Exploring the Applicability of a Multifactor Mindfulness Scale in Chinese College Context

Exploring the Applicability of a Multifactor Mindfulness Scale in Chinese College Context | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Applicability of a Multifactor Mindfulness Scale in Chinese College Context Dan Zhang, Jianbo Shen, Hongyu Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3938635/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Owing to the lack of a precise and comprehensive mindfulness measurement tool capable of capturing all facets of mindfulness, developing such an assessment tool has become an intriguing and worthwhile area of exploration. This study investigates the applicability of a multifactor mindfulness scale to Chinese college students. In particular, it tests the applicability of the Chinese version of the Comprehensive Inventory of Mindfulness Experiences (CHIME) in college students. Methods: Prior to the formal test, 410 subjects completed the CHIME-37. The feedback received from this pretest was used to obtain the final descriptions. During the formal assessment, 1927 subjects participated, and 490 students were retested two months later. The criteria-related validity of the CHIME-37 was assessed using instruments such as the subjective well-being scale, psychological well-being scale, peace of mind scale, self-reflection and insight scale, emotion regulation scale, depression-anxiety-stress scale, and sickness questionnaire. Results: The sample was randomly divided into two halves. In the exploratory factor analysis (EFA) of Sample 1 (n = 838), CHIME comprised 8 factors: 1) Awareness of internal experiences, 2) Awareness of external experiences, 3) Mindful action, 4) Acceptance and non-judgment, 5) Decentering and non-reactivity, 6) Experiential openness, 7) Relativity of thoughts and reality, and 8) Insightful understanding. The cumulative variance accounted for 70.696%. Confirmatory factor, criterion-related validity, and internal consistency analyses were conducted on the randomly split 947 samples for validation. Confirmatory factor analysis of Sample 2 confirmed the 8-factor model (x 2 /df = 1.751, CFI = 0.981, TLI = 0.979, RMSEA = 0.028). The internal consistency coefficients of the eight dimensions range from 0.848 to 0.914, with test-retest reliabilities ranging from 0.746 to 0.885, and split-half reliabilities ranging from 0.795 to 0.898. Total scores and scores on the eight dimensions are significantly positively correlated with subjective well-being, psychological well-being, emotion stability, and cognitive reappraisal (P < 0.01), while they are negatively correlated with physical and mental illnesses, depression-anxiety-stress, and expressive inhibition (P < 0.01). Conclusion: The revised version of the CHIME demonstrates robust reliability and validity, establishing it as a suitable tool for measuring the mindfulness levels of Chinese college students. Mindfulness The Chinese Comprehensive Inventory of Mindfulness Experiences Factor structure validation Figures Figure 1 Figure 2 Introduction At present, over 10 tools are available for assessing mindfulness; however, none of them can accurately capture all its attributes. A unanimous consensus on the specific traits encompassed within mindfulness is still lacking [1]. Currently, the definition of mindfulness given by Kabat-Zinn is widely accepted, stating that it is “A purposeful, nonjudgmental attention to present moment awareness.” [2] Mindfulness is viewed as a practice that involves fostering a curious, open, nonjudgmental, and accepting attitude. It directs attention and awareness to the present moment’s internal and external stimuli, encompassing emotions, cognition, and bodily sensations like touch, taste, smell, and breath [3]. Experience-oriented mindfulness views mindfulness as an individual’s awareness of various present moment mind-body experiences, placing emphasis on awareness and acceptance [4]. A proficiency-oriented approach underscores a range of mindfulness practices: mindfulness meditation, mindfulness attention training, and purely mental mindfulness exercises [5]. The competence-oriented perspective regards mindfulness as an inherent capacity in individuals, suggesting the enhancement of mindfulness abilities or skills through practices like mindful breathing, walking, and other mindfulness exercises. The trait-oriented approach perceives mindfulness as a trait-like variable, comparable to character strengths or virtues in positive psychology. Mindfulness, influenced by genetic and environmental factors, is a unique individual difference factor and a personality trait that can be modified through specific training [6]. The diverse definitions of mindfulness from these different perspectives imply that mindfulness is a multidimensional concept. Evaluating it necessitates adherence to theoretical standards, such as historical definitions of mindfulness, precision in measurement, considering psychological measurement properties, and hypothesis testing, including assessments of convergent and discriminant validity. Nevertheless, there are differing emphases among various scales, and the measurement of mindfulness abilities and levels has not been comprehensive across all scales [7]. For instance, the Mindful Attention Awareness Scale (MAAS) specifically focuses on the attentional aspect of mindfulness. The Kentucky Inventory of Mindfulness Skills (KIMS) and the Five Facet Mindfulness Inventory (FMI) assess mindfulness as a multifaceted concept. However, these facets differ from one another [8]. Research has indicated that the measured correlations of mindfulness among MAAS, CAMS（the Cognitive and Affective Mindfulness Scale-Revised）, FMI, KIMS, and PHLMS （the Philadelphia Mindfulness Scale） range between 0.21 and 0.67 [9]. Variations in the aspects of mindfulness addressed by different tools pose a direct obstacle to the comparability and reproducibility of research findings. In 2006, Ruth Baer and colleagues amalgamated the mentioned five mindfulness scales. They discerned five distinct and interpretable dimensions through exploratory and confirmatory factor analyses: observation, description, acting with awareness, non-judgment of inner experience, and non-reactivity to inner experience. However, these dimensions failed to fully capture all components of mindfulness. Furthermore, the diverse factors and constructs represented by assessment tools mirror different mindfulness skills. Following the COVID-19 pandemic, prominently endorsed mindfulness interventions like Mindfulness-Based Stress Reduction, Mindfulness-Based Cognitive Therapy, and mindfulness meditation awareness training have underscored the urgent need of scientifically precise mindfulness assessment tools to gauge the quality and comprehensiveness of these programs. In recent years, researchers have considerably heightened their scrutiny of the favorable effects of mindfulness on mental health [10], physical well-being [11], behavioral adaptation [12, 13], and wisdom [14]. Nevertheless, aligning these high-dimensional mindfulness skills or abilities with corresponding components in existing measurement tools remains challenging In 2014, Bergom et al. undertook theoretical derivations and data-driven analyses on eight mindfulness questionnaires [15]. The subscales and conceptual frameworks were incorporated within the questionnaires. This endeavor resulted in the development of the Comprehensive Mindfulness Experience Scale (CHIME-37). The empirical validation results from Australian adolescents, comprising four adolescent samples, supported an 8-factor, 25-item Adolescent Comprehensive Mindfulness Experience Scale [16]. The Dutch version of the Comprehensive Mindfulness Experience Scale has also passed the validation test. Furthermore, CHIME-SF, a brief form, has been established [17]. CHIME displays favorable psychological measurement properties in New Zealand samples [18]. It also exhibits invariance when evaluated among meditators and non-meditators [19] suggesting its potential as a scientifically precise tool for measuring mindfulness. However, CHIME, having been validated in Germany, is not currently accessible in China. This study seeks to translate and adapt CHIME into Chinese, evaluate its reliability and validity among non-clinical Chinese participants (university students), and scrutinize its psychometric properties within the context of Chinese cultural backgrounds. The objective is to establish a scientifically valid measurement tool for mindfulness research and clinical practice in China. Methods Participants We initially recruited 410 university students from a university in Hubei Province, China, for a pilot survey to assess potential issues with the wording of the questionnaire and finalize its content. The survey was conducted through on-site paper-and-pencil testing, administered by psychology graduate students who had undergone specialized training, and was carried out in a class setting. Furthermore, 372 valid questionnaires were collected, with 162 (43.5%) male participants and 210 (56.5%) female participants. The participants had an average age of 19.21 ± 0.63 years. Sample 1: For the formal assessment, the participants were split into two groups. Using a convenient sampling method, the first group underwent on-site paper-and-pencil testing. Following the same testing procedure as in the pilot survey, students from four universities in Jiangsu, Gansu, Sichuan, and Hubei were chosen as participants. The second group engaged in online testing, recruiting university students to complete a questionnaire with identical content to the paper version. A total of 2,113 questionnaires were distributed and collected. After excluding invalid responses such as patterned answers, 1,785 valid questionnaires were obtained, resulting in an effective rate of 84.4%. These valid questionnaires constituted responses from 819 (45.9%) male participants and 966 (54.1%) female participants. The participants’ average age was 20.17 ± 1.62 years. The participant data were randomly split into two groups: Group 1, comprising 838 valid datasets for item and exploratory factor analyses, and Group 2, comprising 947 valid datasets for confirmatory factor, criterion-related validity, and internal consistency analyses. Table 1 presents the characteristics of groups 1 and 2. Table 1 Descriptive statistics of the participants Group n % Age (Mean ± SD) 1 1.Gender Male 398 47.49% 19.98 ± 1.59 Female 440 52.51% 20.07 ± 1.60 2.Mindfulness Practice Yes 307 36.63% 19.99 ± 1.62 No 531 63.37% 20.05 ± 1.58 2 1.Gender Male 428 45.2 19.88 ± 1.50 Female 519 54.8 19.99 ± 1.49 2.Mindfulness Practice Yes 360 38.0 19.86 ± 1.35 No 587 62.0 19.90 ± 1.57 Sample 2: Two weeks following the formal assessment, a subset of participants was chosen for retesting utilizing the paper-and-pencil method. Accordingly, 490 questionnaires were distributed on-site. After eliminating unmatched data, 391 valid questionnaires were obtained. Among these, 160 were from male participants and 231 were from female participants, accounting for 40.9% and 59.1%, respectively. The participants had an average age of 19.53 ± 0.76 years. Measures Comprehensive inventory of mindfulness experiences The CHIME−37 questionnaire comprise d 37 items, encompassing eight subscales [ 20 ]: 1) Awareness of internal experiences, 2) Awareness of external experiences, 3) Mindful action, 4) Acceptance and non−judgment, 5) Decentering and non−reactivity, 6) Experiential openness, 7) Relativity of thoughts and reality, and 8) Insightful understanding. Each item wa s rated on a Likert scale ranging from 1 (never) to 7 (always). All samples u tilized the CHIME questionnaire . We adhered to the guidelines of the stage model for the cross-cultural adaptation of assessment tools proposed by Geisinger [ 21 ] to translate the original version of CHIME into Chinese. In the initial stage, a bilingual individual proficient in German and Chinese translated the CHIME items into Chinese. This individual, with 10 years of experience in mindfulness practice, possessed a profound understanding of the concepts. In the second stage, two bilingual individuals, fluent in German and Chinese and experienced in mindfulness practice, collaboratively assessed the initial translation. The evaluation aimed to ensure consistency with the original text and the comprehensibility of the translated version. After a joint review of the translation, feedback from the evaluators was provided to the translator in the third stage. Following this feedback, the translator revised the draft of the Chinese CHIME based on the evaluators’ suggestions. Any inconsistencies throughout the process were discussed and modified for proper alignment, ensuring that the expressions maintained the original German meaning while being clear and understandable. In the fourth stage, the authors introduced the initial draft of the Chinese CHIME to a small sample (n = 372). Their characteristics were similar to those of the final study sample (e.g., university undergraduates). Consistent with Geisinger’s suggestions, the participants from the convenience sample were interviewed by researchers to understand their experiences regarding the comprehensibility, wording, and understanding of the items. Drawing from participant feedback and response patterns, relevant issues regarding the questionnaire content were identified. The research team engaged in discussions to address these issues, leading to minor adjustments in the translation draft. Subsequent to these modifications, the final version of the Chinese CHIME was established. Satisfaction with life scale The Satisfaction with Life Scale (SWLS), developed by Diener et al. [ 22 ], was used to assess life satisfaction. The Chinese version of the SWLS has been utilized in previous large-scale cross-sectional studies [ 23 , 24 ]. The confirmatory factor analysis (CFA) indicated that this scale demonstrated a good fit: χ 2 /df = 2.859, RMSEA = 0.993, CFI = 0.997, TLI = 0.953, and SRMR = 0.010. The participants responded on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores indicating a greater subjective sense of well-being. The Cronbach’s α coefficient for the study sample was 0.838. Psychological well-being The Flourishing Scale (FS), comprising eight items, was used to assess psychological well-being (PWB) [ 25 ]. The Chinese version of the FS has been validated in the community and adolescent samples [ 26 ]. The CFA indicated that this scale demonstrated an acceptable fit: χ 2 /df = 3.322, RMSEA = 0.054, CFI = 0.995, TLI = 0.993, and SRMR = 0.008. The participants responded on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores indicating a greater sense of PWB. The Cronbach’s α coefficient was 0.896. Peace of mind This scale comprised seven items describing the respondents’ sense of internal peace and ease in their daily life [ 27 ]. The CFA demonstrated a good fit of this scale: χ 2 /df = 3.770, RMSEA = 0.060, CFI = 0.962, TLI = 0.953, and SRMR = 0.030. Sample items included the following: 1. My mind is free and at ease, and 4. I have peace and harmony in mind. The participants were asked to indicate the frequency of their feelings on a scale from 1 (Not at all) to 5 (All of the time). Peace of mind was assessed based on the sum of scores of the seven items, with a high score indicating a high level of peace of mind. The Cronbach alpha coefficient was 0.916. SicknessQ SicknessQ, a concise tool comprising nine items, was utilized to evaluate the perceived sickness behavior of individuals [ 28 , 29 ]. The CFA demonstrated the scale’s good fit: χ 2 /df = 4.907, RMSEA = 0.064, CFI = 0.976, TLI = 0.965, and SRMR = 0.030. The participants were required to assess their current feelings using a four-level scale ranging from 0 to 3 (0 = disagree, 1 = somewhat agree, 2 = mostly agree, 3 = agree), with higher scores indicating a lower overall level of physical and mental health for individuals. In this study, the overall questionnaire and each dimension exhibited Cronbach’s alpha coefficients of 0.847, 0.768, and 0.848, respectively. Depression, anxiety, and stress scale This scale was used to assess the participants’ psychological health. It has proven to be a reliable and effective measure for evaluating the mental well-being of the Chinese population [ 30 ]. The CFA indicated an acceptable fit of the scale: χ 2 /df = 3.513, RMSEA = 0.052, CFI = 0.957, TLI = 0.951, and SRMR = 0.027. Item scores were recorded using a 4-point Likert scale (1 = strongly disagree, 5 = strongly agree), with higher scores indicating higher levels of depression, anxiety, and stress for individuals. The overall questionnaire and each dimension demonstrated Cronbach’s alpha coefficients of 0.898, 0.917, 0.895, and 0.889, respectively. English version of the depression, anxiety, and stress scale This scale, developed by Grant et al. [ 31 ] and Liu et al. [ 32 ], comprised 20 self-assessment items and three subscales of Engagement Reflection, Motivation Reflection, and Insight. The CFA indicated that an acceptable fit of the scale: χ 2 /df = 5.727, RMSEA = 0.060, CFI = 0.940, TLI = 0.932, and SRMR = 0.071. Item scores were recorded using a 6-point Likert scale (1 = strongly disagree, 6 = strongly agree), with higher scores reflecting higher levels of self-reflection and insight for individuals. The overall questionnaire and each dimension demonstrated Cronbach’s alpha coefficients of 0.944, 0.954, and 0.893, respectively. Self-Reflection and Insight Scale The Self-Reflection and Insight Scale (SRIS) in English version was developed by Grant et al., consisting of 20 self-assessment items. The scale comprises three subscales: Reflective Engagement, Motivation Reflection, and Insight. The CFA demonstrated the scale’s good fit: χ 2 /df = 1.189, RMSEA = 0.014, CFI = 0.997, TLI = 0.997, and SRMR = 0.016. Items are scored using a Likert 6-point scale (1 = Strongly Disagree, 6 = Strongly Agree). Higher scores reflect higher levels of reflection and insight in individuals. In this study, the overall questionnaire and each dimension had Cronbach's α coefficients of 0.944, 0.954, and 0.893, respectively. Emotion regulation questionnaire This questionnaire, developed by Gross [ 33 , 34 ], comprised 10 items assessing two dimensions: Cognitive Reappraisal and Expressive Suppression. The CFA indicated that an acceptable fit of the scale: χ2/df = 1.254, RMSEA = 0.060, CFI = 0.999, TLI = 0.998, and SRMR = 0.016. Item scores were recorded on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores indicating a higher frequency of employing emotion regulation strategies. The Chinese version of the questionnaire demonstrated robust reliability and validity. The Cronbach’s alpha coefficients for each dimension of the questionnaire were 0.917 and 0.923, respectively. Data analysis Utilizing SPSS 22.0, we conducted the item, exploratory factor, internal consistency reliability, criterion-related validity, and test-retest reliability analyses. CFA was performed using MPLUS 8. Values below 0.05 were considered statistically significant. Furthermore, exact p values were reported to signify the level of significance in the findings. Ethical considerations Ethical considerations played a crucial role throughout the research process, with meticulous measures to ensure the well-being and rights of the participants. The study received ethical approval from the Ethics Committee at Central China Normal University, highlighting a commitment to adhering to the established ethical guidelines and promoting transparency and credibility. All participants provided written informed consent after receiving comprehensive information regarding the study’s purpose, procedures, potential risks, and benefits. The participants were assured of the voluntary nature of their participation and were given the freedom to withdraw from the study without facing any adverse consequences. Emphasizing the significance of informed consent demonstrates a dedication to respecting autonomy and upholding ethical standards. Maintaining privacy and confidentiality remained of utmost importance throughout the research process. The participants were assured that their personal information would be handled with the highest level of confidentiality and used exclusively for the intended purposes of the study. Robust data anonymization techniques were applied to prevent the disclosure of any identifiable information in publications or reports stemming from the study, safeguarding the participants’ anonymity and confidentiality. To mitigate potential harm, additional measures were taken to address the emotional well-being of the participants. Debriefing procedures were implemented. Furthermore, the participants were given access to support resources in case of any distress or discomfort arising from their participation in the study. Prioritizing the welfare of participants further strengthened the ethical underpinning of the research. In summary, ethical considerations in this study encompassed securing informed consent, guaranteeing privacy and confidentiality, minimizing potential harm to participants, and adhering to the ethical guidelines established by the Ethics Committee at Central China Normal University. These stringent measures were implemented to uphold ethical standards, protect the participants’ rights, and contribute to the overall integrity of the research process. Results Item analysis The critical ratio method was utilized for the item analysis of the questionnaire. The CHIME-37 item scores were arranged in descending order, with the bottom 27% of participants categorized as the low-scoring group and the top 27% as the high-scoring group. We examined disparities between the two groups on each item. Independent sample t-tests were conducted for each item’s scores, revealing significant differences for all items (P < 0.001). Furthermore, correlation analyses were performed between the scores of each item and the total score. The results indicated correlation coefficients ranging between 0.417 and 0.734, all statistically significant at 0.01 level. Further details are presented in Table 2 . Table 2 The overall correlation and decision values for each item of the CHIME-37 Items T R Items t r 1 18.005** 0.603** 20 16.577** 0.578** 2 15.710** 0.513** 21 12.451** 0.432** 3 15.896** 0.540** 22 13.550** 0.504** 4 13.869** 0.501** 23 16.971** 0.551** 5 17.434** 0.575** 24 19.669** 0.598** 6 16.681** 0.561** 25 16.022** 0.556** 7 15.494** 0.552** 26 15.953** 0.539** 8 20.648** 0.629** 27 18.981** 0.584** 9 16.051** 0.554** 28 18.221** 0.612** 10 16.141** 0.534** 29 11.053** 0.385** 12 18.309** 0.590** 30 14.269** 0.538** 12 21.340** 0.650** 31 16.448** 0.561** 13 16.842** 0.591** 32 12.350** 0.422** 14 13.734** 0.514** 33 19.038** 0.608** 15 19.387** 0.604** 34 13.345** 0.525** 16 19.159** 0.612** 35 15.450** 0.542** 17 15.600** 0.556** 36 15.524** 0.541** 18 11.382** 0.393** 37 18.335** 0.595** 19 21.284** 0.621** **p < 0.01 Structural validity Exploratory factor analysis The Chinese version of CHIME-37 underwent EFA using data from Group 1 (n = 838). EFA was employed with principal component analysis (PCA) and varimax rotation to assess conformity levels and assign names to the extracted factors in the internal structure of CHIME among Chinese college students. The Kaiser–Meyer–Olkin value was 0.940, and the Bartlett sphericity test yielded χ 2 = 17700.459 (df = 666, p < 0.001), indicating the suitability of the data for EFA. Factor extraction retained factors with eigenvalues greater than 1, resulting in a cumulative variance contribution of 70.696%. This reflects substantial explanatory power of the factors, preserving the original data information comprehensively. The variance percentage of the first factor was 30.754, which is less than 40%, suggesting the absence of severe common method bias. Moreover, the analysis of the scree plot (Fig. 1 ) led to the decision to extract eight factors. Although the 7-factor and 8-factor models demonstrated good to excellent fits, model fit indices indicated a superior fit for the 8-factor model. The chi-square difference test was significant, supporting the adoption of the more parsimonious (8-factor) model, as outlined in Table 3 . Factor loadings of the items illustrated a better conceptual fit with the 8-factor model. The factor loadings and communality for the 37 items are presented in Table 4 . In line with the nomenclature proposed by Bergomi et al., these eight factors were named as follows: Awareness of Inner Experience, Awareness of Outer Experience, Acting with Awareness, Acceptance and Non-Judgment, Decentering and Non-Reactivity, Openness to Experience, Relativity of Thoughts, and Insightful Understanding. Table 3 Exploratory Factor Analyses - Model Fit Comparisons Model RMSEA CFI TLI χ2 df Models compared χ2 df 1-factor 0.114 0.673 0.653 8319.78 629 2-factor 0.105 0.723 0.707 7126.564 628 1-factor against 2-factor 1193.216 1 3-factor 0.098 0.758 0.743 5305.354 626 2-factor against 3-factor 1821.21 2 4-factor 0.086 0.813 0.801 5004.739 623 3-factor against 4-factor 300.615 3 5-factor 0.078 0.846 0.835 4230.981 619 4-factor against 5-factor 773.758 4 6-factor 0.072 0.871 0.86 3648.039 614 5-factor against 6-factor 582.942 5 7-factor 0.052 0.934 0.927 2164.599 608 6-factor against 7-factor 1483.44 6 8-factor 0.028 0.981 0.979 1052.624 601 7-factor against 8-factor 1111.975 7 Note. RMSEA root-mean-square error of approximation; CFI comparative fit index; TLI Tucker–Lewis index. All p < 0.001 Table 4 Exploratory Factor Analysis of CHIME-37 With Item-Factor Loadings and Proportion of communality (N = 838) Items Factor loadings communality F1 F2 F3 F4 F5 F6 F7 F8 F1: Awareness of Inner Experience 1 0.740 0.671 5 0.771 0.689 13 0.756 0.679 28 0.759 0.699 33 0.765 0.703 F2: Awareness of Outer Experience 9 0.789 0.724 17 0.795 0.737 21 0.779 0.730 26 0.803 0.740 F3: Awareness of Outer Experience 10R 0.810 0.714 11 0.778 0.713 16 0.769 0.714 25 0.803 0.726 37 0.758 0.690 F4: Accepting and Nonjudgmental Attitude 2 0.782 0.693 7 0.768 0.700 31 0.785 0.730 35 0.780 0.712 F5: Non-Reactivity and Decentering 8 0.784 0.717 12 0.759 0.707 15 0.807 0.731 19 0.738 0.661 24 0.764 0.681 27 0.790 0.698 F6: Openness to Experience 18 0.826 0.714 21 0.834 0.742 29 0.827 0.714 32 0.834 0.739 F7: Relativity of Thoughts and Reality 4 0.782 0.688 22 0.794 0.710 30 0.769 0.697 34 0.774 0.692 F8: Insightful Understanding 3 0.762 0.664 6 0.795 0.719 14 0.816 0.719 23 0.787 0.706 36 0.786 0.695 Confirmatory factor analysis CFA was performed on the structure of the Adolescent Autonomy Questionnaire using Sample 2 (n = 947). Initially, we established an 8-factor model based on the factor structure of the original questionnaire and conducted structural validation for the Chinese version to assess its applicability. The original factor structure included the following: ① Awareness of Inner Experience (items 1, 5, 14, 29, and 34), ② Awareness of External Experience (items 9, 18, 21, and 27), ③ Acting with Awareness (items 10, 12, 17, 26, and 38), ④ Acceptance and nonjudgmental attitude (items 2, 7, 32, and 36), ⑤ Non-Reactivity and Decentering (items 8, 13, 16, 20, 25, and 28), ⑥ Openness to Experience (items 19, 30, 33, and 22), ⑦ Relativity of Thoughts and Reality (items 4, 23, 31, and 35), and ⑧ Insightful Understanding (items 3, 6, 15, 24, and 37). The results of the CFA revealed that the fit indices for the 8-factor model were χ²/df = 1.751, CFI = 0.981, TLI = 0.979, SRMR = 0.027, and RMSEA = 0.028, indicating a relatively good fit. This finding suggests that the original factor structure is also applicable to the Chinese version of the questionnaire. Figure 2 depicts standardized parameters of the 8-factor model of the CHIME-37. Internal validity analysis A CFA of the CHIME questionnaire was conducted. Internal validity indicators, inter-dimension correlations, and convergent validity were computed. Table 5 presents the detailed information. The analysis revealed significant correlations between CHIME and its dimensions, with Pearson correlation coefficients ranging between 0.302 and 0.704. All pairwise correlations between CHIME dimensions were statistically significant, with the average variance extracted (AVE) value exceeding 0.5 for each dimension. This finding suggests that the CHIME questionnaire effectively captures various facets of mindfulness, demonstrating robust internal discriminant validity. Table 5 Correlation Coefficients and AVE Values Among Dimensions of the Chinese Version of CHIME-37 Dimensions 1 2 3 4 5 6 7 8 1: Awarelnt 0.780 2: AwareExt 0.689** 0.856 3: ActAware 0.630** 0.560** 0.812 4: AccNJ 0.680** 0.615** 0.675** 0.782 5: DecNR 0.765** 0.706** 0.634** 0.684** 0.796 6: Openness 0.434** 0.370** 0.302** 0.328** 0.391** 0.771 7: Relativity 0.704** 0.579** 0.567** 0.565** 0.633** 0.427** 0.796 8: Insight 0.650** 0.589** 0.566** 0.595** 0.682** 0.312** 0.566** 0.794 Note: **p < 0.01, The average variance extracted (AVE) values are provided on the diagonal. Criterion-related validity analysis We conducted Pearson correlation analyses to examine the relations between the total score and each dimension of CHIME and scores on various factors. These factors included subjective well-being, psychological well-being, mental tranquility, physical and mental health, depression-anxiety-stress, self-reflection and insight, cognitive reappraisal, and expressive suppression. The total mindfulness experience score and its dimensions exhibited significant positive correlations with subjective well-being, psychological well-being, mental tranquility, and cognitive reappraisal (P < 0.01). Furthermore, the total mindfulness experience score and its dimensions exhibited negative correlations with physical and mental health, depression-anxiety-stress, and expressive suppression (P < 0.01) (Refer to Table 6 for details). Table 6 Analysis of the Calibration-Related Validity of CHIME-37 Awarelnt AwareExt ActAware AccNJ DecNR Openness Relativity Insight CHIME Subjective well-being 0.288** 0.291** 0.280** 0.296** 0.330** 0.246** 0.256** 0.322** 0.380** Psychological well-being 0.419** 0.412** 0.400** 0.407** 0.429** 0.291** 0.338** 0.384** 0.508** Peace of mind 0.439** 0.377** 0.381** 0.365** 0.412** 0.167** 0.368** 0.325** 0.470** Physical condition -0.230** -0.176** -0.237** -0.299** -0.215** -0.238** -0.221** -0.190** -0.293** Psychological status -0.214** -0.145** -0.257** -0.267** -0.208** -0.193** -0.166** -0.159** -0.264** Psychosomatic illness -0.242** -0.173** -0.271** -0.307** -0.231** -0.233** -0.208** -0.189** -0.303** Stress -0.437** -0.377** -0.454** -0.420** -0.443** -0.296** -0.385** -0.373** -0.526** Anxiety -0.376** -0.373** -0.416** -0.390** -0.388** -0.265** -0.313** -0.338** -0.472** Depression -0.341** -0.331** -0.418** -0.400** -0.377** -0.251** -0.329** -0.362** -0.463** Depression-anxiety-stress -0.532** -0.496** -0.592** -0.556** -0.557** -0.374** -0.474** -0.494** -0.673** Self-reflection 0.377** 0.341** 0.364** 0.377** 0.413** 0.244** 0.316** 0.315** 0.455** Insight 0.323** 0.283** 0.351** 0.348** 0.337** 0.253** 0.276** 0.310** 0.409** Self-reflection and insight 0.407** 0.365** 0.409** 0.418** 0.440** 0.281** 0.344** 0.356** 0.499** Cognitive reappraisal 0.402** 0.365** 0.337** 0.339** 0.383** 0.284** 0.312** 0.295** 0.448** Expression inhibition -0.493** -0.426** -0.498** -0.436** -0.505** -0.354** -0.417** -0.463** -0.594** **p < 0.01 Reliability analysis The overall Cronbach’s α coefficient for the questionnaire was 0.961, with individual dimensions ranging between 0.883 and 0.961. The overall test-retest reliability coefficient of the questionnaire was 0.840 (P < 0.01), and test-retest reliability coefficients for individual dimensions ranged between 0.746** and 0.732** (Ps < 0.01). When dividing the questionnaire into two equal halves based on item numbers and correlating the scores of the two halves, the split-half reliability for the overall questionnaire was 0.871, with individual dimensions ranging between 0.795 and 0.919 (Refer to Table 7 for the results of the reliability analysis). Table 7 Descriptive Statistics, Test-Retest Reliability, Split-Half Reliability, and Internal Consistency Reliability Dimension M ± SD Retest reliability Split-half reliability α Awarelnt 24.48 ± 5.23 0.746** 0.853** 0.883 AwareExt 20.43 ± 4.69 0.848** 0.919** 0.914 ActAware 20.10 ± 5.59 0.885** 0.856** 0.905 AccNJ 16.71 ± 4.34 0.840** 0.802** 0.855 DecNR 26.13 ± 6.30 0.825** 0.898** 0.910 Openness 17.46 ± 4.13 0.861** 0.891** 0.848 Relativity 18.86 ± 3.98 0.856** 0.871** 0.871 Insight 23.11 ± 5.14 0.886** 0.795** 0.891 CHIME 167.28 ± 30.20 0.840** 0.894** 0.961 Measurement invariance test Following the recommendation of Cheng and Rensvold (2002) [ 35 ], we employed ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 as criteria for assessing invariance. The results, presented in Table 8 , indicate that the metric and scalar invariance hypotheses hold true within the gender subgroup. In the subgroup based on the extent of mindfulness practice, the metric and scalar invariance hypotheses were confirmed. These findings suggest that the metric and scalar invariance hypotheses are supported within the identified subgroups. Table 8 Measurement invariance test Model Chi-square df CFI RMSEA (90CI) △CFI △RMSEA Gender Configural 1638.994 1202 0.981 0.028 (0.024,0.031) Metric 1658.591 1231 0.982 0.027 (0.024,0.030) 0.001 0.001 Scalar 1684.576 1260 0.982 0.027 (0.023,0.030) 0.000 0.000 Mindfulness Practice Configural 1682.938 1202 0.980 0.029 (0.026,0.032) Metric 1710.768 1231 0.980 0.029 (0.025,0.032) 0.000 0.000 Scalar 1748.715 0260 0.979 0.029 (0.025,0.032) 0.001 0.000 Discussion This study aimed to investigate, for the first time, the suitability of a multifactorial mindfulness measurement for Chinese university students. Following standard procedures, the CHIME questionnaire was translated into Chinese and administered to Chinese university students to assess CHIME-37. The results supported the 8-factor, 37-item mindfulness measurement tool (CHIME-37) among Chinese university students. The results demonstrated excellent model fit indices and good internal consistency for the total score and eight subscales. Differentiation among the eight dimensions is a crucial characteristic of the CHIME. First, the AVE values for each of the eight dimensions exceeded 0.5, indicating good discriminant validity among the internal dimensions of the Chinese version of CHIME. This finding suggests that the eight skills and abilities of mindfulness are distinguishable from each other. Second, the Pearson correlation coefficients among the eight dimensions ranged between 0.3 and 0.7. This finding indicates that these dimensions are correlated but not overlapping. This finding aligns with the theoretical construction of mindfulness, which posits eight distinct components or skills. This has significant value for the comprehensive and accurate measurement of mindfulness levels and the assessment of the completeness and quality of mindfulness programs. Furthermore, mindfulness demonstrated moderate correlations with criterion-related validity. Thus, CHIME plays a strong predictive role in subjective well-being, psychological well-being, emotion regulation, insight, anxiety, depression, perceived stress, and overall health. Notably, the correlation between mindfulness and PWB was significantly higher than that between mindfulness and subjective well-being (SWLS, hedonic well-being). This finding confirms that mindfulness is strongly connected with aspects related to self-actualization, such as autonomy, personal growth, life goals [ 36 ], self-acceptance, environmental mastery, and positive relationships with others [ 37 ]. This alignment with Eastern cultural values emphasizes the realization of human potential, as opposed to the Western emphasis on individualistic hedonic well-being. Reflection and insight, considered crucial traits, skills, or abilities in Eastern cultures, are well represented in the Chinese version of CHIME (factors 7 and 8). Moreover, the strong correlation of mindfulness with reflection and insight provides excellent validation. Therefore, the revised mindfulness experience questionnaire, CHIME, is suitable for the current cultural context in China. The refined Chinese version of CHIME affirms that mindfulness can be categorized into three groups: awareness (encompassing the basic dimensions of mindfulness), behavioral regulation (encompassing mindful actions and non-reactivity to inner experiences), and evaluative wisdom (representing the most advanced mindfulness skills) [ 38 ]. As per the theoretical framework of the CHIME Scale, mindfulness comprises eight components or skills, further categorized into 3 groups. Specifically, Factor 1, Internal Awareness, and Factor 2, External Awareness, signify the foundational skills of mindfulness. Cultivating these skills may enhance individuals’ focus on present experiences and actions, facilitating a shift from automatic navigation to an existence mode and enabling more skillful responses to challenges. Factor 3, Mindful Actions, and Factor 4, Acceptance and Non-Judgment, represent intermediate skills in behavioral regulation within mindfulness. Practicing mindfulness in daily life can result in improved focus and executive functioning for individuals. In the face of failure and setbacks, individuals can engage in self-care, allowing themselves space for acceptance and self-compassion and maintaining a rational mindset, emotions, and behaviors. Factor 5, Decentering and Non-Reactivity, and Factor 6, Openness to Experiences, signify intermediate skills in emotional regulation within mindfulness. When confronted with painful emotions, sensations, experiences, and thoughts, individuals can release attachments, avoid struggling, surrender without resistance, and uphold an open awareness or meta-awareness without evading pain. Factors 7, Relativity of Thoughts and Reality, and Factor 8, Insightful Thoughts, signify high-level skills or abilities in the cognitive defusion of mindfulness wisdom. By employing metacognitive patterns, individuals can overcome negative thoughts, emotions, and the cognitive fusion of needs, becoming aware of life’s imperfections, impermanence, and the concept of non-self. This transcendent practice enhances wisdom and insight into the impermanence of life. Furthermore, our research results validate that the Chinese version of CHIME aligns with the four effective mechanisms of mindfulness: attention regulation, body awareness, emotion regulation, and a change in self-perspective. Through the synergistic action of these four mechanisms, individuals’ self-regulatory abilities are enhanced [ 39 ]. In the analysis of criterion-related validity, the Chinese version of CHIME exhibited significant positive correlations with subjective well-being, psychological well-being, emotional tranquility, self-reflection, and insight but showed negative correlations with physical and mental health issues, depression-anxiety-stress, and expressive inhibition. These findings suggest that mindfulness skills and abilities are centered on redirecting individuals’ attention to the direct experience of their body, mind, emotions, sensations, and thoughts. This process helps prevent students from comparing themselves in terms of achievements or other aspects, fostering increased happiness, inner peace, enhanced reflection and insight, and improved cognitive regulation abilities among them. Simultaneously, mindfulness skills and abilities can alleviate physical and mental suffering, reduce perceived stress levels, and minimize emotional suppression. A decline in mindfulness may be associated with a decrease in physical health, psychological distress, and emotional imbalance. Mindfulness serves as a positive psychological resource and trait for college students, aiding in their adjustment of body and mind, emotion regulation, increased happiness, and the enhancement of wisdom. Furthermore, the early adulthood phase in college represents a peak period for wisdom development [ 40 ]. Mindfulness may contribute to the enhancement of wisdom and insight, providing individuals with additional resources to cope with academic, life, and societal challenges. Implications for research and practice The eight factors of mindfulness correspond to eight skills or abilities associated with mindfulness, positively contributing to enhancing college students’ psychological resilience, mental qualities, emotional regulation, and wisdom. This offers valuable insights for improving mental health among college students. Generally, these eight skills and abilities of mindfulness are developed progressively, beginning with basic skills and advancing to intermediate and advanced skills. Factors 1 and 2 signify the fundamental dimensions of mindfulness (internal sensations), encompassing basic mindfulness skills. Individuals can nurture the ability to stay connected with their inner and outer experiences through body scanning and breath observation. Factors 3 and 4 can be enhanced through mindful walking, stretching, sitting meditation (non-selective awareness practices), and mindful dishwashing. Factors 5 and 6 foster psychological resilience, emotional acceptance, and adaptability to present moment experiences. Meditation on “recognizing aversion” and the three-minute breath space exercise can contribute to these skills. Factors 7 and 8 help develop individuals’ ability to maintain clear cognition, benevolence, and take wise and insightful actions following life’s challenges. Mindfulness meditation (recognizing the relativity of thoughts and reality) and compassionate meditation can contribute to developing these skills. For college students engaging in mindfulness practices, beginners can start with basic exercises and progressively move on to intermediate and advanced practices. After establishing a solid foundation in basic skills, individuals can gradually explore more advanced practices, following a step-by-step approach starting from easy tasks and then switching to challenging tasks. Limitations and future research First, the participants were exclusively recruited from four universities in the southern, central, and western regions of China, potentially limiting the generalizability of the results to universities across the country. Future research could address this limitation by seeking a more representative sample from various regions nationwide, ensuring a broader representation, particularly from the eastern and northern parts of China. Including students from diverse geographical areas would enhance the extrapolation of research results to the entire population of Chinese university students. Second, the reliance on self-report measures as the sole assessment tool is another limitation. To overcome this, future research should use diverse measurement methods, such as objective observations, interviews with teachers, parents, or school staff, or the integration of standardized assessments. The incorporation of multiple measurement approaches would enable researchers to conduct a more comprehensive and thorough evaluation of mindfulness in Chinese university students, mitigating potential measurement biases and offering a more accurate depiction. Third, the study’s cross-sectional design restricts the ability to infer causality from the presented findings. The study focused on a non-clinical sample of university students. Replicating these findings to a clinical population of university students is highly recommended to investigate the relation between mindfulness and psychopathological indicators in university students. Given these limitations, several recommendations emerge for future research. First, efforts should be made to secure a more representative sample of university students from diverse regions of China to enhance the generalizability of survey results. Alongside self-report measures, incorporating diverse assessment methods, such as objective observations and standardized assessments, can provide a more holistic assessment of mindfulness levels and abilities in university students. Emphasis should be placed on conducting longitudinal studies covering multiple academic years to capture the dynamic nature of mindfulness levels and abilities in Chinese university students. Conclusions The Chinese adaptation of the CHIME demonstrates robust psychometric properties and is well-suited for assessing mindfulness in Chinese college students. Moreover, engaging Chinese college students in mindfulness practices may be beneficial for them, contributing to the enhancement of their physical and mental well-being. Mindfulness practice is shown to be effective in regulating emotions, fostering a heightened sense of happiness, and promoting overall wisdom among Chinese college students. Declarations Authors' contributions ZD contributed to the conceptualization and design of the study, conducted data collection, analysis, and interpretation. ZD played a significant role in drafting the original manuscript and participated in reviewing, editing, and revising its content. ZD provided final approval of the manuscript, ensuring its integrity, and assumed public responsibility for the work.SJB was involved in the conceptualization and design, translation, and polishing of the study. SJB contributed valuable insights and suggestions, ensuring the accuracy and clarity of the paper's presentation. SJB also participated in drafting and revising important intellectual content to maintain the manuscript's quality and accuracy.MHY was responsible for the conceptualization and design of the study, as well as its revision, validation, and supervision. MHY provided valuable guidance and direction throughout the research process, ensuring the quality and accuracy of the manuscript. MHY also supervised the validation process, ensuring the credibility and effectiveness of the findings. Acknowledgements We would like to acknowledge all the participants of this study. Funding Not applicable. Availability of data and materials The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate This study was approved by the Ethics Committee for Psychological Research of the corresponding author’s institution (Central China Normal University) .Participants provided written informed consent, emphasizing autonomy and ethical standards. Privacy and confidentiality were maintained, and robust data anonymization techniques were applied. Additional measures addressed emotional well-being, including debriefing procedures and access to support resources. 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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-3938635","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273027839,"identity":"b14ec05a-1389-4877-9e0d-807a972f18ec","order_by":0,"name":"Dan Zhang","email":"","orcid":"","institution":"Central China Normal University (CCNU)","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Zhang","suffix":""},{"id":273027840,"identity":"15a67825-d77f-4508-9430-57855fb2c1d4","order_by":1,"name":"Jianbo Shen","email":"","orcid":"","institution":"The National Tax Institute of STA","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Shen","suffix":""},{"id":273027841,"identity":"bf1baca1-4e0a-4a77-b4bf-af3eb6a012a5","order_by":2,"name":"Hongyu Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACHgaGD0CSDy5wgAgtjDOAJBtJWpiBFjEQr0W3/ewxadscOxk2BuaHnwvbGOT4biQwfi7Ao8XsTF6adO62ZKDD2IylZ7YxGEveSGCWnoFPy4EcM6AWZqAWHjZm3jaGxA03EtjATsWp5fwbM2nLbfVwLfWEtdwA2sK47TBcS4IBYS1vjC17tx0Hqgf6heechOHMMw+bpfE7LMfwxs9t1fb87M0PP/OU2cjzHU8++BmfFiBgkQBTzGASxGZswK8BqPYDIRWjYBSMglEwwgEAnCM81TvmF5gAAAAASUVORK5CYII=","orcid":"","institution":"Central China Normal University (CCNU)","correspondingAuthor":true,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-02-08 03:29:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3938635/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3938635/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51239845,"identity":"b8036d8f-1133-41bd-9feb-974eeda49402","added_by":"auto","created_at":"2024-02-16 17:24:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122258,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3938635/v1/f3b1c75a62428c9e7219eb44.jpeg"},{"id":51240335,"identity":"7773c542-2dc9-4b6a-90a1-4d33f5cee56c","added_by":"auto","created_at":"2024-02-16 17:32:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109210,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3938635/v1/f4affc45a7a5f0f760b8fcb3.png"},{"id":53934770,"identity":"f20a812d-4adf-4e17-87a7-0f77a82e8c2f","added_by":"auto","created_at":"2024-04-02 11:50:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":709870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3938635/v1/52c331ab-bb30-4e9e-9fcc-1c424b430480.pdf"},{"id":51239846,"identity":"bb025188-7f64-410a-9fd1-db2e0d9f1707","added_by":"auto","created_at":"2024-02-16 17:24:10","extension":"doc","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":27136,"visible":true,"origin":"","legend":"","description":"","filename":"TheChineseComprehensiveInventoryofMindfulness.doc","url":"https://assets-eu.researchsquare.com/files/rs-3938635/v1/a5b57b5460aa0e7b9e222144.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Applicability of a Multifactor Mindfulness Scale in Chinese College Context","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAt present, over 10 tools are available for assessing mindfulness; however, none of them can accurately capture all its attributes. A unanimous consensus on the specific traits encompassed within mindfulness is still lacking\u0026nbsp;[1].\u003c/p\u003e\n\u003cp\u003eCurrently, the definition of mindfulness given by Kabat-Zinn is widely accepted, stating that it is \u0026ldquo;A purposeful, nonjudgmental attention to present moment awareness.\u0026rdquo;\u0026nbsp;[2]\u003c/p\u003e\n\u003cp\u003eMindfulness is viewed as a practice that involves fostering a curious, open, nonjudgmental, and accepting attitude. It directs attention and awareness to the present moment\u0026rsquo;s internal and external stimuli, encompassing emotions, cognition, and bodily sensations like touch, taste, smell, and breath\u0026nbsp;[3].\u003c/p\u003e\n\u003cp\u003eExperience-oriented mindfulness views mindfulness as an individual\u0026rsquo;s awareness of various present moment mind-body experiences, placing emphasis on awareness and acceptance\u0026nbsp;[4].\u003c/p\u003e\n\u003cp\u003eA proficiency-oriented approach underscores a range of mindfulness practices: mindfulness meditation, mindfulness attention training, and purely mental mindfulness exercises\u0026nbsp;[5]. The competence-oriented perspective regards mindfulness as an inherent capacity in individuals, suggesting the enhancement of mindfulness abilities or skills through practices like mindful breathing, walking, and other mindfulness exercises. The trait-oriented approach perceives mindfulness as a trait-like variable, comparable to character strengths or virtues in positive psychology. Mindfulness, influenced by genetic and environmental factors, is a unique individual difference factor and a personality trait that can be modified through specific training\u0026nbsp;[6]. The diverse definitions of mindfulness from these different perspectives imply that mindfulness is a multidimensional concept. Evaluating it necessitates adherence to theoretical standards, such as historical definitions of mindfulness, precision in measurement, considering psychological measurement properties, and hypothesis testing, including assessments of convergent and discriminant validity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, there are differing emphases among various scales, and the measurement of mindfulness abilities and levels has not been comprehensive across all scales\u0026nbsp;[7].\u003c/p\u003e\n\u003cp\u003eFor instance, the Mindful Attention Awareness Scale (MAAS) specifically focuses on the attentional aspect of mindfulness. The Kentucky Inventory of Mindfulness Skills (KIMS) and the Five Facet Mindfulness Inventory (FMI) assess mindfulness as a multifaceted concept. However, these facets differ from one another\u0026nbsp;[8].\u003c/p\u003e\n\u003cp\u003eResearch has indicated that the measured correlations of mindfulness among MAAS, CAMS（the Cognitive and Affective Mindfulness Scale-Revised）, FMI, KIMS, and PHLMS\u0026nbsp;（the Philadelphia Mindfulness Scale）\u0026nbsp;range between 0.21 and 0.67\u0026nbsp;[9]. Variations in the aspects of mindfulness addressed by different tools pose a direct obstacle to the comparability and reproducibility of research findings. In 2006, Ruth Baer and colleagues amalgamated the mentioned five mindfulness scales. They discerned five distinct and interpretable dimensions through exploratory and confirmatory factor analyses: observation, description, acting with awareness, non-judgment of inner experience, and non-reactivity to inner experience. However, these dimensions failed to fully capture all components of mindfulness. Furthermore, the diverse factors and constructs represented by assessment tools mirror different mindfulness skills. Following the COVID-19 pandemic, prominently endorsed mindfulness interventions like Mindfulness-Based Stress Reduction, Mindfulness-Based Cognitive Therapy, and mindfulness meditation awareness training have underscored the urgent need of scientifically precise mindfulness assessment tools to gauge the quality and comprehensiveness of these programs.\u003c/p\u003e\n\u003cp\u003eIn recent years, researchers have considerably heightened their scrutiny of the favorable effects of mindfulness on mental health\u0026nbsp;[10], physical well-being\u0026nbsp;[11], behavioral adaptation\u0026nbsp;[12, 13], and wisdom\u0026nbsp;[14]. Nevertheless, aligning these high-dimensional mindfulness skills or abilities with corresponding components in existing measurement tools remains challenging\u003c/p\u003e\n\u003cp\u003eIn 2014, Bergom et al. undertook theoretical derivations and data-driven analyses on eight mindfulness questionnaires\u0026nbsp;[15]. The subscales and conceptual frameworks were incorporated within the questionnaires. This endeavor resulted in the development of the Comprehensive Mindfulness Experience Scale (CHIME-37). The empirical validation results from Australian adolescents, comprising four adolescent samples, supported an 8-factor, 25-item Adolescent Comprehensive Mindfulness Experience Scale\u0026nbsp;[16]. The Dutch version of the Comprehensive Mindfulness Experience Scale has also passed the validation test. Furthermore, CHIME-SF, a brief form, has been established\u0026nbsp;[17].\u003c/p\u003e\n\u003cp\u003eCHIME displays favorable psychological measurement properties in New Zealand samples [18]. It also exhibits invariance when evaluated among meditators and non-meditators [19] suggesting its potential as a scientifically precise tool for measuring mindfulness. However, CHIME, having been validated in Germany, is not currently accessible in China. This study seeks to translate and adapt CHIME into Chinese, evaluate its reliability and validity among non-clinical Chinese participants (university students), and scrutinize its psychometric properties within the context of Chinese cultural backgrounds. The objective is to establish a scientifically valid measurement tool for mindfulness research and clinical practice in China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe initially recruited 410 university students from a university in Hubei Province, China, for a pilot survey to assess potential issues with the wording of the questionnaire and finalize its content. The survey was conducted through on-site paper-and-pencil testing, administered by psychology graduate students who had undergone specialized training, and was carried out in a class setting. Furthermore, 372 valid questionnaires were collected, with 162 (43.5%) male participants and 210 (56.5%) female participants. The participants had an average age of 19.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63 years.\u003c/p\u003e \u003cp\u003eSample 1: For the formal assessment, the participants were split into two groups. Using a convenient sampling method, the first group underwent on-site paper-and-pencil testing. Following the same testing procedure as in the pilot survey, students from four universities in Jiangsu, Gansu, Sichuan, and Hubei were chosen as participants. The second group engaged in online testing, recruiting university students to complete a questionnaire with identical content to the paper version. A total of 2,113 questionnaires were distributed and collected. After excluding invalid responses such as patterned answers, 1,785 valid questionnaires were obtained, resulting in an effective rate of 84.4%. These valid questionnaires constituted responses from 819 (45.9%) male participants and 966 (54.1%) female participants. The participants\u0026rsquo; average age was 20.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 years. The participant data were randomly split into two groups: Group 1, comprising 838 valid datasets for item and exploratory factor analyses, and Group 2, comprising 947 valid datasets for confirmatory factor, criterion-related validity, and internal consistency analyses. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of groups 1 and 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.Mindfulness Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.Mindfulness Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSample 2: Two weeks following the formal assessment, a subset of participants was chosen for retesting utilizing the paper-and-pencil method. Accordingly, 490 questionnaires were distributed on-site. After eliminating unmatched data, 391 valid questionnaires were obtained. Among these, 160 were from male participants and 231 were from female participants, accounting for 40.9% and 59.1%, respectively. The participants had an average age of 19.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eComprehensive inventory of mindfulness experiences\u003c/h2\u003e \u003cp\u003e \u003csup\u003eThe CHIME\u0026minus;37 questionnaire comprise\u003c/sup\u003ed \u003csup\u003e37 items, encompassing\u003c/sup\u003e eight \u003csup\u003esubscales\u003c/sup\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: \u003csup\u003e1) Awareness of internal experiences, 2) Awareness of external experiences, 3) Mindful action, 4) Acceptance and non\u0026minus;judgment, 5) Decentering and non\u0026minus;reactivity, 6) Experiential openness, 7) Relativity of thoughts and reality, and 8) Insightful understanding. Each item\u003c/sup\u003e wa\u003csup\u003es\u003c/sup\u003e rated \u003csup\u003eon a Likert scale ranging from 1 (never) to 7 (always). All samples u\u003c/sup\u003etilized \u003csup\u003ethe CHIME questionnaire\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe adhered to the guidelines of the stage model for the cross-cultural adaptation of assessment tools proposed by Geisinger [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to translate the original version of CHIME into Chinese.\u003c/p\u003e \u003cp\u003eIn the initial stage, a bilingual individual proficient in German and Chinese translated the CHIME items into Chinese. This individual, with 10 years of experience in mindfulness practice, possessed a profound understanding of the concepts. In the second stage, two bilingual individuals, fluent in German and Chinese and experienced in mindfulness practice, collaboratively assessed the initial translation. The evaluation aimed to ensure consistency with the original text and the comprehensibility of the translated version. After a joint review of the translation, feedback from the evaluators was provided to the translator in the third stage. Following this feedback, the translator revised the draft of the Chinese CHIME based on the evaluators\u0026rsquo; suggestions. Any inconsistencies throughout the process were discussed and modified for proper alignment, ensuring that the expressions maintained the original German meaning while being clear and understandable.\u003c/p\u003e \u003cp\u003eIn the fourth stage, the authors introduced the initial draft of the Chinese CHIME to a small sample (n\u0026thinsp;=\u0026thinsp;372). Their characteristics were similar to those of the final study sample (e.g., university undergraduates). Consistent with Geisinger\u0026rsquo;s suggestions, the participants from the convenience sample were interviewed by researchers to understand their experiences regarding the comprehensibility, wording, and understanding of the items. Drawing from participant feedback and response patterns, relevant issues regarding the questionnaire content were identified. The research team engaged in discussions to address these issues, leading to minor adjustments in the translation draft. Subsequent to these modifications, the final version of the Chinese CHIME was established.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSatisfaction with life scale\u003c/h2\u003e \u003cp\u003eThe Satisfaction with Life Scale (SWLS), developed by Diener et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], was used to assess life satisfaction. The Chinese version of the SWLS has been utilized in previous large-scale cross-sectional studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The confirmatory factor analysis (CFA) indicated that this scale demonstrated a good fit: χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;2.859, RMSEA\u0026thinsp;=\u0026thinsp;0.993, CFI\u0026thinsp;=\u0026thinsp;0.997, TLI\u0026thinsp;=\u0026thinsp;0.953, and SRMR\u0026thinsp;=\u0026thinsp;0.010. The participants responded on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores indicating a greater subjective sense of well-being. The Cronbach\u0026rsquo;s α coefficient for the study sample was 0.838.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003ePsychological well-being\u003c/h2\u003e \u003cp\u003eThe Flourishing Scale (FS), comprising eight items, was used to assess psychological well-being (PWB) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Chinese version of the FS has been validated in the community and adolescent samples [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The CFA indicated that this scale demonstrated an acceptable fit: χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;3.322, RMSEA\u0026thinsp;=\u0026thinsp;0.054, CFI\u0026thinsp;=\u0026thinsp;0.995, TLI\u0026thinsp;=\u0026thinsp;0.993, and SRMR\u0026thinsp;=\u0026thinsp;0.008. The participants responded on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores indicating a greater sense of PWB. The Cronbach\u0026rsquo;s α coefficient was 0.896.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePeace of mind\u003c/h2\u003e \u003cp\u003eThis scale comprised seven items describing the respondents\u0026rsquo; sense of internal peace and ease in their daily life [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The CFA demonstrated a good fit of this scale: χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;3.770, RMSEA\u0026thinsp;=\u0026thinsp;0.060, CFI\u0026thinsp;=\u0026thinsp;0.962, TLI\u0026thinsp;=\u0026thinsp;0.953, and SRMR\u0026thinsp;=\u0026thinsp;0.030. Sample items included the following: 1. My mind is free and at ease, and 4. I have peace and harmony in mind. The participants were asked to indicate the frequency of their feelings on a scale from 1 (Not at all) to 5 (All of the time). Peace of mind was assessed based on the sum of scores of the seven items, with a high score indicating a high level of peace of mind. The Cronbach alpha coefficient was 0.916.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSicknessQ\u003c/h2\u003e \u003cp\u003eSicknessQ, a concise tool comprising nine items, was utilized to evaluate the perceived sickness behavior of individuals [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The CFA demonstrated the scale\u0026rsquo;s good fit: χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;4.907, RMSEA\u0026thinsp;=\u0026thinsp;0.064, CFI\u0026thinsp;=\u0026thinsp;0.976, TLI\u0026thinsp;=\u0026thinsp;0.965, and SRMR\u0026thinsp;=\u0026thinsp;0.030. The participants were required to assess their current feelings using a four-level scale ranging from 0 to 3 (0\u0026thinsp;=\u0026thinsp;disagree, 1\u0026thinsp;=\u0026thinsp;somewhat agree, 2\u0026thinsp;=\u0026thinsp;mostly agree, 3\u0026thinsp;=\u0026thinsp;agree), with higher scores indicating a lower overall level of physical and mental health for individuals. In this study, the overall questionnaire and each dimension exhibited Cronbach\u0026rsquo;s alpha coefficients of 0.847, 0.768, and 0.848, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eDepression, anxiety, and stress scale\u003c/h2\u003e \u003cp\u003eThis scale was used to assess the participants\u0026rsquo; psychological health. It has proven to be a reliable and effective measure for evaluating the mental well-being of the Chinese population [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The CFA indicated an acceptable fit of the scale: χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;3.513, RMSEA\u0026thinsp;=\u0026thinsp;0.052, CFI\u0026thinsp;=\u0026thinsp;0.957, TLI\u0026thinsp;=\u0026thinsp;0.951, and SRMR\u0026thinsp;=\u0026thinsp;0.027. Item scores were recorded using a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores indicating higher levels of depression, anxiety, and stress for individuals. The overall questionnaire and each dimension demonstrated Cronbach\u0026rsquo;s alpha coefficients of 0.898, 0.917, 0.895, and 0.889, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnglish version of the depression, anxiety, and stress scale\u003c/h2\u003e \u003cp\u003eThis scale, developed by Grant et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and Liu et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], comprised 20 self-assessment items and three subscales of Engagement Reflection, Motivation Reflection, and Insight. The CFA indicated that an acceptable fit of the scale: χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;5.727, RMSEA\u0026thinsp;=\u0026thinsp;0.060, CFI\u0026thinsp;=\u0026thinsp;0.940, TLI\u0026thinsp;=\u0026thinsp;0.932, and SRMR\u0026thinsp;=\u0026thinsp;0.071. Item scores were recorded using a 6-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 6\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores reflecting higher levels of self-reflection and insight for individuals. The overall questionnaire and each dimension demonstrated Cronbach\u0026rsquo;s alpha coefficients of 0.944, 0.954, and 0.893, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Reflection and Insight Scale\u003c/h2\u003e \u003cp\u003eThe Self-Reflection and Insight Scale (SRIS) in English version was developed by Grant et al., consisting of 20 self-assessment items. The scale comprises three subscales: Reflective Engagement, Motivation Reflection, and Insight. The CFA demonstrated the scale\u0026rsquo;s good fit: χ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;1.189, RMSEA\u0026thinsp;=\u0026thinsp;0.014, CFI\u0026thinsp;=\u0026thinsp;0.997, TLI\u0026thinsp;=\u0026thinsp;0.997, and SRMR\u0026thinsp;=\u0026thinsp;0.016. Items are scored using a Likert 6-point scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 6\u0026thinsp;=\u0026thinsp;Strongly Agree). Higher scores reflect higher levels of reflection and insight in individuals. In this study, the overall questionnaire and each dimension had Cronbach's α coefficients of 0.944, 0.954, and 0.893, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEmotion regulation questionnaire\u003c/h2\u003e \u003cp\u003eThis questionnaire, developed by Gross [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], comprised 10 items assessing two dimensions: Cognitive Reappraisal and Expressive Suppression. The CFA indicated that an acceptable fit of the scale: χ2/df\u0026thinsp;=\u0026thinsp;1.254, RMSEA\u0026thinsp;=\u0026thinsp;0.060, CFI\u0026thinsp;=\u0026thinsp;0.999, TLI\u0026thinsp;=\u0026thinsp;0.998, and SRMR\u0026thinsp;=\u0026thinsp;0.016. Item scores were recorded on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores indicating a higher frequency of employing emotion regulation strategies. The Chinese version of the questionnaire demonstrated robust reliability and validity. The Cronbach\u0026rsquo;s alpha coefficients for each dimension of the questionnaire were 0.917 and 0.923, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eUtilizing SPSS 22.0, we conducted the item, exploratory factor, internal consistency reliability, criterion-related validity, and test-retest reliability analyses. CFA was performed using MPLUS 8. Values below 0.05 were considered statistically significant. Furthermore, exact p values were reported to signify the level of significance in the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eEthical considerations played a crucial role throughout the research process, with meticulous measures to ensure the well-being and rights of the participants. The study received ethical approval from the Ethics Committee at Central China Normal University, highlighting a commitment to adhering to the established ethical guidelines and promoting transparency and credibility.\u003c/p\u003e \u003cp\u003e All participants provided written informed consent after receiving comprehensive information regarding the study\u0026rsquo;s purpose, procedures, potential risks, and benefits. The participants were assured of the voluntary nature of their participation and were given the freedom to withdraw from the study without facing any adverse consequences. Emphasizing the significance of informed consent demonstrates a dedication to respecting autonomy and upholding ethical standards.\u003c/p\u003e \u003cp\u003eMaintaining privacy and confidentiality remained of utmost importance throughout the research process. The participants were assured that their personal information would be handled with the highest level of confidentiality and used exclusively for the intended purposes of the study. Robust data anonymization techniques were applied to prevent the disclosure of any identifiable information in publications or reports stemming from the study, safeguarding the participants\u0026rsquo; anonymity and confidentiality.\u003c/p\u003e \u003cp\u003eTo mitigate potential harm, additional measures were taken to address the emotional well-being of the participants. Debriefing procedures were implemented. Furthermore, the participants were given access to support resources in case of any distress or discomfort arising from their participation in the study. Prioritizing the welfare of participants further strengthened the ethical underpinning of the research.\u003c/p\u003e \u003cp\u003e In summary, ethical considerations in this study encompassed securing informed consent, guaranteeing privacy and confidentiality, minimizing potential harm to participants, and adhering to the ethical guidelines established by the Ethics Committee at Central China Normal University. These stringent measures were implemented to uphold ethical standards, protect the participants\u0026rsquo; rights, and contribute to the overall integrity of the research process.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eItem analysis\u003c/h2\u003e \u003cp\u003eThe critical ratio method was utilized for the item analysis of the questionnaire. The CHIME-37 item scores were arranged in descending order, with the bottom 27% of participants categorized as the low-scoring group and the top 27% as the high-scoring group. We examined disparities between the two groups on each item. Independent sample t-tests were conducted for each item\u0026rsquo;s scores, revealing significant differences for all items (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, correlation analyses were performed between the scores of each item and the total score. The results indicated correlation coefficients ranging between 0.417 and 0.734, all statistically significant at 0.01 level. Further details are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe overall correlation and decision values for each item of the CHIME-37\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.005**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.603**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.577**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.578**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.710**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.513**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.451**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.432**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.896**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.540**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.550**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.504**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.869**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.501**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.971**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.551**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.434**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.575**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.669**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.598**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.681**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.561**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.022**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.556**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.494**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.552**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.953**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.539**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.648**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.629**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.981**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.584**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.051**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.554**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.221**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.612**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.141**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.534**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.053**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.385**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.309**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.590**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.269**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.538**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.340**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.650**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.448**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.561**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.842**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.591**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.350**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.422**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.734**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.514**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.038**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.608**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.387**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.604**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.345**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.525**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.159**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.612**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.450**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.542**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.600**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.556**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.524**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.541**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.382**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.393**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.595**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.284**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.621**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStructural validity\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eExploratory factor analysis\u003c/h2\u003e \u003cp\u003eThe Chinese version of CHIME-37 underwent EFA using data from Group 1 (n\u0026thinsp;=\u0026thinsp;838). EFA was employed with principal component analysis (PCA) and varimax rotation to assess conformity levels and assign names to the extracted factors in the internal structure of CHIME among Chinese college students. The Kaiser\u0026ndash;Meyer\u0026ndash;Olkin value was 0.940, and the Bartlett sphericity test yielded χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;17700.459 (df\u0026thinsp;=\u0026thinsp;666, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating the suitability of the data for EFA. Factor extraction retained factors with eigenvalues greater than 1, resulting in a cumulative variance contribution of 70.696%. This reflects substantial explanatory power of the factors, preserving the original data information comprehensively. The variance percentage of the first factor was 30.754, which is less than 40%, suggesting the absence of severe common method bias. Moreover, the analysis of the scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) led to the decision to extract eight factors. Although the 7-factor and 8-factor models demonstrated good to excellent fits, model fit indices indicated a superior fit for the 8-factor model. The chi-square difference test was significant, supporting the adoption of the more parsimonious (8-factor) model, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Factor loadings of the items illustrated a better conceptual fit with the 8-factor model. The factor loadings and communality for the 37 items are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In line with the nomenclature proposed by Bergomi et al., these eight factors were named as follows: Awareness of Inner Experience, Awareness of Outer Experience, Acting with Awareness, Acceptance and Non-Judgment, Decentering and Non-Reactivity, Openness to Experience, Relativity of Thoughts, and Insightful Understanding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory Factor Analyses - Model Fit Comparisons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModels compared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8319.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7126.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1-factor against 2-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1193.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5305.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2-factor against 3-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1821.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5004.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3-factor against 4-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e300.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4230.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4-factor against 5-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e773.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3648.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5-factor against 6-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e582.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2164.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6-factor against 7-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1483.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1052.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7-factor against 8-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1111.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. RMSEA root-mean-square error of approximation; CFI comparative fit index; TLI Tucker\u0026ndash;Lewis index. All p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory Factor Analysis of CHIME-37 With Item-Factor Loadings and Proportion of communality (N\u0026thinsp;=\u0026thinsp;838)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eFactor loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecommunality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF1: Awareness of Inner Experience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF2: Awareness of Outer Experience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF3: Awareness of Outer Experience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF4: Accepting and Nonjudgmental Attitude\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF5: Non-Reactivity and Decentering\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF6: Openness to Experience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF7: Relativity of Thoughts and Reality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eF8: Insightful Understanding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory factor analysis\u003c/h2\u003e \u003cp\u003eCFA was performed on the structure of the Adolescent Autonomy Questionnaire using Sample 2 (n\u0026thinsp;=\u0026thinsp;947).\u003c/p\u003e \u003cp\u003eInitially, we established an 8-factor model based on the factor structure of the original questionnaire and conducted structural validation for the Chinese version to assess its applicability. The original factor structure included the following: ① Awareness of Inner Experience (items 1, 5, 14, 29, and 34), ② Awareness of External Experience (items 9, 18, 21, and 27), ③ Acting with Awareness (items 10, 12, 17, 26, and 38), ④ Acceptance and nonjudgmental attitude (items 2, 7, 32, and 36), ⑤ Non-Reactivity and Decentering (items 8, 13, 16, 20, 25, and 28), ⑥ Openness to Experience (items 19, 30, 33, and 22), ⑦ Relativity of Thoughts and Reality (items 4, 23, 31, and 35), and ⑧ Insightful Understanding (items 3, 6, 15, 24, and 37). The results of the CFA revealed that the fit indices for the 8-factor model were χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.751, CFI\u0026thinsp;=\u0026thinsp;0.981, TLI\u0026thinsp;=\u0026thinsp;0.979, SRMR\u0026thinsp;=\u0026thinsp;0.027, and RMSEA\u0026thinsp;=\u0026thinsp;0.028, indicating a relatively good fit. This finding suggests that the original factor structure is also applicable to the Chinese version of the questionnaire. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts standardized parameters of the 8-factor model of the CHIME-37.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eInternal validity analysis\u003c/h2\u003e \u003cp\u003eA CFA of the CHIME questionnaire was conducted. Internal validity indicators, inter-dimension correlations, and convergent validity were computed. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the detailed information. The analysis revealed significant correlations between CHIME and its dimensions, with Pearson correlation coefficients ranging between 0.302 and 0.704. All pairwise correlations between CHIME dimensions were statistically significant, with the average variance extracted (AVE) value exceeding 0.5 for each dimension. This finding suggests that the CHIME questionnaire effectively captures various facets of mindfulness, demonstrating robust internal discriminant validity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Coefficients and AVE Values Among Dimensions of the Chinese Version of CHIME-37\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1: Awarelnt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2: AwareExt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.689**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3: ActAware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.630**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.560**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4: AccNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.680**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.675**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5: DecNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.765**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.706**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.684**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6: Openness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.434**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.370**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.328**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.391**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7: Relativity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.704**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.579**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.567**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.565**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.633**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.427**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8: Insight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.650**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.589**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.566**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.682**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.312**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.566**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, The average variance extracted (AVE) values are provided on the diagonal.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCriterion-related validity analysis\u003c/h2\u003e \u003cp\u003eWe conducted Pearson correlation analyses to examine the relations between the total score and each dimension of CHIME and scores on various factors. These factors included subjective well-being, psychological well-being, mental tranquility, physical and mental health, depression-anxiety-stress, self-reflection and insight, cognitive reappraisal, and expressive suppression. The total mindfulness experience score and its dimensions exhibited significant positive correlations with subjective well-being, psychological well-being, mental tranquility, and cognitive reappraisal (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, the total mindfulness experience score and its dimensions exhibited negative correlations with physical and mental health, depression-anxiety-stress, and expressive suppression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Refer to Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for details).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the Calibration-Related Validity of CHIME-37\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwarelnt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAwareExt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActAware\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccNJ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecNR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRelativity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInsight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCHIME\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective well-being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.288**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.291**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.296**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.330**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.246**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.256**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.322**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.380**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological well-being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.419**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.400**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.407**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.429**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.291**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.338**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.384**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.508**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeace of mind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.439**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.381**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.365**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.412**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.167**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.368**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.325**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.470**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.230**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.176**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.237**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.299**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.215**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.238**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.221**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.190**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.293**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.214**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.145**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.257**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.267**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.208**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.193**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.166**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.159**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.264**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychosomatic illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.242**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.173**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.271**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.307**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.231**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.233**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.208**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.189**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.303**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.437**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.454**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.420**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.443**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.296**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.385**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.373**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.526**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.376**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.373**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.416**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.390**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.388**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.265**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.313**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.338**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.472**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.341**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.331**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.418**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.400**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.251**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.329**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.362**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.463**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression-anxiety-stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.532**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.496**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.592**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.556**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.557**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.374**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.474**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.494**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.673**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reflection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.364**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.413**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.244**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.316**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.315**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.455**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.323**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.283**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.351**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.348**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.337**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.253**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.276**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.310**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.409**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reflection and insight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.407**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.365**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.409**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.418**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.440**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.281**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.344**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.356**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.499**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive reappraisal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.402**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.365**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.337**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.339**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.383**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.284**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.312**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.295**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.448**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpression inhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.493**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.426**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.498**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.436**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.505**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.354**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.417**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.463**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.594**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eReliability analysis\u003c/h2\u003e \u003cp\u003eThe overall Cronbach\u0026rsquo;s α coefficient for the questionnaire was 0.961, with individual dimensions ranging between 0.883 and 0.961. The overall test-retest reliability coefficient of the questionnaire was 0.840 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and test-retest reliability coefficients for individual dimensions ranged between 0.746** and 0.732** (Ps\u0026thinsp;\u0026lt;\u0026thinsp;0.01). When dividing the questionnaire into two equal halves based on item numbers and correlating the scores of the two halves, the split-half reliability for the overall questionnaire was 0.871, with individual dimensions ranging between 0.795 and 0.919 (Refer to Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for the results of the reliability analysis).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics, Test-Retest Reliability, Split-Half Reliability, and Internal Consistency Reliability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRetest reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSplit-half reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwarelnt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.48\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.746**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.853**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareExt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.919**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActAware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.885**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.856**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.840**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e26.13\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.898**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.861**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.891**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelativity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.856**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.871**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e23.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.886**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.795**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHIME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e167.28\u0026thinsp;\u0026plusmn;\u0026thinsp;30.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.840**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement invariance test\u003c/h2\u003e \u003cp\u003eFollowing the recommendation of Cheng and Rensvold (2002) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], we employed ΔCFI\u0026thinsp;\u0026le;\u0026thinsp;0.01 and ΔRMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.015 as criteria for assessing invariance. The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, indicate that the metric and scalar invariance hypotheses hold true within the gender subgroup. In the subgroup based on the extent of mindfulness practice, the metric and scalar invariance hypotheses were confirmed. These findings suggest that the metric and scalar invariance hypotheses are supported within the identified subgroups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement invariance test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA (90CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e△CFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e△RMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1638.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028 (0.024,0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1658.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027 (0.024,0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1684.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027 (0.023,0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMindfulness Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1682.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029 (0.026,0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1710.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029 (0.025,0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1748.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029 (0.025,0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate, for the first time, the suitability of a multifactorial mindfulness measurement for Chinese university students. Following standard procedures, the CHIME questionnaire was translated into Chinese and administered to Chinese university students to assess CHIME-37. The results supported the 8-factor, 37-item mindfulness measurement tool (CHIME-37) among Chinese university students. The results demonstrated excellent model fit indices and good internal consistency for the total score and eight subscales.\u003c/p\u003e \u003cp\u003eDifferentiation among the eight dimensions is a crucial characteristic of the CHIME. First, the AVE values for each of the eight dimensions exceeded 0.5, indicating good discriminant validity among the internal dimensions of the Chinese version of CHIME. This finding suggests that the eight skills and abilities of mindfulness are distinguishable from each other. Second, the Pearson correlation coefficients among the eight dimensions ranged between 0.3 and 0.7. This finding indicates that these dimensions are correlated but not overlapping. This finding aligns with the theoretical construction of mindfulness, which posits eight distinct components or skills. This has significant value for the comprehensive and accurate measurement of mindfulness levels and the assessment of the completeness and quality of mindfulness programs. Furthermore, mindfulness demonstrated moderate correlations with criterion-related validity. Thus, CHIME plays a strong predictive role in subjective well-being, psychological well-being, emotion regulation, insight, anxiety, depression, perceived stress, and overall health. Notably, the correlation between mindfulness and PWB was significantly higher than that between mindfulness and subjective well-being (SWLS, hedonic well-being). This finding confirms that mindfulness is strongly connected with aspects related to self-actualization, such as autonomy, personal growth, life goals [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], self-acceptance, environmental mastery, and positive relationships with others [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This alignment with Eastern cultural values emphasizes the realization of human potential, as opposed to the Western emphasis on individualistic hedonic well-being. Reflection and insight, considered crucial traits, skills, or abilities in Eastern cultures, are well represented in the Chinese version of CHIME (factors 7 and 8). Moreover, the strong correlation of mindfulness with reflection and insight provides excellent validation. Therefore, the revised mindfulness experience questionnaire, CHIME, is suitable for the current cultural context in China.\u003c/p\u003e \u003cp\u003eThe refined Chinese version of CHIME affirms that mindfulness can be categorized into three groups: awareness (encompassing the basic dimensions of mindfulness), behavioral regulation (encompassing mindful actions and non-reactivity to inner experiences), and evaluative wisdom (representing the most advanced mindfulness skills) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As per the theoretical framework of the CHIME Scale, mindfulness comprises eight components or skills, further categorized into 3 groups. Specifically, Factor 1, Internal Awareness, and Factor 2, External Awareness, signify the foundational skills of mindfulness. Cultivating these skills may enhance individuals\u0026rsquo; focus on present experiences and actions, facilitating a shift from automatic navigation to an existence mode and enabling more skillful responses to challenges. Factor 3, Mindful Actions, and Factor 4, Acceptance and Non-Judgment, represent intermediate skills in behavioral regulation within mindfulness. Practicing mindfulness in daily life can result in improved focus and executive functioning for individuals. In the face of failure and setbacks, individuals can engage in self-care, allowing themselves space for acceptance and self-compassion and maintaining a rational mindset, emotions, and behaviors. Factor 5, Decentering and Non-Reactivity, and Factor 6, Openness to Experiences, signify intermediate skills in emotional regulation within mindfulness. When confronted with painful emotions, sensations, experiences, and thoughts, individuals can release attachments, avoid struggling, surrender without resistance, and uphold an open awareness or meta-awareness without evading pain. Factors 7, Relativity of Thoughts and Reality, and Factor 8, Insightful Thoughts, signify high-level skills or abilities in the cognitive defusion of mindfulness wisdom. By employing metacognitive patterns, individuals can overcome negative thoughts, emotions, and the cognitive fusion of needs, becoming aware of life\u0026rsquo;s imperfections, impermanence, and the concept of non-self. This transcendent practice enhances wisdom and insight into the impermanence of life. Furthermore, our research results validate that the Chinese version of CHIME aligns with the four effective mechanisms of mindfulness: attention regulation, body awareness, emotion regulation, and a change in self-perspective. Through the synergistic action of these four mechanisms, individuals\u0026rsquo; self-regulatory abilities are enhanced [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the analysis of criterion-related validity, the Chinese version of CHIME exhibited significant positive correlations with subjective well-being, psychological well-being, emotional tranquility, self-reflection, and insight but showed negative correlations with physical and mental health issues, depression-anxiety-stress, and expressive inhibition. These findings suggest that mindfulness skills and abilities are centered on redirecting individuals\u0026rsquo; attention to the direct experience of their body, mind, emotions, sensations, and thoughts. This process helps prevent students from comparing themselves in terms of achievements or other aspects, fostering increased happiness, inner peace, enhanced reflection and insight, and improved cognitive regulation abilities among them. Simultaneously, mindfulness skills and abilities can alleviate physical and mental suffering, reduce perceived stress levels, and minimize emotional suppression. A decline in mindfulness may be associated with a decrease in physical health, psychological distress, and emotional imbalance. Mindfulness serves as a positive psychological resource and trait for college students, aiding in their adjustment of body and mind, emotion regulation, increased happiness, and the enhancement of wisdom. Furthermore, the early adulthood phase in college represents a peak period for wisdom development [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Mindfulness may contribute to the enhancement of wisdom and insight, providing individuals with additional resources to cope with academic, life, and societal challenges.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eImplications for research and practice\u003c/h2\u003e \u003cp\u003eThe eight factors of mindfulness correspond to eight skills or abilities associated with mindfulness, positively contributing to enhancing college students\u0026rsquo; psychological resilience, mental qualities, emotional regulation, and wisdom. This offers valuable insights for improving mental health among college students. Generally, these eight skills and abilities of mindfulness are developed progressively, beginning with basic skills and advancing to intermediate and advanced skills. Factors 1 and 2 signify the fundamental dimensions of mindfulness (internal sensations), encompassing basic mindfulness skills. Individuals can nurture the ability to stay connected with their inner and outer experiences through body scanning and breath observation. Factors 3 and 4 can be enhanced through mindful walking, stretching, sitting meditation (non-selective awareness practices), and mindful dishwashing. Factors 5 and 6 foster psychological resilience, emotional acceptance, and adaptability to present moment experiences. Meditation on \u0026ldquo;recognizing aversion\u0026rdquo; and the three-minute breath space exercise can contribute to these skills. Factors 7 and 8 help develop individuals\u0026rsquo; ability to maintain clear cognition, benevolence, and take wise and insightful actions following life\u0026rsquo;s challenges. Mindfulness meditation (recognizing the relativity of thoughts and reality) and compassionate meditation can contribute to developing these skills. For college students engaging in mindfulness practices, beginners can start with basic exercises and progressively move on to intermediate and advanced practices. After establishing a solid foundation in basic skills, individuals can gradually explore more advanced practices, following a step-by-step approach starting from easy tasks and then switching to challenging tasks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future research\u003c/h2\u003e \u003cp\u003eFirst, the participants were exclusively recruited from four universities in the southern, central, and western regions of China, potentially limiting the generalizability of the results to universities across the country. Future research could address this limitation by seeking a more representative sample from various regions nationwide, ensuring a broader representation, particularly from the eastern and northern parts of China. Including students from diverse geographical areas would enhance the extrapolation of research results to the entire population of Chinese university students.\u003c/p\u003e \u003cp\u003eSecond, the reliance on self-report measures as the sole assessment tool is another limitation. To overcome this, future research should use diverse measurement methods, such as objective observations, interviews with teachers, parents, or school staff, or the integration of standardized assessments. The incorporation of multiple measurement approaches would enable researchers to conduct a more comprehensive and thorough evaluation of mindfulness in Chinese university students, mitigating potential measurement biases and offering a more accurate depiction.\u003c/p\u003e \u003cp\u003eThird, the study\u0026rsquo;s cross-sectional design restricts the ability to infer causality from the presented findings. The study focused on a non-clinical sample of university students. Replicating these findings to a clinical population of university students is highly recommended to investigate the relation between mindfulness and psychopathological indicators in university students. Given these limitations, several recommendations emerge for future research. First, efforts should be made to secure a more representative sample of university students from diverse regions of China to enhance the generalizability of survey results. Alongside self-report measures, incorporating diverse assessment methods, such as objective observations and standardized assessments, can provide a more holistic assessment of mindfulness levels and abilities in university students. Emphasis should be placed on conducting longitudinal studies covering multiple academic years to capture the dynamic nature of mindfulness levels and abilities in Chinese university students.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe Chinese adaptation of the CHIME demonstrates robust psychometric properties and is well-suited for assessing mindfulness in Chinese college students. Moreover, engaging Chinese college students in mindfulness practices may be beneficial for them, contributing to the enhancement of their physical and mental well-being. Mindfulness practice is shown to be effective in regulating emotions, fostering a heightened sense of happiness, and promoting overall wisdom among Chinese college students.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZD contributed to the conceptualization and design of the study, conducted data collection, analysis, and interpretation. ZD played a significant role in drafting the original manuscript and participated in reviewing, editing, and revising its content. ZD provided final approval of the manuscript, ensuring its integrity, and assumed public responsibility for the work.SJB was involved in the conceptualization and design, translation, and polishing of the study. SJB contributed valuable insights and suggestions, ensuring the accuracy and clarity of the paper\u0026apos;s presentation. SJB also participated in drafting and revising important intellectual content to maintain the manuscript\u0026apos;s quality and accuracy.MHY was responsible for the conceptualization and design of the study, as well as its revision, validation, and supervision. MHY provided valuable guidance and direction throughout the research process, ensuring the quality and accuracy of the manuscript. MHY also supervised the validation process, ensuring the credibility and effectiveness of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge all the participants of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee for Psychological Research of the corresponding author\u0026rsquo;s institution (Central China Normal University) .Participants provided written informed consent, emphasizing autonomy and ethical standards. \u0026nbsp; Privacy and confidentiality were maintained, and robust data anonymization techniques were applied. \u0026nbsp; Additional measures addressed emotional well-being, including debriefing procedures and access to support resources. \u0026nbsp; Overall, stringent ethical measures were implemented to uphold standards, protect rights, and maintain research integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMalinowski P. 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New well-being measures: Short scales to assess flourishing and positive and negative feelings. Soc Indic Res. 2010;97:143-56. doi:10.1007/s11205-009-9493-y\u003c/li\u003e\n\u003cli\u003eDuan W, Xie D. Measuring adolescent flourishing: psychometric properties of flourishing scale in a sample of Chinese adolescents. J Psychoeduc Assess. 2019;37(1):131-5. doi:10.1177/0734282916655504\u003c/li\u003e\n\u003cli\u003eLee YC, Lin YC, Huang CL, Fredrickson BL. The construct and measurement of peace of mind. J Happiness Stud. 2013;14:571-90. doi:10.1007/s10902-012-9343-5\u003c/li\u003e\n\u003cli\u003eAndreasson A, Wicksell RK, Lodin K, Karshikoff B, Axelsson J, Lekander M. A global measure of sickness behaviour: Development of the sickness questionnaire. J Health Psychol. 2018;23(11):1452-63. doi:10.1177/1359105316659917\u003c/li\u003e\n\u003cli\u003eTang X, Guan Q, Duan W. Sickness Questionnaire: A two-factor instrument reflecting physical and mental symptoms in the Chinese context. 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New York: Cambridge University Press; 2005. p. 160-88.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mindfulness, The Chinese Comprehensive Inventory of Mindfulness Experiences, Factor structure validation","lastPublishedDoi":"10.21203/rs.3.rs-3938635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3938635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Owing to the lack of a precise and comprehensive mindfulness measurement tool capable of capturing all facets of mindfulness, developing such an assessment tool has become an intriguing and worthwhile area of exploration. This study investigates the applicability of a multifactor mindfulness scale to Chinese college students. In particular, it tests the applicability of the Chinese version of the Comprehensive Inventory of Mindfulness Experiences (CHIME) in college students.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Prior to the formal test, 410 subjects completed the CHIME-37. The feedback received from this pretest was used to obtain the final descriptions. During the formal assessment, 1927 subjects participated, and 490 students were retested two months later. The criteria-related validity of the CHIME-37 was assessed using instruments such as the subjective well-being scale, psychological well-being scale, peace of mind scale, self-reflection and insight scale, emotion regulation scale, depression-anxiety-stress scale, and sickness questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The sample was randomly divided into two halves. In the exploratory factor analysis (EFA) of Sample 1 (n = 838), CHIME comprised 8 factors: 1) Awareness of internal experiences, 2) Awareness of external experiences, 3) Mindful action, 4) Acceptance and non-judgment, 5) Decentering and non-reactivity, 6) Experiential openness, 7) Relativity of thoughts and reality, and 8) Insightful understanding. The cumulative variance accounted for 70.696%. Confirmatory factor, criterion-related validity, and internal consistency analyses were conducted on the randomly split 947 samples for validation. Confirmatory factor analysis of Sample 2 confirmed the 8-factor model (x\u003csup\u003e2\u003c/sup\u003e/df = 1.751, CFI = 0.981, TLI = 0.979, RMSEA = 0.028). The internal consistency coefficients of the eight dimensions range from 0.848 to 0.914, with test-retest reliabilities ranging from 0.746 to 0.885, and split-half reliabilities ranging from 0.795 to 0.898. Total scores and scores on the eight dimensions are significantly positively correlated with subjective well-being, psychological well-being, emotion stability, and cognitive reappraisal (P \u0026lt; 0.01), while they are negatively correlated with physical and mental illnesses, depression-anxiety-stress, and expressive inhibition (P \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The revised version of the CHIME demonstrates robust reliability and validity, establishing it as a suitable tool for measuring the mindfulness levels of Chinese college students.\u003c/p\u003e","manuscriptTitle":"Exploring the Applicability of a Multifactor Mindfulness Scale in Chinese College Context","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 17:24:05","doi":"10.21203/rs.3.rs-3938635/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0b6ac06-faa2-4117-9aa3-656965c339ad","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-02T11:42:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 17:24:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3938635","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3938635","identity":"rs-3938635","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}