correlation values of a factor with other factors are lower than the square root of the AVE of this factor [67, 69], and if the MSV values is smaller than the AVE values [70], then divergent validity is achieved [71]. In addition, for divergent validity, HTMT correlations must be less than 1.00 [72, 73].\u003c/p\u003e\n\u003cp\u003eCronbach alpha, McDonald\u0026apos;s omega, and CR values were examined for reliability. These values must be greater than 0.70 [59, 60]. The reliability and validity results of the structural measurements of the CompACT scale are shown in Table 5. In order to evaluate the internal consistency reliability in the study, Cronbach alpha and McDonald\u0026apos;s omega values were examined separately for the entire scale and its three subscales. All alpha and omega values were determined to be above 0.70. It was also observed that CR values were above 0.80.\u003c/p\u003e\n\u003cp\u003eWhen Table 5 is examined, it is seen that CR values are greater than 0.70, AVE values are greater than 0.50, and CR values are greater than the relevant AVE values. Therefore, convergent validity is provided. In Table 5, it is also determined that the correlation values of each factor with other factors are lower than the square root value of the AVE of the relevant factor (Values in bold on the diagonal), and its MSV is smaller than its AVE. In addition, it was observed that the HTMT correlations of the factors are less than 1.00. Therefore, divergent validity is provided.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eTest-retest reliability analysis shows the degree of consistency of the survey measurement results applied to the study participants at different periods [74]. In other words, it measures the consistency of the research results in the context of time. In this study, the same survey was applied to 30 participants randomly selected from 302 participants 14 days after the application of the first survey. Pearson correlation coefficient was used in the test-retest analysis. It was found that r=0.746 (p\u0026lt;0.01) for the openness to experience subscale, r=0.723 (p\u0026lt;0.01) for the behavioral awareness subscale, and r=0.781 (p\u0026lt;0.01) for the valued action subscale. It was found that r=0.748 (p\u0026lt;0.01) for global scale. Therefore, the test-retest reliability of the scale was ensured, and strong temporal stability was observed.\u003c/p\u003e\n\u003cp\u003eIn order to determine the divergent validity of the CompACT scale, which investigates the relationship between different scales, the Pearson (r) test was used. The relationship between CompACT and two psychometric scales (AAQ-II and DASS-21) was investigated. It was found that the global CompACT was negatively correlated with AAQ-II (r=‒0.365, p\u0026lt;0.01) and negatively correlated with DASS-21 (r=‒0.403, p\u0026lt;0.01). In addition, it was found that the subscales of the CompACT scale (Openness to experience, behavioral awareness, valued action) were negatively correlated with DASS-21 and AAQ-II (Table 6). While AAQ-II measures experiential avoidance and psychological inflexibility, DASS-21 measures negative emotions (Depression, anxiety, and stress). The negative correlations between CompACT and AAQ-II, DASS-21 shows that the divergent validity condition is met.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 .\u0026nbsp;\u003c/strong\u003eCorrelations of the CompACT with other scales\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 Global scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 Openness to Experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.718**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 Behavioral Awareness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.331**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 Valued Action\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.448**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.138*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-.148**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 AAQ-II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.365**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.201**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.258**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 DASS-21\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.403**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.123*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-.239**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.322**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e.244**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7 DASS-21 Depression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.388**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-.234**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.322**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e.223**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.982**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e \u003cstrong\u003eDASS-21 Anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.433**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.154**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-.248**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.325**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e.267**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.975**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e.934**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e \u003cstrong\u003eDASS-21 Stress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.359**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-.219**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-.298**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e.223**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.978**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e.948**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.922**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Global scale is the CompACT total scores; AAQ-II is the Acceptance and Action Questionnaire-II; DASS-21 is the Depression Anxiety Stress Scale-21, **. Correlation is significant at the 0.01 level (2-tailed), *. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe concept of psychological flexibility, one of the concepts that can increase disaster workers\u0026apos; ability to make urgent decisions about human life under high stress to adapt to variable and unpredictable disaster environments and focus on their duties, has attracted attention in recent years. Psychological flexibility is defined as the ability of an individual to restructure himself/herself in coping with stressful or challenging situations, adapt to events, and react flexibly [4]. This concept means that an individual can achieve his/her goals despite the negativities in his/her life by choosing appropriate thought and behavior patterns for the current situation [31]. Psychological flexibility enables a person to cope with challenging situations more effectively through emotional regulation, cognitive reframing, and awareness [75]. Therefore, psychological flexibility includes managing stressful events and individual development by learning from such events [4].\u003c/p\u003e\n\u003cp\u003eAlthough there are different scales in the literature that attempt to measure psychological flexibility, one of the most notable rating scores was The Comprehensive Assessment of Acceptance and Commitment Therapy Process (CompACT) scale developed by Francis et al. (2016) [37].\u003c/p\u003e\n\u003cp\u003eThis study aims to make a cross-cultural adaptation of the English version of The Comprehensive Assessment of Acceptance and Commitment Therapy Process scale into Turkish and to evaluate its psychometric properties by adapting it to emergency aid and disaster management students. In this way, it is to present a scale that can accurately measure the psychological flexibility of disaster workers.\u003c/p\u003e\n\u003cp\u003eIn the study, EFA was first performed to determine the construct validity, and then CFA was performed to verify the construct. As a result of EFA, four factors with eigenvalues above one were identified. OE3 and OE9 items were observed to be grouped under the 4th factor, and the factor loading of the VA1 item was below 0.50. These items were removed, and EFA was performed again, resulting in a 3-factor structure. These three factors explained 75.848% of the total variance. The obtained factors (Openness to experience, behavioral awareness, and valued action) and the items under these factors were obtained like the original CompACT scale created by Francis et al. (2016) [38]. The global Cronbach alpha value of the scale performed at this stage was determined as 0.843.\u003c/p\u003e\n\u003cp\u003eIn the second stage of construct validity, CFA analysis was performed to verify the 3-factor structure obtained from EFA. Single-factor, two-factor, and three-factor models were completed, and it was determined that the three-factor model had the highest goodness of fit values. Some general goodness of fit values were good, and some were acceptable. All of the standardized regression coefficients were found to be above 0.600. Due to some relationships between the error terms in the three-factor CFA model, modifications were made, and the final model was obtained. The assumed structure was confirmed as a result of the CFA.\u003c/p\u003e\n\u003cp\u003eIn the study, the heterotrait-monotrait (HTMT) ratio of correlations was used for convergent and divergent validity in the context of the factors of the scale [66]. Cronbach alpha (\u0026alpha;), McDonald\u0026apos;s omega (\u0026omega;), and composite reliability (CR) values were examined for reliability. It was determined that the CR values were greater than 0.70, the AVE values were greater than 0.50, and the CR values were greater than the relevant AVE values. Therefore, convergent validity was achieved between the factors of the scale. For the inter-factor divergent validity, it was determined that the correlation values of each factor with other factors were lower than the square root value of the AVE of the relevant factor, and its MSV was smaller than its AVE. However, the HTMT correlations of the factors were observed to be less than 1.00. Therefore, divergent validity was achieved.\u003c/p\u003e\n\u003cp\u003eIn order to evaluate the internal consistency reliability in the study, Cronbach alpha and McDonald\u0026apos;s omega values were examined separately for the entire scale and its three subscales. It was observed that all alpha, omega, and CR values were above 0.70. \u003c/p\u003e\n\u003cp\u003eIn this study, the same survey was applied to 30 randomly selected participants out of 302 participants 14 days after the first survey was applied. Both global scale and subscales Pearson correlation coefficients were found to be greater than 0.70. Therefore, the test-retest reliability of the scale was ensured, and strong temporal stability was observed.\u003c/p\u003e\n\u003cp\u003eIn order to determine the divergent validity that investigates the relationship between the CompACT scale and different scales, the relationships between CompACT and two psychometric scales (AAQ-II and DASS-21) were investigated. It was found that there were negative relationships between global CompACT and AAQ-II, DASS-21. In addition, it was found that the subscales of the CompACT scale (Openness to experience, behavioral awareness, valued action) were negatively related to DASS-21 and AAQ-II. The negative relationships between CompACT and AAQ-II, DASS-21 showed that the divergent validity condition was met.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.Contributions of CompACT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDisaster workers may experience negative emotions for many reasons, such as the vital importance of their work and the time constraints. These negative emotions may cause psychological disorders after a certain period. Psychological flexibility is a practical feature in coping with negative emotions and can be learned. Measuring the psychological flexibility values of disaster workers and providing employees with low levels with a specific training process will be beneficial for their psychological health and for them to do their jobs more effectively and efficiently. CompACT scale, whose validity was tested in this study, was found to be a tool that can accurately measure psychological flexibility.\u003c/p\u003e\n\u003cp\u003eDisaster workers receive regular training related to their jobs to do their jobs better. The compACT scale can be used as an objective tool to monitor the development of disaster workers in terms of psychological flexibility in the training provided. The effectiveness of training programs can be evaluated through measurements to be carried out before and after the training. The results obtained can guide the revision of training content and training processes.\u003c/p\u003e\n\u003cp\u003ePsychosocial support activities are carried out for disaster response workers after the disaster response. These activities aim to identify psychological and social changes in employees and help them maintain their work efficiency. After the disaster response, the CompACT scale can be applied to employees, and special training and therapy programs can be organized for those with low scores. Instead of applying psychosocial support activities to all employees, focus can be placed on those who need it, and the waste of resources can be prevented.\u003c/p\u003e\n\u003cp\u003eThe organizational structure and leadership in disaster response teams can determine the outcome of the disaster response. The psychological flexibility levels of team leaders can also change the dynamics of the team and the effectiveness of disaster response management. The CompACT scale can help determine leaders\u0026apos; evaluation of leadership development programs\u0026apos; effectiveness and identify new strategies to increase resilience. Considering that psychological flexibility is an element of success in tasks under high stress and pressure, it can be stated that CompACT can be an objective criterion tool in recruiting and promoting disaster response team members. By improving team members\u0026apos; and leaders\u0026apos; psychological flexibility levels, more effective communication, coordination, and decision-making processes can be established in crises, and harmony and solidarity within the team can be strengthened. By using this scale, disaster response teams can be enabled to use their emotional and cognitive resources better. At the same time, in-group solidarity and moral support can be strengthened.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.Limitations \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn adapting the CompACT scale to Turkey, participants were selected from among emergency aid and disaster management students using the convenience sampling method, which is one of the non-random sampling methods. Therefore, the research results cannot be generalized. Beside this, testing the scale test on emergency aid and disaster workers will provide more accurate results. This study is cross-sectional, so it needs to be supported by longitudinal studies. Re-measuring the same sample at different times helps address the reasons for changes over time. In the study, AAQ-II and DASS-21 scales were used to determine the convergent and divergent validity of the adapted scale. Due to the increase in the number of questions, the voluntary nature of participation, and the fact that the research was conducted without using funds, only two scales were used compared to other scales. Comparison with many similar and different scales will help obtain more accurate validity results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhen looking at the temporal development of psychotherapeutic interventions, views that manage and support change from diagnosis and treatment of symptoms have come to the fore. One of these views is psychological flexibility. Psychological flexibility refers to people\u0026apos;s ability to adapt to complex and stressful situations and manage their emotional and cognitive reactions. Individuals with high psychological flexibility can adapt to changing demands, restructure their mental resources, change their perspectives on life, and balance competing desires, needs, and living spaces. Disaster response workers are individuals who work in difficult and stressful environments and are highly vulnerable to psychological disorders. In addition, since their work is about saving human lives, they need emotional and mental clarity. Otherwise, the consequences will be fatal if they have difficulty focusing on their work. Considering the benefits of high psychological flexibility, it would be beneficial to determine and develop the psychological flexibility levels of disaster workers. Although different scales measure psychological flexibility, the CompACT scale is at the forefront of quality ratings. The CompACT scale is still in its infancy and needs further evaluation and cross-cultural validation. In this study, the Turkish adaptation of the CompACT scale was tested. As a result of the analysis, it was determined that the Turkish adaptation of CompACT is a practical, valid, and consistent tool suitable for the Turkish cultural context.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eAAQ II:\u0026nbsp;Acceptance and Action Questionnaire\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAGFI: Adjusted Goodness of Fit Index\u003c/p\u003e\n\u003cp\u003eAMOS:\u0026nbsp;Analysis of Moment Structures\u003c/p\u003e\n\u003cp\u003eAVE: Average Variance Extracted\u003c/p\u003e\n\u003cp\u003eBA: Behavioral awareness\u003c/p\u003e\n\u003cp\u003eCFA: Confirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003eCFI: Comparative Fit Index\u003c/p\u003e\n\u003cp\u003eCMIN/df: Chi-Square Statistic\u003c/p\u003e\n\u003cp\u003eCompACT:\u0026nbsp;Comprehensive Assessment of Acceptance and Commitment Therapy Processes\u003c/p\u003e\n\u003cp\u003eCR: Composite Reliability\u003c/p\u003e\n\u003cp\u003eDASS-21:\u0026nbsp;Depression Anxiety and Stress Scales\u003c/p\u003e\n\u003cp\u003eEFA: Explanatory Factor Analysis\u003c/p\u003e\n\u003cp\u003eGFI: Goodness of Fit Index\u003c/p\u003e\n\u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e: Communality\u003c/p\u003e\n\u003cp\u003eHTMT: Heterotrait-monotrait\u003c/p\u003e\n\u003cp\u003eIFI: Incremental Fit Index\u003c/p\u003e\n\u003cp\u003eI-T: Corrected item-total correlations\u003c/p\u003e\n\u003cp\u003eKMO:\u0026nbsp;Kaiser-Meyer-Olkin\u003c/p\u003e\n\u003cp\u003eM: Mean,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMSV: Maximum Shared Variance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNFI: Normed Fit Index\u003c/p\u003e\n\u003cp\u003eOE: Openness to experience\u003c/p\u003e\n\u003cp\u003eRMSEA: Root Mean Square Error of Approximation\u003c/p\u003e\n\u003cp\u003eSD:\u0026nbsp;Standart Deviation\u003c/p\u003e\n\u003cp\u003eSE: Standart error\u003c/p\u003e\n\u003cp\u003eSPSS:\u0026nbsp;Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eTL: Turkiye Liras\u003c/p\u003e\n\u003cp\u003eTLI: Tucker Lewis Index\u003c/p\u003e\n\u003cp\u003eVA: Valued action\u003c/p\u003e\n\u003cp\u003e\u0026alpha;:\u0026nbsp;Cronbach alpha\u003c/p\u003e\n\u003cp\u003e\u0026omega;:\u0026nbsp;McDonald\u0026apos;s omega\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHayes SC, Hofmann SG. 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Sir Syed J Educ Soc Res. 2020;3(3):105-16. doi:10.36902/sjesr-vol3-iss3-2020(105-116)\u003c/li\u003e\n\u003cli\u003eHenseler J, Ringle CM, Sarstedt M. New criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43(1):115-35. doi:10.1007/s11747-014-0403-8\u003c/li\u003e\n\u003cli\u003eKline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford Publications; 2015.\u003c/li\u003e\n\u003cli\u003eNguyen R, Brooks M, Bruno R, Peacock A. Behavioral measures of state impulsivity and their psychometric properties: A systematic review. Pers Individ Dif. 2018;135:67-79. doi:10.1016/j.paid.2018.06.040\u003c/li\u003e\n\u003cli\u003eLevin ME, Luoma JB, Lillis J, Hayes SC, Vilardaga R. Examining psychological inflexibility as a transdiagnostic process across psychological disorders. J Contextual Behav Sci. 2014;3:155-63. doi:10.1016/j.jcbs.2014.06.003\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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Comprehensive Assessment of Acceptance and Commitment Therapy Processes, psychological flexibility, reliability, validity, Turkey","lastPublishedDoi":"10.21203/rs.3.rs-7535464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7535464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e This study aimed to perform an across-cultural adaptation of the English version of The Comprehensive Assessment of Acceptance and Commitment Therapy Processes (CompACT) into Turkish and to evaluate its psychometric properties in emergency aid and disaster management students.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study was conducted in two phases. Phase 1 involved the translation and cross-cultural adaptation of the CompACT. In phase 2, a study was conducted to assess the validity and reliability of the CompACT. It was conducted among 302 emergency aid and disaster management students.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The exploratory factor analysis showed that the CompACT consisted of 3 factors explaining 75.848% of the total variance, comprising 20 items. Communality values varied between 0.537-0.959, and values of the items under each factor varied between 0.731-0.974. CFA analysis was performed with three possible model tests. It was determined that the three-factor model showed the best goodness of fit values. The general fit indices of the model were found to be appropriate, as indicated by the following values: CMIN/df: 2.590; GFI: 0.882; AGFI: 0.848; NFI: 0.941; CFI: 0.963; IFI: 0.9963; TLI: 0.956; RMSEA: 0.073. The heterotrait-monotrait ratio of correlations was used for convergent and divergent validity in the context of the factors of the scale, and the scale met the convergent and divergent validity conditions within its structure. Besides this, divergent validity was performed to test the comparative validity of the scale with different scales. It was found that a negative correlation between CompACT and Acceptance and Action Questionnaire (AAQ II) (r=‒0.365) and Depression Anxiety and Stress Scales (DASS-21) (r=‒0.403). CompACT had high internal consistency (all alpha and omega values ​​were found to be above 0.70) and test-retest reliability (r= 0.748).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The results suggest that the 20-item CompACT is reliable and valid for measuring psychological flexibility in Turkish emergency aid and disaster management students.\u003c/p\u003e","manuscriptTitle":"Preliminary validation of a Turkish version of the Comprehensive Assessment of Acceptance and Commitment Therapy Processes in students of the emergency aid and disaster management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 17:59:33","doi":"10.21203/rs.3.rs-7535464/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-09T06:05:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T03:05:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T20:37:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52259895148356199281792368320431375245","date":"2026-04-11T12:35:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273469694376433111849751636929890562062","date":"2026-04-09T01:05:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T07:39:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99505669545097802102487741348025038865","date":"2026-03-04T07:29:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T11:24:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-25T11:00:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T10:38:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T08:52:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-30T08:16:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b5289330-0260-422f-ac62-369d5de753a0","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-09T06:05:05+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":63306507,"name":"Health sciences/Health care"},{"id":63306508,"name":"Health sciences/Medical research"},{"id":63306509,"name":"Biological sciences/Psychology"},{"id":63306510,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-09T06:10:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 17:59:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7535464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7535464","identity":"rs-7535464","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}