Measuring Balint Group learning effects in medical students: A validation of the Balint Group Questionnaire for Medical Students in English

Measuring Balint Group learning effects in medical students: A validation of the Balint Group Questionnaire for Medical Students in English | 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 Measuring Balint Group learning effects in medical students: A validation of the Balint Group Questionnaire for Medical Students in English Roshni Bahri, Abdou Mohamed, Sian Davies, Azjad Elmubarak, Simon Heyland, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7687973/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Balint groups are an established method for strengthening doctor-patient relationships and building professional resilience, particularly in undergraduate medical education. Despite their increasing use, a validated English-language tool to measure the specific learning outcomes for medical students has been conspicuous by its absence. This study sought to validate an English translation of the German Balint Group Questionnaire (BGQ-E) for use within this population. Methods A cross-sectional validation study was undertaken involving 349 fourth-year medical students at the University of Birmingham. After their Balint group sessions, students completed the 15-item BGQ-E. To examine the instrument's psychometric properties and factor structure, a suite of analyses was conducted, including descriptive statistics, reliability analysis, as well as exploratory and confirmatory factor analysis (CFA). The CFA was specifically used to test various competing models, including two-factor, three-factor, and second-order factor structures. Results The BGQ-E demonstrated excellent internal consistency, with a Cronbach's alpha of 0.93. While the initial analysis pointed towards a three-factor structure, the CFA revealed that the factors were too highly correlated, indicating a lack of discriminant validity. The model that provided the best fit was a second-order factor model with a dominant general factor, which has been termed "Balint Process and Learning," and two distinct, albeit related, subscales: "Individual Learning" and "Group Learning". All items loaded significantly onto their respective factors, and both subscales showed strong internal consistency (alpha values of 0.92 and 0.79, respectively). Conclusion The BGQ-E is the first validated English-language tool for measuring the effects of Balint groups in a medical student population. It is a reliable instrument with a robust underlying structure. For research and evaluation, it is recommended to use the total score as the primary measure, with the two subscales providing valuable supplementary insight into the dynamics of individual versus group learning. Future research should focus on longitudinal validation and testing this tool in diverse healthcare populations. Figures Figure 1 Figure 2 Background Originating in the 1950s from psychological training seminars held by psychoanalyst Michael Balint and his wife, Enid, for general practitioners in London, Balint groups were first described in the seminal work, The Doctor, his Patient and the Illness (1957) 1 . The Balint method eschews didactic lectures for a collaborative, psychoanalytically led format. In these small, confidential groups, a clinician presents a case, and the ensuing discussion focuses not on finding a diagnosis or treatment plan, but on understanding the complex emotional and relational dynamics between the practitioner and the patient. This process encourages clinicians to become better listeners and to reflect on their own feelings and responses within the clinical encounter. The continuity of these groups, which historically met weekly over several years, fosters a safe environment for open discussion and allows for the long-term follow-up of patient progress. Since its inception, the Balint group model has been adopted worldwide, leading to the establishment of national Balint Societies in over 22 countries dedicated to fostering this unique approach to reflective medical practice 2 . These groups aim to enhance self-awareness and deepen the understanding of the doctor-patient relationship. Balint groups are a mandatory part of psychiatric training in UK 3 and are increasingly being used in other medical specialities 4 . Given that nurturing empathy and reflective practice have been embedded in undergraduate medical education, the implementation of Balint Groups for medical students has been increasing 5 . Compelling evidence from a randomized controlled trial involving medical residents established that Balint groups were an effective intervention for preventing burnout 6 . The generalisability of these findings is supported by similar results in other high-stress clinical environments, with studies showing positive effects on the quality of work life for professionals such as intensive care nurses 7 . Furthermore, longitudinal research suggests a lasting impact on professional satisfaction, as general practitioners who engage in Balint training report "thriving better" in their roles, indicating a positive influence on both job satisfaction and professional resilience 8 . Moreover, a systematic review and meta-analysis revealed that participation in Balint groups led to a significant increase in empathy scores compared to control groups, with a standardised mean difference (SMD) of 0.71 among medical students 9 . This finding is particularly pertinent given the documented decline in empathy levels during medical training 10 . Various medical schools in the United Kingdom have implemented Balint Groups as part of their psychiatry curriculum, and with 400 students per cohort, our scheme at the University of Birmingham is one of the biggest in the country 11 , 12 . Despite the recognised benefits of Balint groups, there exists a notable gap in the availability of validated instruments to assess the learning outcomes derived from these sessions, especially among medical students. The German Balint Group Questionnaire 13 , 14 is a validated 17 step questionnaire to evaluate distinct processes involved in Balint Groups, such as emotional and cognitive learning, mirroring, and transference. While a Mandarin version of the Balint Group Questionnaire (BGQ) has been validated in China 15 , an English version. Furthermore, there is no existing questionnaire that has been validated for medical students. This absence hampers the ability to systematically evaluate and compare the effectiveness of Balint groups across diverse educational settings. Traditionally, studies have relied on general empathy scales, such as the Jefferson Scale of Physician Empathy 16 , to measure the impact of Balint groups. However, these tools may not fully capture the unique reflective and relational dynamics that are being developed within Balint sessions. The development and validation of an English version of the BGQ specifically for medical students would provide a more nuanced and accurate assessment of the educational value of Balint groups. Additionally, it will also help us understand whether the process of Balint is well translated across to medical students. This study aims to bridge this gap by validating the English translation of the BGQ for use among medical students. By doing so, it seeks to enhance the tools available for evaluating the reflective learning processes integral to Balint groups and to support the integration of such groups into medical curricula. Methods Study design and setting This study was undertaken at the Birmingham and Solihull Mental Health and NHS Foundation Trust Teaching Academy and employed a cross-sectional validation design. It took place between September 2024 and March 2025, across five consecutive rotations in the academic year. Participants The target population consisted of fourth- year medical students from the University of Birmingham, United Kingdom. Participation in the Balint Groups was a mandatory component of their psychiatry rotation. A total of 400 students, spread roughly across five rotations, participated in the Balint groups. Within each rotation, 8 Balint groups ran concurrently. Balint group intervention Each student attended a maximum of four Balint group sessions over the course of their five-week psychiatry placement. For the large majority of students this was their first exposure to the Balint method - a deliberate choice to capture first impressions and learning gains that are not impacted by prior experiences. Each group comprised 10 to 12 students. Groups were facilitated by facilitators ranging in Balint experience, but all having been trained in the process. Questionnaire Students were invited to voluntarily complete the English version of BGQ-G at the end of their rotation. All participants provided informed consent prior to data collection. Students completed the forms anonymously to reduce social desirability bias and encourage candid responses. The translated version of the BGQ-G 13,14 was obtained via email from the original authors of the BGQ, Prof. Guido Flatten. The BGQ-G 13,14 aims to capture key intrapersonal learning processes and perceived group dynamics inherent to Balint group work. Theoretically, the items were designed to reflect three dimensions identified in previous research: (1) Reflection of transference dynamics in the doctor-patient relationship, (2) Emotional and cognitive learning, and (3) Case mirroring within the dynamic of the group 14 . Participants responded to each of the 15 statements using a 5-point Likert-type scale, ranging from 0 (“Strongly Disagree) to 5 (“Strongly Agree"). Higher scores indicate greater subjective endorsement of the learning process described in the item. No items required reverse scoring. The full list of questionnaire items is available in Appendix A. There are some key differences in the English version of the BGQ as compared to the original German version. The German BGQ comprises of 17 items, with 12 items being divided into three domains (Table 1 ). However, the English translation of the BGQ (Appendix A), contains 15 questions, with questions 1 and 8 of the German BGQ missing. The missing questions 1 and 8 do not come under any factor in the German BGQ scales, so therefore should not impede the calculation of scores based on domains. However, a limited factor exploration based on the 3 previously named factors and an exploratory factor analysis will be undertaken to explore whether the missing questions in the English BGQ impact the 3 domains that the BGQ tests. The updated questions used in the English BGQ are in Table 2 . Table 1 Domains in German BGQ Factor Items Interpretation Factor 1: Emotional & Cognitive Learning Items 5, 6, 9, 11, 14 New knowledge and emotional processing from case discussions. Factor 2: Reflection of Transference Dynamics Items 2, 3, 10, 13, 15, 16 Awareness of unconscious influences on doctor-patient interactions. Factor 3: Case Mirroring in Group Dynamics Items 4, 7, 12 How patient case dynamics are reflected in group interactions. Table 2 Corresponding questions in BGQ-E Thus, a total of 15 items can be assigned to the three following domains (Table 3 ). German BGQ questions Corresponding question on English BGQ 1 Missing 2 1 3 2 4 3 5 4 6 5 7 6 8 Missing 9 7 10 8 11 9 12 10 13 11 14 12 15 13 16 14 17 15 Table 3 Reorganised questions per domain in English BGQ. Domain 1 Reflection of transference dynamics in the doctor-patient relationship Questions 1, 8, 11, 13, 14 Domain 2 Emotional and Cognitive learning Questions 4, 5, 7, 9 Domain 3 Case mirroring in the dynamic of the group Questions 3, 6, 10 Ethical considerations The study was reviewed and approved by the University of Birmingham's Medical School Ethics Committee. Informed consent was obtained from all participants, with assurances of voluntary participation, anonymity, and the right to withdraw without academic penalty. Statistical methods All statistical analyses were performed using R version 4.4.2 (2024-10-31) and relevant packages, including readxl 16 , dplyr 17 , psych 18) , and lavaan 19 . An alpha level of 0.05 was adopted for all tests of statistical significance. Descriptive statistics (means, standard deviations, medians, skewness, kurtosis, Cronbach’s alpha) were computed for all 15 BGQ items. The three-factor structure found in the German BGQ was tested within our sample. Reliability coefficients ≥ .70 were considered acceptable 20 . To investigate the underlying empirical factor structure of the 15-item BGQ within the study sample ( N = 349), an exploratory factor analysis (EFA) was conducted. The number of factors to retain was primarily determined by Horn's Parallel Analysis 21 , supplemented by the Kaiser criterion (eigenvalues > 1) and scree plot inspection 22 . An oblique rotation (Oblimin) was applied, allowing factors to correlate. A comparative EFA forcing a two-factor solution was also conducted to examine a potentially more parsimonious structure suggested by some retention criteria. Factor loadings ≥ .40 were considered salient 23 . Confirmatory factor analysis (CFA) was utilised via the lavaan package 18 to evaluate competing factor structures identified during the EFA phase. Models were estimated using the Weighted Least Squares Mean and Variance (WLSMV) estimator, treating items as ordered categorical variables 24 . Based on the EFA results, the following candidate models were specified a priori for evaluation: (a) a correlated two-factor model, (b) a correlated three-factor model, and (c) a second-order factor model testing a hierarchical structure with three first-order factors loading onto one general factor. Model adequacy was evaluated using goodness-of-fit indices: the scaled chi-square statistic (χ²), Comparative Fit Index (CFI; ≥ .95 excellent), Tucker-Lewis Index (TLI; ≥ .95 excellent), Root Mean Square Error of Approximation (RMSEA; ≤ .06 good, ≤ .08 acceptable), and Standardized Root Mean Square Residual (SRMR; ≤ .08 good) 25 . The final model was selected based on statistical fit, theoretical interpretability, and parsimony. Given that the primary goal was to test specific a priori models against one another using CFA, and to maximize statistical power for these model comparisons, all analyses were conducted on the full sample. Results Participant characteristics Out of 400 medical students, 353 gave informed consent to participate in the study and filled out the BGQ-E. 4 were excluded due to missing data, leaving a total cohort of 349 students. All students are 4th year medical students from the University of Birmingham. Item distribution The frequency distributions of all BGQ-E questions were not normally distributed, with some negatively skewed distributions with mild to moderate ceiling effects (Table 4 ). Questions 1, 6, 10 and 11 showed clear ceiling effects. All item distributions were unimodal, with modal values of 4. The mean values show a high agreement of the participants on average. The present ceiling effects limit the differentiability of the items, although the item selectivities were still within the statistically acceptable range. Due to the data not following normal distribution, methods for ordinally scaled variables with ordered categories had to be used for inferential statistical analysis. Table 4 Descriptive statistics for BGQ-E items Item N Mean SD Median Skewness Kurtosis Q1 351 3.68 1.03 4 -1.06 1.70 Q2 351 3.39 1.22 4 -0.66 -0.21 Q3 351 3.26 1.30 3 -0.64 -0.16 Q4 350 2.23 1.60 2 -0.02 -1.16 Q5 351 3.20 1.43 4 -0.57 -0.54 Q6 351 3.72 1.22 4 -1.10 0.93 Q7 351 3.26 1.27 3 -0.65 -0.06 Q8 351 3.13 1.24 3 -0.45 -0.25 Q9 351 3.34 1.27 4 -0.75 0.04 Q10 350 3.61 1.15 4 -0.92 0.95 Q11 351 3.60 1.31 4 -1.04 0.57 Q12 351 2.66 1.47 3 -0.22 -0.87 Q13 351 3.18 1.27 3 -0.58 -0.14 Q14 351 3.29 1.23 3 -0.64 0.09 Q15 351 3.41 1.33 4 -0.82 0.06 Reliability analysis The full 15-item BGQ demonstrated excellent internal consistency (Cronbach's α = 0.93). Reliability estimates for the three theoretically proposed subscales 14 were good for the Transference Dynamics (α = 0.84) and Emotional/Cognitive Learning subscales (α = 0.80), and acceptable for Case Mirroring (α = 0.69). However, as the factor structure was revisited empirically, reliability values for revised subscales are presented in later in this section. Exploratory factor analysis (EFA) The suitability of the 15-item data for factor analysis ( N = 349) was confirmed. Bartlett's test of sphericity was significant (χ²(105) = 2961.19, p 1) and the scree plot (Fig. 1 ) both suggested a two-factor structure. Prioritising the a priori 3- factor model used by Flatten et al 14, a 3-factor solution was also extracted and examined. The initial three-factor EFA solution accounted for 60.7% of the total item variance and revealed three weak but interpretable factors (Table 5 ). Factor names were decided based on authors’ deliberation on the theoretical clustering of questions: Factor 1: Broad Insight/Transference Dimension, Factor 2: Reflected Group Process/Mirroring Factor 3: Non-Specific Learning Aspects However, cross-loadings were notable for Q6 and Q11 (> 0.40), and item Q7 also showed complexity. Moreover, the factors were highly correlated (r = 0.44 to 0.68), suggesting substantial overlap in the underlying constructs, proving this model to not be the best fit, at least, in our population. Table 5 EFA Pattern Matrix Loadings for 3-Factor Solution (Oblimin Rotation) Item Factor 1 (Insight/Transf.) Factor 2 (Group process) Factor 3 (Learning) Q1 .429 Q2 .682 Q3 .746 Q4 .408 Q5 .656 Q6 .430 .410 Q7 .553 .308 Q8 .755 Q9 .678 Q10 .435 Q11 .348 .512 Q12 .670 Q13 .872 Q14 .934 Q15 .496 To clarify the structure, a forced two-factor EFA was performed using the same rotation and extraction. This solution accounted for 57.4% of the variance and showed clearer separation (Table 6 ). Here Factor 1 grouped 12 items broadly related to Insight, Learning, and Transference (Q1, Q4–Q10, Q12–Q15) and Factor 2 consisted of three items reflecting Group Process/Mirroring (Q2, Q3, Q11). Notably, no cross-loadings above 0.40 were observed, though the correlation between factors remained high (r = 0.71), once again pointing to a shared variance between domains. The conflicting outputs and particularly the high inter-factor correlations in both models described above suggested that a more parsimonious model might be needed. Therefore, to formally test the structure, a confirmatory factor analysis (CFA) was conducted. Table 6 EFA for forced 2-factor solution (Oblimin rotation) Item Factor 1 (Insight/Learn/Transf.) Factor 2 (Group process) Q1 .642 .142 Q2 .021 .723 Q3 .039 .690 Q4 .621 − .071 Q5 .800 − .050 Q6 .499 .284 Q7 .838 − .084 Q8 .687 .101 Q9 .805 .090 Q10 .520 .201 Q11 .170 .547 Q12 .873 − .143 Q13 .835 − .007 Q14 .784 .049 Q15 .762 .113 Confirmatory factor analysis (CFA) To formally test these structures, CFA models were estimated using WLSMV with items treated as ordered categorical. Three models were tested: Correlated Two-Factor Model (based on forced EFA) Correlated Three-Factor Model (based on theory and initial EFA) Second-Order Factor Model, where three first-order factors load onto a general latent factor. All models demonstrated good fit (Table 7 ), with the three-factor and second-order models performing slightly better. However, the three-factor model showed extremely high correlations between factors, most notably between Insight/Transference and Learning (r = 0.91), severely undermining their discriminant validity (Table 6 ). The two-factor model also showed high inter-factor correlation (r = 0.79). Table 7 Goodness-of-Fit Indices and Correlational Matrices for Competing CFA Models Measure Correlated 2-factor Correlated 3-factor Second-Order 3-factor Scaled Chi-square (χ²) 257.56 239.55 239.55 Degrees of Freedom (df) 89 87 87 p -value < .001 < .001 < .001 CFI 0.978 0.980 0.980 TLI 0.974 0.976 0.976 RMSEA 0.074 0.071 0.071 RMSEA 90% CI [0.063, 0.084] [0.060, 0.082] [0.060, 0.082] SRMR 0.041 0.039 0.039 Correlation matrices for three-factor model Correlation pair Correlated 2-factor model Correlated 3-factor model F1 ~ ~ F2 0.788*** 0.785*** F1 ~ ~ F3 --- 0.906*** F2 ~ ~ F3 --- 0.737*** Consequently, the second-order factor model was evaluated. This model, with three first-order factors loading onto a single higher-order factor, demonstrated fit indices identical to the correlated three-factor model (CFI = 0.980, TLI = 0.976, RMSEA = 0.071, SRMR = 0.039), indicating statistical equivalence. This equivalence confirms that the substantial correlations among the three first-order factors can be effectively explained by a single, overarching general factor. Given its ability to account for the high factor correlations while retaining the specific first-order facets suggested by EFA, the second-order factor model was selected as the final, best-fitting representation of the BGQ-E structure in this sample. Parameters of the final second-order factor model and theoretical reframing In this model (Table 8 ), all items loaded significantly ( p < .001) onto their respective first-order factors: Table 8 Standardised Loadings and Factor R-squared for the final second-order factor model Standardized loading (β) p -value First-order loadings F1 (Insight/Transf.) → Q1 .749 < .001 Q7 .772 < .001 Q8 .778 < .001 Q9 .877 < .001 Q10 .678 < .001 Q12 .759 < .001 Q13 .835 < .001 Q14 .835 < .001 Q15 .852 < .001 F2 (Group process) → Q2 .710 < .001 Q3 .709 < .001 Q11 .723 < .001 F3 (Learning) → Q4 .597 < .001 Q5 .820 < .001 Q6 .774 < .001 Second-order loadings G_SO → F1 .982 < .001 G_SO → F2 .799 < .001 G_SO → F3 .923 < .001 While statistical modelling suggested a three-factor structure, the extremely high correlation between the Insight/Transference and Learning factors (r = 0.91) supports a conceptual simplification. These domains appear to reflect a single underlying construct of reflective, intrapersonal development - what we term Individual Learning. Conversely, Group Learning remained more distinct, capturing interpersonal and relational dynamics in Balint groups. Thus, the empirical and theoretical evidence converges on a two-subscale model, nested within a broader general factor. Upon further review of both factor loadings and item content, Q6 and Q10 (Appendix A)—although statistically loading more weakly onto the general factor (loadings = 0.499 and 0.520, respectively)—align conceptually with Group Learning, their item content reflects shared reflection and group-based emotional processing, core aspects of the Group Learning construct. Therefore, these two items were reassigned to the Group Learning subscale based on strong theoretical fit, supporting a more nuanced and practically useful interpretation of subscale scores. In summary, the structural analysis indicates that while items can be grouped into three facets reflecting theory, these facets are highly related and largely driven by a dominant general dimension of Balint learning. Based on this structure, subsequent use of the questionnaire should prioritise the reliable Total Score, with the revised Group Learning and Individual Learning subscales offering meaningful supplementary insight for researchers or educators interested in specific group dynamics. The final questionnaire and its subscales Following reliability testing, factor analyses (EFA and CFA), and a theory-driven review of item content, we propose a refined subscale structure for the English version of the Balint Group Questionnaire (BGQ-E). The BGQ-E consists of 15 items, all of which contribute to a robust general factor representing overall Balint Learning Process. Within this structure, two interpretable subscales emerged: Individual Learning (10 items): reflecting self-awareness, insight, emotional processing, transference and countertransference. Group Learning (5 items): capturing mirroring, shared reflection and group process dynamics. Internal consistency for each scale was strong, as shown in Table 9 . Table 9 Final Cronbach’s alpha for BGG-E Dimension Cronbach’s alpha Number of items General Balint Learning 0.93 15 Individual Learning 0.92 10 Group Learning 0.79 5 Discussion This study aimed to evaluate the psychometric properties and factor structure of a 15-item Balint Group Questionnaire - English (BGQ-E) administered to fourth-year medical students in the UK following participation in Balint group sessions. The overall goal was to validate the instrument for measuring key aspects of learning that takes place in Balint groups within this specific population and context. The principal finding of this structural validation is that, within this sample of UK medical students, the BGQ-E items primarily reflect a strong, dominant general dimension of Balint Group Learning, with two further subscales being divided into Individual Learning (10 questions;α = 0.93) and Group Learning (5 questions; α = 0.79), which each retain good degrees of specificity, both theoretically and statistically. The factor structure identified in this study contrasts with the original validation of the BGQ-G in a German-speaking professional sample 13 , 14 . Flatten and colleagues 14 established a correlated three-factor structure (Transference Dynamics, Emotional and Cognitive Learning, Case Mirroring) with good fit and moderate-to-high, but arguably distinct, factor correlations ( r s ranging from 0.53 to 0.78 reported in their EFA). Our findings, however, align more closely with the validation of the Chinese version (BGQ-C) by Fritzsche et al 15 . (2021). While Fritzsche et al. 15 retained the correlated three-factor model based on acceptable CFA fit (CFI = 0.977, TLI = 0.971, RMSEA = 0.085, SRMR = 0.025), they explicitly noted extremely high factor correlations (reported EFA correlations of r = 0.90 to 0.92) and acknowledged that these "suggested the question of whether there may not be a general factor in the Chinese population after all” 15 . Our study, applying CFA to both two- and three-factor models derived from EFA on polychronic correlations, confirmed similarly high, untenable correlations between first-order factors (up to r = 0.91 in the 3-factor CFA). This lack of discriminant validity led us to conclude that the second-order factor model, with one general factor and two suggested subscales, provided a better representation than the correlated factor models for our sample. Several factors may contribute to the difference between our findings (and potentially the Chinese findings 15 ) and the original German validation 13 , 14 : Sample Characteristics: This is likely a primary driver, as our sample consisted of fourth-year medical students, likely with limited prior exposure to Balint groups and potentially less developed frameworks for differentiating nuanced psychological processes like transference versus cognitive insight compared to the presumably more experienced, professionally diverse German sample used by Flatten et al 13,14 . Less experienced participants might perceive and report their experience more holistically, leading to higher correlations between conceptually distinct aspects. The Chinese sample was also professionally diverse but culturally distinct. Additionally, facilitator experience may have also contributed. In the German sample, BG leaders had an average of 16.5 years of facilitation experience and 33 years of clinical experience. In contrast, both our facilitators and those in the Mandarin language study had substantially less experience. This difference in facilitation expertise may influence the depth and clarity of group processes, potentially affecting how participants report their learning. The professional diversity in our cohort is similar to Fritzsche et al’s study 15 , but participation conditions varied. Approximately 35% of the Chinese sample participated voluntarily, and participation in the German study was partially mandatory. In contrast, all students in our study were medical students engaging in Balint groups as part of their curriculum, without prior Balint exposure. This Balint-naïve, homogenous cohort likely shaped how participants interpreted and responded to items on the BGQ-E, particularly in distinguishing subscale content. Ceiling Effects: Ceiling effects were noted in our data similar to Fritzsche et al. 15 , although to a much lower extent. While statistically addressed using WLSMV estimation in CFA and polychromic correlations in EFA, pervasive ceiling effects can still restrict variance and potentially inflate correlations, making it harder to empirically distinguish related constructs. If the original German sample exhibited less pronounced ceiling effects, as suggested by Fritzsche et al 15 ., this could partly explain the clearer factor separation they observed. Cultural and Contextual Factors: Balint groups operate within specific cultural and educational contexts. Differences in teaching styles, communication norms regarding emotional expression or reflection, and the nature of the doctor-patient relationship in the UK medical training environment compared to Germany or China could influence how participants experience and report on the group process, impacting the questionnaire's factor structure 15 . Methodological Differences: While Flatten et al 14 . used EFA and CFA, slight variations in specific analytic choices (e.g., exact estimator details if not WLSMV, rotation methods, factor retention criteria prioritization) across studies could contribute minor differences, although the use of robust methods in our study and Fritzsche et al 15 . strengthens the likelihood that sample characteristics are the main driver of the high correlations observed. Furthermore, the questionnaires were completed by participants once at the end of the final Balint session in our study. This was not the case in the German 13 , 14 and Chinese 15 samples, where the questionnaires were completed at the end of each session. This repetitive exposure to the questionnaire could have helped participants distinguish between the nuance differences in separate questions. Scale Differences: The approach to scale response options from the German 13 , 14 version of the questionnaire might poorly translate to a UK audience (0 = strongly disagree, 1 = mostly disagree, 2 = agree somewhat, 3 = mildly agree, 4 = agree, 5 = strongly agree) as this is unusual in UK questionnaires. Students might have found it difficult to fill in the survey with more nuance. Limitations Several limitations should be considered when interpreting these findings. First, the study utilised a cross-sectional design, preventing inferences about causality or the trajectory of learning over time within Balint Groups. Longitudinal studies are needed to understand whether subscale distinctions become clearer with experience. Second, the sample consisted solely of fourth-year medical students from a single UK university context. While this allowed for internal consistency, it limits the generalisability of our findings to other healthcare populations (e.g., postgraduate trainees, experienced clinicians, other healthcare professions, different cultural settings), especially given the structural differences observed compared to the original German validation sample. Third, the reliance on self-report measures is susceptible to biases such as social desirability or halo effects, which may have contributed to the observed ceiling effects. These ceiling effects, although statistically managed, might suggest that the questionnaire struggles to distinguish between participants who report uniformly positive experiences. Additionally, two items (Q6 and Q10) displayed weaker loadings and factor ambiguity. Although statistically aligned with the Individual Learning Factor, their theoretical alignment with Group Learning supports its reclassification. This highlights the possibility of conceptual drift in a few items and suggests minor refinement of factors may be warranted in future iterations of the BGQ. Finally, there was very high correlation observed between the original Transference and Emotional and Cognitive Learning factors (r = 0.91), which challenges their discriminant validity. It appears that participants may not experience these dimensions as separate, but as interconnected aspects of a broader reflective learning process – an important consideration for both analysis and interpretation of subscales. Conclusion This study presents the first validated English-language version of the Balint Group Questionnaire (BGQ-E), and the first psychometric tool, to our knowledge, to assess Balint learning in medical students. The 15 item BGQ-E shows excellent internal consistency (α = 0.93) and a clear underlying general factor that captures overall Balint Process Learning. While a three-factor model was suggested by the original theory and initial analysis, the statistical overlap between dimensions (particularly between Transference and Emotional and Cognitive Learning) supports a more parsimonious interpretation. The content of the items clearly aligns with key theoretical aspects of Balint work, providing face validity We recommend using the total score as the primary outcome measure in research and evaluation. However, the Individual Learning (10 items; α = 0.92; incorporating learning on a personal level such as transference, emotional and cognitive reflection) and Group Learning (5 items; α = 0.79; incorporating Group processes such as case mirroring and group dynamics) subscales might provide supplementary insight, particularly for educational or supervision settings where specific aspects of personal or group reflection may help guide how facilitators approach future sessions. Although these subscales are not fully independent in statistical terms, they offer theoretically meaningful distinctions that may enhance curriculum design, feedback, and facilitation practice. Future Directions Our findings open several avenues for future research: Longitudinal validation: Tracking BGQ-E scores over time may reveal whether subscale distinctions become more psychometrically robust as participants gain Balint experience. Broader population testing: Replicating this validation in facilitator and participant groups – e.g. postgraduate trainees, clinicians, nurses and allied health professionals – will enhance generalisability and could identify population specific factors impacting Balint learning. Convergent and discriminant validity: Comparing BGQ-E scores with related constructs (e.g. empathy, burnout, reflective skills) and behavioural measures (such as facilitator ratings) would help the questionnaire’s construct validity. Qualitative refinement: Exploring qualitative data on how participants interpret each question, especially those with complex factor loadings and ceiling effects can help with future revisions or even the development of tailored versions of the BGQ-E for different professional contexts. Declarations Ethics approval and consent to participate The study was reviewed and approved by the University of Birmingham's Medical School Ethics Committee. Informed consent was obtained from all participants, with assurances of voluntary participation, anonymity, and the right to withdraw without academic penalty. Availability of data and materials There were no large public datasets used for this research. The Balint Group Questionnaire in English is available freely online. The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request. Consent for publication Not applicable. The manuscript contains only anonymised, aggregated data and no individual participant data or identifiable material. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted using personal or departmental resources only. Author contributions R.B. conceptualised the research project and made the statistical plan. All statistical analyses were conducted by and figures made by R.B. and A.K.A.M. R.B., A.K.A.M., S.D., and A.E. wrote the main manuscript text. All authors reviewed the manuscript. References Balint M, THE DOCTOR, HIS PATIENT, AND THE ILLNESS. Lancet. 1955;265(6866):683–8. Salinsky J. (2009) A very short introduction to Balint groups. https://balintsociety.org.uk/very-short-introduction-balint-groups . Accessed 29 August 2025. Royal College of Psychiatrists. (2024). https://www.rcpsych.ac.uk/docs/default-source/training/training/silver-guide-version-august-2024.pdf?sfvrsn=6933c4cb_19 . Accessed 29 August 2025. Huang H, Zhang H, Xie Y, Wang S, Cui H, Li L, Shao H, Geng Q. Effect of Balint group training on burnout and quality of work life among intensive care nurses: A randomized controlled trial. Neurol Psychiatry Brain Res. 2020;35:16–21. Yazdankhahfard M, Haghani F, Omid A. The Balint group and its application in medical education: A systematic review. J Educ Health Promot. 2019;8:124. Baverstock AC, Finlay FO. Maintaining compassion and preventing compassion fatigue: a practical guide. Archives disease Child - Educ Pract Ed. 2015;101(4):170–4. Huang L, Harsh J, Cui H, Wu J, Thai J, Zhang X et al. A Randomized Controlled Trial of Balint Groups to Prevent Burnout Among Residents in China. Front Psychiatry. 2020;10. Kjeldmand D, Holmström I, Rosenqvist U. Balint training makes GPs thrive better in their job. Patient Educ Couns. 2004;55(2):230–5. Gong B, Zhang X, Lu C, Wu C, Yang J. The effectiveness of Balint groups at improving empathy in medical and nursing education: a systematic review and meta-analysis of randomized controlled trials. BMC Med Educ. 2024;24(1). Howick J, Dudko M, Shi N, Feng, Ahmed A, Alluri N, Nockels K et al. Why might medical student empathy change throughout medical school? a systematic review and thematic synthesis of qualitative studies. BMC Med Educ. 2023;23(1). Cowell V, Ayalogu C, Ros A, Brown H, Shittu B, Akella A, Lasisi A, Bancroft J, Whitcroft H, Surendran I, Bu C, Older A, Gaynor E, Sullivan K. Balint Group Sessions for Medical Students: A Pilot Study. BJPsych Open. 2023;9(Suppl 1):S16–7. Wood B. (2023). https://balintsociety.org.uk/kings-college-london-gkt-school-medical-education . Accessed 29 August 2025. Tschuschke V, Flatten G. Effect of group leaders on doctors’ learning in Balint groups. Int J Psychiatry Med. 2018;54(2):83–96. Flatten G, Möller H, Tschuschke V. Wie wirksam sind Balintgruppen-Leiter? Zeitschrift für Psychosomatische Medizin und Psychotherapie. 2019;65(1):4–13. Fritzsche K, Shi L, Löhlein J, Wei J, Sha Y, Xie Y et al. How can learning effects be measured in Balint groups? Validation of a Balint group questionnaire in China. BMC Med Educ. 2021;21(1). McManus S, Killeen D, Hartnett Y, Fitzgerald G, Murphy KC. Establishing and evaluating a Balint group for fourth-year medical students at an Irish University. Ir J Psychol Med. 2020;37(2):99–105. Wickham H, Bryan J. (2025). readxl: Read Excel Files. R package version 1.4.5. https://github.com/tidyverse/readxl , https://readxl.tidyverse.org Wickham H, François R, Henry L, Müller K, Vaughan D. (2025). dplyr: A Grammar of Data Manipulation. R package version 1.1.4. https://dplyr.tidyverse.org William Revelle. (2025). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.5.6. https://CRAN.R-project.org/package=psych Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012;48(2):1–36. 10.18637/jss.v048.i02 . Nunnally JC, Bernstein IH. Psychometric theory. 3rd ed. New York: Mcgraw-Hill; 1994. Horn JL. A rationale and test for the number of factors in factor analysis. Psychometrika. 1965;30(2):179–85. Cattell RB. The Scree Test For The Number Of Factors. Multivar Behav Res. 1966;1(2):245–76. Howard MC. A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve? Int J Hum Comput Interact. 2016;32(1):51–62. Finney SJ, DiStefano C. Non-Normal and Categorical Data in Structural Equation Modeling. In: Hancock GR, Mueller RO, editors. Structural Equation Modeling: A Second Course. 2nd ed. Charlotte, NC: Information Age Publishing; 2013. pp. 439–92. Pavlov G, Maydeu-Olivares A, Shi D. Using the Standardized Root Mean Squared Residual (SRMR) to Assess Exact Fit in Structural Equation Models. Educ Psychol Meas. 2020;81(1):110–30. Additional Declarations No competing interests reported. Supplementary Files BalintGroupQuestionnairev1.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers invited by journal 15 Oct, 2025 Editor invited by journal 14 Oct, 2025 Editor assigned by journal 13 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 22 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":93472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eScree Plot of EFA\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7687973/v1/402e1172c24ede3238e1134c.png"},{"id":94663614,"identity":"fb73c68f-35b3-4999-9778-24436e7d0a76","added_by":"auto","created_at":"2025-10-29 12:12:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31784,"visible":true,"origin":"","legend":"\u003cp\u003eSubscales of the Birmingham Medical Student BGQ\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7687973/v1/ccd7e7d136da239ccff05e0d.png"},{"id":94674069,"identity":"be8e669c-11e2-4997-8c93-058638d3aacb","added_by":"auto","created_at":"2025-10-29 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English","fulltext":[{"header":"Background","content":"\u003cp\u003eOriginating in the 1950s from psychological training seminars held by psychoanalyst Michael Balint and his wife, Enid, for general practitioners in London, Balint groups were first described in the seminal work, \u003cem\u003eThe Doctor, his Patient and the Illness\u003c/em\u003e (1957) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Balint method eschews didactic lectures for a collaborative, psychoanalytically led format. In these small, confidential groups, a clinician presents a case, and the ensuing discussion focuses not on finding a diagnosis or treatment plan, but on understanding the complex emotional and relational dynamics between the practitioner and the patient. This process encourages clinicians to become better listeners and to reflect on their own feelings and responses within the clinical encounter. The continuity of these groups, which historically met weekly over several years, fosters a safe environment for open discussion and allows for the long-term follow-up of patient progress. Since its inception, the Balint group model has been adopted worldwide, leading to the establishment of national Balint Societies in over 22 countries dedicated to fostering this unique approach to reflective medical practice\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These groups aim to enhance self-awareness and deepen the understanding of the doctor-patient relationship.\u003c/p\u003e\u003cp\u003eBalint groups are a mandatory part of psychiatric training in UK\u003csup\u003e3\u003c/sup\u003e and are increasingly being used in other medical specialities \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Given that nurturing empathy and reflective practice have been embedded in undergraduate medical education, the implementation of Balint Groups for medical students has been increasing \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCompelling evidence from a randomized controlled trial involving medical residents established that Balint groups were an effective intervention for preventing burnout\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The generalisability of these findings is supported by similar results in other high-stress clinical environments, with studies showing positive effects on the quality of work life for professionals such as intensive care nurses\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Furthermore, longitudinal research suggests a lasting impact on professional satisfaction, as general practitioners who engage in Balint training report \"thriving better\" in their roles, indicating a positive influence on both job satisfaction and professional resilience\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, a systematic review and meta-analysis revealed that participation in Balint groups led to a significant increase in empathy scores compared to control groups, with a standardised mean difference (SMD) of 0.71 among medical students\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This finding is particularly pertinent given the documented decline in empathy levels during medical training\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Various medical schools in the United Kingdom have implemented Balint Groups as part of their psychiatry curriculum, and with 400 students per cohort, our scheme at the University of Birmingham is one of the biggest in the country \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite the recognised benefits of Balint groups, there exists a notable gap in the availability of validated instruments to assess the learning outcomes derived from these sessions, especially among medical students. The German Balint Group Questionnaire\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e is a validated 17 step questionnaire to evaluate distinct processes involved in Balint Groups, such as emotional and cognitive learning, mirroring, and transference. While a Mandarin version of the Balint Group Questionnaire (BGQ) has been validated in China\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003c/sup\u003e an English version. Furthermore, there is no existing questionnaire that has been validated for medical students. This absence hampers the ability to systematically evaluate and compare the effectiveness of Balint groups across diverse educational settings.\u003c/p\u003e\u003cp\u003eTraditionally, studies have relied on general empathy scales, such as the Jefferson Scale of Physician Empathy\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003c/sup\u003e to measure the impact of Balint groups. However, these tools may not fully capture the unique reflective and relational dynamics that are being developed within Balint sessions. The development and validation of an English version of the BGQ specifically for medical students would provide a more nuanced and accurate assessment of the educational value of Balint groups. Additionally, it will also help us understand whether the process of Balint is well translated across to medical students.\u003c/p\u003e\u003cp\u003eThis study aims to bridge this gap by validating the English translation of the BGQ for use among medical students. By doing so, it seeks to enhance the tools available for evaluating the reflective learning processes integral to Balint groups and to support the integration of such groups into medical curricula.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eThis study was undertaken at the Birmingham and Solihull Mental Health and NHS Foundation Trust Teaching Academy and employed a cross-sectional validation design. It took place between September 2024 and March 2025, across five consecutive rotations in the academic year.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe target population consisted of fourth- year medical students from the University of Birmingham, United Kingdom. Participation in the Balint Groups was a mandatory component of their psychiatry rotation. A total of 400 students, spread roughly across five rotations, participated in the Balint groups. Within each rotation, 8 Balint groups ran concurrently.\u003c/p\u003e\n\u003ch3\u003eBalint group intervention\u003c/h3\u003e\n\u003cp\u003eEach student attended a maximum of four Balint group sessions over the course of their five-week psychiatry placement. For the large majority of students this was their first exposure to the Balint method - a deliberate choice to capture first impressions and learning gains that are not impacted by prior experiences. Each group comprised 10 to 12 students. Groups were facilitated by facilitators ranging in Balint experience, but all having been trained in the process.\u003c/p\u003e\n\u003ch3\u003eQuestionnaire\u003c/h3\u003e\n\u003cp\u003eStudents were invited to voluntarily complete the English version of BGQ-G at the end of their rotation. All participants provided informed consent prior to data collection. Students completed the forms anonymously to reduce social desirability bias and encourage candid responses. The translated version of the BGQ-G\u003csup\u003e13,14\u003c/sup\u003e was obtained via email from the original authors of the BGQ, Prof. Guido Flatten.\u003c/p\u003e\u003cp\u003eThe BGQ-G\u003csup\u003e13,14\u003c/sup\u003e aims to capture key intrapersonal learning processes and perceived group dynamics inherent to Balint group work. Theoretically, the items were designed to reflect three dimensions identified in previous research: (1) Reflection of transference dynamics in the doctor-patient relationship, (2) Emotional and cognitive learning, and (3) Case mirroring within the dynamic of the group \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eParticipants responded to each of the 15 statements using a 5-point Likert-type scale, ranging from 0 (\u0026ldquo;Strongly Disagree) to 5 (\u0026ldquo;Strongly Agree\"). Higher scores indicate greater subjective endorsement of the learning process described in the item. No items required reverse scoring. The full list of questionnaire items is available in Appendix A.\u003c/p\u003e\u003cp\u003eThere are some key differences in the English version of the BGQ as compared to the original German version. The German BGQ comprises of 17 items, with 12 items being divided into three domains (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the English translation of the BGQ (Appendix A), contains 15 questions, with questions 1 and 8 of the German BGQ missing. The missing questions 1 and 8 do not come under any factor in the German BGQ scales, so therefore should not impede the calculation of scores based on domains. However, a limited factor exploration based on the 3 previously named factors and an exploratory factor analysis will be undertaken to explore whether the missing questions in the English BGQ impact the 3 domains that the BGQ tests. The updated questions used in the English BGQ are 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=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDomains in German BGQ\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor 1: Emotional \u0026amp; Cognitive Learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems 5, 6, 9, 11, 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNew knowledge and emotional processing from case discussions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor 2: Reflection of Transference Dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems 2, 3, 10, 13, 15, 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAwareness of unconscious influences on doctor-patient interactions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor 3: Case Mirroring in Group Dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems 4, 7, 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHow patient case dynamics are reflected in group interactions.\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\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\u003e\u003cem\u003eCorresponding questions in BGQ-E\u003c/em\u003e Thus, a total of 15 items can be assigned to the three following domains (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGerman BGQ questions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorresponding question on English BGQ\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\u003eMissing\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\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\u003cp\u003e2\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\u003cp\u003e3\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\u003e4\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\u003cp\u003e5\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\u003cp\u003e6\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\u003cp\u003eMissing\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\u003cp\u003e7\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\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\u003cp\u003e9\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\u003cp\u003e10\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\u003e11\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\u003cp\u003e12\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\u003cp\u003e13\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\u003cp\u003e14\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\u003cp\u003e15\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\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\u003eReorganised questions per domain in English BGQ.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomain 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReflection of transference dynamics in the doctor-patient relationship\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuestions\u003c/p\u003e\u003cp\u003e1, 8, 11, 13, 14\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomain 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmotional and Cognitive learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuestions\u003c/p\u003e\u003cp\u003e4, 5, 7, 9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomain 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCase mirroring in the dynamic of the group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuestions\u003c/p\u003e\u003cp\u003e3, 6, 10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study was reviewed and approved by the University of Birmingham's Medical School Ethics Committee. Informed consent was obtained from all participants, with assurances of voluntary participation, anonymity, and the right to withdraw without academic penalty.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical methods\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R version 4.4.2 (2024-10-31) and relevant packages, including readxl \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003c/sup\u003e dplyr \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, psych\u003csup\u003e18)\u003c/sup\u003e, and lavaan\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. An alpha level of 0.05 was adopted for all tests of statistical significance.\u003c/p\u003e\u003cp\u003eDescriptive statistics (means, standard deviations, medians, skewness, kurtosis, Cronbach\u0026rsquo;s alpha) were computed for all 15 BGQ items. The three-factor structure found in the German BGQ was tested within our sample. Reliability coefficients\u0026thinsp;\u0026ge;\u0026thinsp;.70 were considered acceptable \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo investigate the underlying empirical factor structure of the 15-item BGQ within the study sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;349), an exploratory factor analysis (EFA) was conducted. The number of factors to retain was primarily determined by Horn's Parallel Analysis\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003c/sup\u003e supplemented by the Kaiser criterion (eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1) and scree plot inspection\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. An oblique rotation (Oblimin) was applied, allowing factors to correlate. A comparative EFA forcing a two-factor solution was also conducted to examine a potentially more parsimonious structure suggested by some retention criteria. Factor loadings\u0026thinsp;\u0026ge;\u0026thinsp;.40 were considered salient\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eConfirmatory factor analysis (CFA) was utilised via the lavaan package\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e to evaluate competing factor structures identified during the EFA phase. Models were estimated using the Weighted Least Squares Mean and Variance (WLSMV) estimator, treating items as ordered categorical variables\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Based on the EFA results, the following candidate models were specified \u003cem\u003ea priori\u003c/em\u003e for evaluation: (a) a correlated two-factor model, (b) a correlated three-factor model, and (c) a second-order factor model testing a hierarchical structure with three first-order factors loading onto one general factor. Model adequacy was evaluated using goodness-of-fit indices: the scaled chi-square statistic (χ\u0026sup2;), Comparative Fit Index (CFI; \u0026ge; .95 excellent), Tucker-Lewis Index (TLI; \u0026ge; .95 excellent), Root Mean Square Error of Approximation (RMSEA; \u0026le; .06 good, \u0026le; .08 acceptable), and Standardized Root Mean Square Residual (SRMR; \u0026le; .08 good)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The final model was selected based on statistical fit, theoretical interpretability, and parsimony.\u003c/p\u003e\u003cp\u003eGiven that the primary goal was to test specific a priori models against one another using CFA, and to maximize statistical power for these model comparisons, all analyses were conducted on the full sample.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eParticipant characteristics\u003c/h2\u003e\u003cp\u003eOut of 400 medical students, 353 gave informed consent to participate in the study and filled out the BGQ-E. 4 were excluded due to missing data, leaving a total cohort of 349 students. All students are 4th year medical students from the University of Birmingham.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eItem distribution\u003c/h2\u003e\u003cp\u003eThe frequency distributions of all BGQ-E questions were not normally distributed, with some negatively skewed distributions with mild to moderate ceiling effects (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Questions 1, 6, 10 and 11 showed clear ceiling effects. All item distributions were unimodal, with modal values of 4. The mean values show a high agreement of the participants on average. The present ceiling effects limit the differentiability of the items, although the item selectivities were still within the statistically acceptable range. Due to the data not following normal distribution, methods for ordinally scaled variables with ordered categories had to be used for inferential statistical analysis.\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\u003eDescriptive statistics for BGQ-E items\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\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\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\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eReliability analysis\u003c/h2\u003e\u003cp\u003eThe full 15-item BGQ demonstrated excellent internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.93). Reliability estimates for the three theoretically proposed subscales\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e were good for the Transference Dynamics (α\u0026thinsp;=\u0026thinsp;0.84) and Emotional/Cognitive Learning subscales (α\u0026thinsp;=\u0026thinsp;0.80), and acceptable for Case Mirroring (α\u0026thinsp;=\u0026thinsp;0.69). However, as the factor structure was revisited empirically, reliability values for revised subscales are presented in later in this section.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eExploratory factor analysis (EFA)\u003c/h2\u003e\u003cp\u003eThe suitability of the 15-item data for factor analysis (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;349) was confirmed. Bartlett's test of sphericity was significant (χ\u0026sup2;(105)\u0026thinsp;=\u0026thinsp;2961.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the Kaiser-Meyer-Olkin measure indicated excellent sampling adequacy (KMO\u0026thinsp;=\u0026thinsp;0.95).\u003c/p\u003e\u003cp\u003eAn EFA using the polychromic correlation matrix was undertaken. The Kaiser criterion (eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1) and the scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) both suggested a two-factor structure. Prioritising the a priori 3- factor model used by Flatten et al\u003csup\u003e14,\u003c/sup\u003e a 3-factor solution was also extracted and examined.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe initial three-factor EFA solution accounted for 60.7% of the total item variance and revealed three weak but interpretable factors (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Factor names were decided based on authors\u0026rsquo; deliberation on the theoretical clustering of questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFactor 1: Broad Insight/Transference Dimension,\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFactor 2: Reflected Group Process/Mirroring\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFactor 3: Non-Specific Learning Aspects\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eHowever, cross-loadings were notable for Q6 and Q11 (\u0026gt;\u0026thinsp;0.40), and item Q7 also showed complexity. Moreover, the factors were highly correlated (r\u0026thinsp;=\u0026thinsp;0.44 to 0.68), suggesting substantial overlap in the underlying constructs, proving this model to not be the best fit, at least, in our population.\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\u003eEFA Pattern Matrix Loadings for 3-Factor Solution (Oblimin Rotation)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor 1 (Insight/Transf.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor 2 (Group process)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFactor 3 (Learning)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.429\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\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\u003e.408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ5\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\u003e.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.755\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.678\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.435\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.670\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.872\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.934\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.496\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\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo clarify the structure, a forced two-factor EFA was performed using the same rotation and extraction. This solution accounted for 57.4% of the variance and showed clearer separation (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Here Factor 1 grouped 12 items broadly related to Insight, Learning, and Transference (Q1, Q4\u0026ndash;Q10, Q12\u0026ndash;Q15) and Factor 2 consisted of three items reflecting Group Process/Mirroring (Q2, Q3, Q11). Notably, no cross-loadings above 0.40 were observed, though the correlation between factors remained high (r\u0026thinsp;=\u0026thinsp;0.71), once again pointing to a shared variance between domains.\u003c/p\u003e\u003cp\u003eThe conflicting outputs and particularly the high inter-factor correlations in both models described above suggested that a more parsimonious model might be needed. Therefore, to formally test the structure, a confirmatory factor analysis (CFA) was conducted.\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\u003eEFA for forced 2-factor solution (Oblimin rotation)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor 1 (Insight/Learn/Transf.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor 2 (Group process)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.113\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\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eConfirmatory factor analysis (CFA)\u003c/h2\u003e\u003cp\u003eTo formally test these structures, CFA models were estimated using WLSMV with items treated as ordered categorical. Three models were tested:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCorrelated Two-Factor Model (based on forced EFA)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCorrelated Three-Factor Model (based on theory and initial EFA)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSecond-Order Factor Model, where three first-order factors load onto a general latent factor.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eAll models demonstrated good fit (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), with the three-factor and second-order models performing slightly better. However, the three-factor model showed extremely high correlations between factors, most notably between \u003cem\u003eInsight/Transference\u003c/em\u003e and \u003cem\u003eLearning\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.91), severely undermining their discriminant validity (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The two-factor model also showed high inter-factor correlation (r\u0026thinsp;=\u0026thinsp;0.79).\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\u003eGoodness-of-Fit Indices and Correlational Matrices for Competing CFA Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelated 2-factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCorrelated 3-factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSecond-Order 3-factor\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScaled Chi-square (χ\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e257.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e239.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e239.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegrees of Freedom (df)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA 90% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[0.063, 0.084]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.060, 0.082]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.060, 0.082]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eCorrelation matrices for three-factor model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCorrelation pair\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCorrelated 2-factor model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCorrelated 3-factor model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF1\u0026thinsp;~\u0026thinsp;~\u0026thinsp;F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.788***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.785***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF1\u0026thinsp;~\u0026thinsp;~\u0026thinsp;F3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.906***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF2\u0026thinsp;~\u0026thinsp;~\u0026thinsp;F3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.737***\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\u003eConsequently, the second-order factor model was evaluated. This model, with three first-order factors loading onto a single higher-order factor, demonstrated fit indices identical to the correlated three-factor model (CFI\u0026thinsp;=\u0026thinsp;0.980, TLI\u0026thinsp;=\u0026thinsp;0.976, RMSEA\u0026thinsp;=\u0026thinsp;0.071, SRMR\u0026thinsp;=\u0026thinsp;0.039), indicating statistical equivalence. This equivalence confirms that the substantial correlations among the three first-order factors can be effectively explained by a single, overarching general factor. Given its ability to account for the high factor correlations while retaining the specific first-order facets suggested by EFA, the second-order factor model was selected as the final, best-fitting representation of the BGQ-E structure in this sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eParameters of the final second-order factor model and theoretical reframing\u003c/h2\u003e\u003cp\u003eIn this model (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), all items loaded significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) onto their respective first-order factors:\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\u003eStandardised Loadings and Factor R-squared for the final second-order factor model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStandardized loading (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eFirst-order loadings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF1 (Insight/Transf.) \u0026rarr;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF2 (Group process) \u0026rarr;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF3 (Learning) \u0026rarr;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSecond-order loadings\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG_SO \u0026rarr; F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG_SO \u0026rarr; F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG_SO \u0026rarr; F3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\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\u003eWhile statistical modelling suggested a three-factor structure, the extremely high correlation between the \u003cem\u003eInsight/Transference\u003c/em\u003e and \u003cem\u003eLearning\u003c/em\u003e factors (r\u0026thinsp;=\u0026thinsp;0.91) supports a conceptual simplification. These domains appear to reflect a single underlying construct of reflective, intrapersonal development - what we term Individual Learning. Conversely, Group Learning remained more distinct, capturing interpersonal and relational dynamics in Balint groups. Thus, the empirical and theoretical evidence converges on a two-subscale model, nested within a broader general factor.\u003c/p\u003e\u003cp\u003eUpon further review of both factor loadings and item content, Q6 and Q10 (Appendix A)\u0026mdash;although statistically loading more weakly onto the general factor (loadings\u0026thinsp;=\u0026thinsp;0.499 and 0.520, respectively)\u0026mdash;align conceptually with Group Learning, their item content reflects shared reflection and group-based emotional processing, core aspects of the Group Learning construct. Therefore, these two items were reassigned to the Group Learning subscale based on strong theoretical fit, supporting a more nuanced and practically useful interpretation of subscale scores.\u003c/p\u003e\u003cp\u003eIn summary, the structural analysis indicates that while items can be grouped into three facets reflecting theory, these facets are highly related and largely driven by a dominant general dimension of Balint learning. Based on this structure, subsequent use of the questionnaire should prioritise the reliable Total Score, with the revised Group Learning and Individual Learning subscales offering meaningful supplementary insight for researchers or educators interested in specific group dynamics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eThe final questionnaire and its subscales\u003c/h2\u003e\u003cp\u003eFollowing reliability testing, factor analyses (EFA and CFA), and a theory-driven review of item content, we propose a refined subscale structure for the English version of the Balint Group Questionnaire (BGQ-E).\u003c/p\u003e\u003cp\u003eThe BGQ-E consists of 15 items, all of which contribute to a robust general factor representing overall Balint Learning Process. Within this structure, two interpretable subscales emerged:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIndividual Learning (10 items): reflecting self-awareness, insight, emotional processing, transference and countertransference.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGroup Learning (5 items): capturing mirroring, shared reflection and group process dynamics.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInternal consistency for each scale was strong, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFinal Cronbach\u0026rsquo;s alpha for BGG-E\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\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\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of items\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral Balint Learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual Learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup Learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\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 evaluate the psychometric properties and factor structure of a 15-item Balint Group Questionnaire - English (BGQ-E) administered to fourth-year medical students in the UK following participation in Balint group sessions. The overall goal was to validate the instrument for measuring key aspects of learning that takes place in Balint groups within this specific population and context.\u003c/p\u003e\u003cp\u003eThe principal finding of this structural validation is that, within this sample of UK medical students, the BGQ-E items primarily reflect a strong, dominant general dimension of Balint Group Learning, with two further subscales being divided into Individual Learning (10 questions;α\u0026thinsp;=\u0026thinsp;0.93) and Group Learning (5 questions; α\u0026thinsp;=\u0026thinsp;0.79), which each retain good degrees of specificity, both theoretically and statistically.\u003c/p\u003e\u003cp\u003eThe factor structure identified in this study contrasts with the original validation of the BGQ-G in a German-speaking professional sample\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Flatten and colleagues\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e established a correlated three-factor structure (Transference Dynamics, Emotional and Cognitive Learning, Case Mirroring) with good fit and moderate-to-high, but arguably distinct, factor correlations (\u003cem\u003er\u003c/em\u003es ranging from 0.53 to 0.78 reported in their EFA). Our findings, however, align more closely with the validation of the Chinese version (BGQ-C) by Fritzsche et al\u003csup\u003e15\u003c/sup\u003e. (2021). While Fritzsche et al.\u003csup\u003e15\u003c/sup\u003e retained the correlated three-factor model based on acceptable CFA fit (CFI\u0026thinsp;=\u0026thinsp;0.977, TLI\u0026thinsp;=\u0026thinsp;0.971, RMSEA\u0026thinsp;=\u0026thinsp;0.085, SRMR\u0026thinsp;=\u0026thinsp;0.025), they explicitly noted extremely high factor correlations (reported EFA correlations of \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90 to 0.92) and acknowledged that these \"suggested the question of whether there may not be a general factor in the Chinese population after all\u0026rdquo; \u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study, applying CFA to both two- and three-factor models derived from EFA on polychronic correlations, confirmed similarly high, untenable correlations between first-order factors (up to \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91 in the 3-factor CFA). This lack of discriminant validity led us to conclude that the second-order factor model, with one general factor and two suggested subscales, provided a better representation than the correlated factor models for our sample.\u003c/p\u003e\u003cp\u003eSeveral factors may contribute to the difference between our findings (and potentially the Chinese findings\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e) and the original German validation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSample Characteristics: This is likely a primary driver, as our sample consisted of fourth-year medical students, likely with limited prior exposure to Balint groups and potentially less developed frameworks for differentiating nuanced psychological processes like transference versus cognitive insight compared to the presumably more experienced, professionally diverse German sample used by Flatten et al\u003csup\u003e13,14\u003c/sup\u003e. Less experienced participants might perceive and report their experience more holistically, leading to higher correlations between conceptually distinct aspects. The Chinese sample was also professionally diverse but culturally distinct. Additionally, facilitator experience may have also contributed. In the German sample, BG leaders had an average of 16.5 years of facilitation experience and 33 years of clinical experience. In contrast, both our facilitators and those in the Mandarin language study had substantially less experience. This difference in facilitation expertise may influence the depth and clarity of group processes, potentially affecting how participants report their learning. The professional diversity in our cohort is similar to Fritzsche et al\u0026rsquo;s study\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, but participation conditions varied. Approximately 35% of the Chinese sample participated voluntarily, and participation in the German study was partially mandatory. In contrast, all students in our study were medical students engaging in Balint groups as part of their curriculum, without prior Balint exposure. This Balint-na\u0026iuml;ve, homogenous cohort likely shaped how participants interpreted and responded to items on the BGQ-E, particularly in distinguishing subscale content.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCeiling Effects: Ceiling effects were noted in our data similar to Fritzsche et al.\u003csup\u003e15\u003c/sup\u003e, although to a much lower extent. While statistically addressed using WLSMV estimation in CFA and polychromic correlations in EFA, pervasive ceiling effects can still restrict variance and potentially inflate correlations, making it harder to empirically distinguish related constructs. If the original German sample exhibited less pronounced ceiling effects, as suggested by Fritzsche et al\u003csup\u003e15\u003c/sup\u003e., this could partly explain the clearer factor separation they observed.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCultural and Contextual Factors: Balint groups operate within specific cultural and educational contexts. Differences in teaching styles, communication norms regarding emotional expression or reflection, and the nature of the doctor-patient relationship in the UK medical training environment compared to Germany or China could influence how participants experience and report on the group process, impacting the questionnaire's factor structure\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMethodological Differences: While Flatten et al\u003csup\u003e14\u003c/sup\u003e. used EFA and CFA, slight variations in specific analytic choices (e.g., exact estimator details if not WLSMV, rotation methods, factor retention criteria prioritization) across studies could contribute minor differences, although the use of robust methods in our study and Fritzsche et al\u003csup\u003e15\u003c/sup\u003e. strengthens the likelihood that sample characteristics are the main driver of the high correlations observed. Furthermore, the questionnaires were completed by participants once at the end of the final Balint session in our study. This was not the case in the German\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and Chinese\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e samples, where the questionnaires were completed at the end of each session. This repetitive exposure to the questionnaire could have helped participants distinguish between the nuance differences in separate questions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eScale Differences: The approach to scale response options from the German\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e version of the questionnaire might poorly translate to a UK audience (0\u0026thinsp;=\u0026thinsp;strongly disagree, 1\u0026thinsp;=\u0026thinsp;mostly disagree, 2\u0026thinsp;=\u0026thinsp;agree somewhat, 3\u0026thinsp;=\u0026thinsp;mildly agree, 4\u0026thinsp;=\u0026thinsp;agree, 5\u0026thinsp;=\u0026thinsp;strongly agree) as this is unusual in UK questionnaires. Students might have found it difficult to fill in the survey with more nuance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, the study utilised a cross-sectional design, preventing inferences about causality or the trajectory of learning over time within Balint Groups. Longitudinal studies are needed to understand whether subscale distinctions become clearer with experience. Second, the sample consisted solely of fourth-year medical students from a single UK university context. While this allowed for internal consistency, it limits the generalisability of our findings to other healthcare populations (e.g., postgraduate trainees, experienced clinicians, other healthcare professions, different cultural settings), especially given the structural differences observed compared to the original German validation sample. Third, the reliance on self-report measures is susceptible to biases such as social desirability or halo effects, which may have contributed to the observed ceiling effects. These ceiling effects, although statistically managed, might suggest that the questionnaire struggles to distinguish between participants who report uniformly positive experiences.\u003c/p\u003e\u003cp\u003eAdditionally, two items (Q6 and Q10) displayed weaker loadings and factor ambiguity. Although statistically aligned with the Individual Learning Factor, their theoretical alignment with Group Learning supports its reclassification. This highlights the possibility of conceptual drift in a few items and suggests minor refinement of factors may be warranted in future iterations of the BGQ.\u003c/p\u003e\u003cp\u003eFinally, there was very high correlation observed between the original Transference and Emotional and Cognitive Learning factors (r\u0026thinsp;=\u0026thinsp;0.91), which challenges their discriminant validity. It appears that participants may not experience these dimensions as separate, but as interconnected aspects of a broader reflective learning process \u0026ndash; an important consideration for both analysis and interpretation of subscales.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents the first validated English-language version of the Balint Group Questionnaire (BGQ-E), and the first psychometric tool, to our knowledge, to assess Balint learning in medical students. The 15 item BGQ-E shows excellent internal consistency (α\u0026thinsp;=\u0026thinsp;0.93) and a clear underlying general factor that captures overall Balint Process Learning. While a three-factor model was suggested by the original theory and initial analysis, the statistical overlap between dimensions (particularly between Transference and Emotional and Cognitive Learning) supports a more parsimonious interpretation. The content of the items clearly aligns with key theoretical aspects of Balint work, providing face validity\u003c/p\u003e\u003cp\u003eWe recommend using the total score as the primary outcome measure in research and evaluation. However, the Individual Learning (10 items; α\u0026thinsp;=\u0026thinsp;0.92; incorporating learning on a personal level such as transference, emotional and cognitive reflection) and Group Learning (5 items; α\u0026thinsp;=\u0026thinsp;0.79; incorporating Group processes such as case mirroring and group dynamics) subscales might provide supplementary insight, particularly for educational or supervision settings where specific aspects of personal or group reflection may help guide how facilitators approach future sessions. Although these subscales are not fully independent in statistical terms, they offer theoretically meaningful distinctions that may enhance curriculum design, feedback, and facilitation practice.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eOur findings open several avenues for future research:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLongitudinal validation: Tracking BGQ-E scores over time may reveal whether subscale distinctions become more psychometrically robust as participants gain Balint experience.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBroader population testing: Replicating this validation in facilitator and participant groups \u0026ndash; e.g. postgraduate trainees, clinicians, nurses and allied health professionals \u0026ndash; will enhance generalisability and could identify population specific factors impacting Balint learning.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConvergent and discriminant validity: Comparing BGQ-E scores with related constructs (e.g. empathy, burnout, reflective skills) and behavioural measures (such as facilitator ratings) would help the questionnaire\u0026rsquo;s construct validity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eQualitative refinement: Exploring qualitative data on how participants interpret each question, especially those with complex factor loadings and ceiling effects can help with future revisions or even the development of tailored versions of the BGQ-E for different professional contexts.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by the University of Birmingham\u0026apos;s Medical School Ethics Committee. Informed consent was obtained from all participants, with assurances of voluntary participation, anonymity, and the right to withdraw without academic penalty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no large public datasets used for this research. The Balint Group Questionnaire in English is available freely online. The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The manuscript contains only anonymised, aggregated data and no individual participant data or identifiable material.\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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted using personal or departmental resources only.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.B. conceptualised the research project and made the statistical plan. All statistical analyses were conducted by and figures made by R.B. and A.K.A.M.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR.B., A.K.A.M., S.D., and A.E. wrote the main manuscript text. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBalint M, THE DOCTOR, HIS PATIENT, AND THE ILLNESS. Lancet. 1955;265(6866):683\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalinsky J. (2009) A very short introduction to Balint groups. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://balintsociety.org.uk/very-short-introduction-balint-groups\u003c/span\u003e\u003cspan address=\"https://balintsociety.org.uk/very-short-introduction-balint-groups\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 29 August 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoyal College of Psychiatrists. 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BMC Med Educ. 2024;24(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHowick J, Dudko M, Shi N, Feng, Ahmed A, Alluri N, Nockels K et al. Why might medical student empathy change throughout medical school? a systematic review and thematic synthesis of qualitative studies. BMC Med Educ. 2023;23(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCowell V, Ayalogu C, Ros A, Brown H, Shittu B, Akella A, Lasisi A, Bancroft J, Whitcroft H, Surendran I, Bu C, Older A, Gaynor E, Sullivan K. Balint Group Sessions for Medical Students: A Pilot Study. BJPsych Open. 2023;9(Suppl 1):S16\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood B. 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(2025). dplyr: A Grammar of Data Manipulation. R package version 1.1.4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dplyr.tidyverse.org\u003c/span\u003e\u003cspan address=\"https://dplyr.tidyverse.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliam Revelle. (2025). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.5.6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=psych\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=psych\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012;48(2):1\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/jss.v048.i02\u003c/span\u003e\u003cspan address=\"10.18637/jss.v048.i02\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNunnally JC, Bernstein IH. Psychometric theory. 3rd ed. New York: Mcgraw-Hill; 1994.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHorn JL. A rationale and test for the number of factors in factor analysis. Psychometrika. 1965;30(2):179\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCattell RB. The Scree Test For The Number Of Factors. Multivar Behav Res. 1966;1(2):245\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoward MC. A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve? Int J Hum Comput Interact. 2016;32(1):51\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinney SJ, DiStefano C. Non-Normal and Categorical Data in Structural Equation Modeling. In: Hancock GR, Mueller RO, editors. Structural Equation Modeling: A Second Course. 2nd ed. Charlotte, NC: Information Age Publishing; 2013. pp. 439\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePavlov G, Maydeu-Olivares A, Shi D. Using the Standardized Root Mean Squared Residual (SRMR) to Assess Exact Fit in Structural Equation Models. Educ Psychol Meas. 2020;81(1):110\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7687973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7687973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBalint groups are an established method for strengthening doctor-patient relationships and building professional resilience, particularly in undergraduate medical education. Despite their increasing use, a validated English-language tool to measure the specific learning outcomes for medical students has been conspicuous by its absence. This study sought to validate an English translation of the German Balint Group Questionnaire (BGQ-E) for use within this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional validation study was undertaken involving 349 fourth-year medical students at the University of Birmingham. After their Balint group sessions, students completed the 15-item BGQ-E. To examine the instrument's psychometric properties and factor structure, a suite of analyses was conducted, including descriptive statistics, reliability analysis, as well as exploratory and confirmatory factor analysis (CFA). The CFA was specifically used to test various competing models, including two-factor, three-factor, and second-order factor structures.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe BGQ-E demonstrated excellent internal consistency, with a Cronbach's alpha of 0.93. While the initial analysis pointed towards a three-factor structure, the CFA revealed that the factors were too highly correlated, indicating a lack of discriminant validity. The model that provided the best fit was a second-order factor model with a dominant general factor, which has been termed \"Balint Process and Learning,\" and two distinct, albeit related, subscales: \"Individual Learning\" and \"Group Learning\". All items loaded significantly onto their respective factors, and both subscales showed strong internal consistency (alpha values of 0.92 and 0.79, respectively).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe BGQ-E is the first validated English-language tool for measuring the effects of Balint groups in a medical student population. It is a reliable instrument with a robust underlying structure. For research and evaluation, it is recommended to use the total score as the primary measure, with the two subscales providing valuable supplementary insight into the dynamics of individual versus group learning. Future research should focus on longitudinal validation and testing this tool in diverse healthcare populations.\u003c/p\u003e","manuscriptTitle":"Measuring Balint Group learning effects in medical students: A validation of the Balint Group Questionnaire for Medical Students in English","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 12:12:12","doi":"10.21203/rs.3.rs-7687973/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-11T20:44:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T08:13:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T14:00:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76429513898521805288358628414557373353","date":"2025-11-11T07:25:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39870755664261783632212055214708564291","date":"2025-11-10T17:38:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T05:51:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T05:15:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T12:03:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-13T12:02:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-09-22T20:10:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85239711-f9f4-41eb-835d-3e0c3594021a","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T08:40:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 12:12:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7687973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7687973","identity":"rs-7687973","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}