Psychometric Validation of the Mizan Meta-memory and Meta-Concentration Scale for Students (MMSS) Among a Sample of Health Care Students in Nigeria

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The Mizan meta-memory and meta-concentration scale for students (MMSS) was developed as a concise tool to assess these cognitive functions. While the MMSS has been validated in Ethiopian and Saudi Arabian populations, its applicability to Nigerian healthcare students remains unexplored. Given the intense cognitive demands of medical education, this study aims to validate the MMSS among Nigerian healthcare students, assessing its psychometric properties and reliability. Methods: A cross-sectional study using simple random sampling was conducted among 299 healthcare students (Medicine and Surgery, Dentistry and Physiotherapy) at the University of Ibadan. Participants completed an online survey containing the MMSS, a nine-item questionnaire divided into two subscales: meta-memory and meta-concentration. Internal consistency was evaluated using Cronbach’s alpha and McDonald’s omega. Exploratory factor analysis (EFA) was performed to assess construct validity while confirmatory factor analysis (CFA) was used to determine model fit. Results: The MMSS demonstrated strong internal consistency both for the MMSS global and subscales (Cronbach’s alpha = 0.875, 0.808, 0.857; McDonald’s omega = 0.871, 0.805, 0.859). EFA confirmed a two-factor structure, with the meta-memory subscale explaining 50.26% of the variance and the meta-concentration subscale accounting for 12.78%. CFA results indicated a good model fit (CFI = 0.974, TLI = 0.959, RMSEA = 0.066, SRMR = 0.041, X 2 /df < 2.284, PCLOSE = 0.127), supporting the scale’s validity. The MMSS was found to be a reliable measure of cognitive self-regulation among Nigerian healthcare students. Conclusion: The findings support the use of the MMSS as a valid tool for assessing meta-memory and meta-concentration in Nigerian healthcare students. Given its strong psychometric properties, the MMSS can be applied in educational settings to enhance learning strategies and cognitive self-regulation. Meta-memory meta-cognition medical education psychometric validation MMSS Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Memory and cognition are fundamental to human functioning, shaping learning, decision-making, and overall well-being. These processes enable individuals to acquire, store, and retrieve information while also regulating their thoughts and adapting to new situations ( 1 ). Two interrelated concepts, meta-memory and meta-cognition, play a crucial role in self-regulating cognitive processes. Meta-memory refers to an individual's awareness and understanding of their memory abilities, including the capacity to assess, monitor, and control memory performance ( 2 ). This awareness allows people to develop strategies that enhance recall and optimise information retention. Meta-cognition, on the other hand, encompasses a broader set of cognitive control mechanisms that enable individuals to plan, monitor, and adjust their thought processes ( 3 , 4 ). Engaging in meta-cognitive strategies can improve problem-solving skills, self-regulation, and adaptive thinking, ultimately enhancing cognitive efficiency and daily functioning. Several scales have been developed to assess meta-memory and metacognition, including the meta-memory in adults questionnaire, the multidimensional metamemory skills scale (MDMS), the eyewitness metamemory scale (EMS), the multifactorial memory questionnaire (MMQ), the metacognition self-assessment scale (MSAS), and the mizan meta-memory meta-concentration scale for students (MMSS) ( 5 – 10 ). Developed by a multidisciplinary team of experts, the MMSS serves as a concise yet effective tool for evaluating key aspects of metacognition, particularly meta-memory and meta-concentration, among university students ( 10 ). What sets it apart is its ability to integrate both components within a single scale while remaining succinct. Unlike other instruments with extensive questionnaires that may overwhelm students with shorter attention spans, the MMSS offers a more streamlined yet comprehensive approach. Although the MMSS has been psychometrically validated in a study among Ethiopian university students and in a sample of Nurses in Saudi Arabia, its applicability to some other cultural and educational contexts remains unexamined ( 10 , 11 ). Psychometric validation is essential to ensure that an instrument maintains its reliability and validity across different populations, as cultural, linguistic, and educational differences can influence how individuals interpret and respond to assessment items. Currently, no research exists on the MMSS among Nigerian healthcare students, a population with distinct academic demands and cognitive challenges. Medical education is one of the most cognitively demanding fields globally, requiring students to rapidly acquire, retain, and apply vast amounts of information in high-pressure situations ( 12 , 13 ). Healthcare students with strong meta-memory skills are better equipped to retain medical concepts, recall essential information during exams, and adapt their learning approaches based on their self-assessment of memory strengths and weaknesses. Beyond academics, meta-memory plays a pivotal role in clinical reasoning and patient care. In medical and health sciences, diagnostic accuracy depends on the swift and precise recall of symptoms, disease mechanisms, and treatment protocols ( 14 ). A healthcare student with well-developed meta-memory can efficiently retrieve relevant medical information, make informed treatment decisions, and minimise errors. Conversely, poor meta-memory skills may lead to overconfidence in incorrect knowledge or underestimation of one’s memory abilities, increasing the likelihood of medical errors, misdiagnoses, and compromised patient safety. Moreover, the demands of lifelong learning in healthcare necessitate strong meta-memory abilities. Medicine is an ever-evolving field, requiring practitioners to continuously update their knowledge and skills ( 15 , 16 ). Those with well-developed meta-memory can effectively self-monitor their learning, identify areas requiring improvement, and engage in targeted professional development. This enhances long-term competence, ensuring that healthcare professionals remain adept at integrating new medical knowledge into practice. Given the intense cognitive demands of medical education, validating the MMSS among Nigerian healthcare students is essential. This study will evaluate its reliability and relevance, offering insights to enhance learning strategies, optimise memory regulation, and improve academic performance. Strengthening meta-memory skills can reduce cognitive errors, sharpen clinical decision-making, and ultimately promote safer, more competent healthcare delivery. METHODS Participants and Sample Size This study included 299 students enrolled in Medicine and Surgery, Dentistry, and Physiotherapy degree programmes at the University of Ibadan. The age range of participants was 16 to 41 years, with a mean age of 22.02 (SD = 3.137). The sample size was calculated using Slovin’s formula for a finite population: n = N / 1 + (Ne²), where n represents the required sample size, N is the total population, and e is the margin of error. Given a total population of 1,280 students and a margin of error of 5%, the computed sample size was 305 students. A total of 305 students were invited to participate; however, 299 completed the survey, yielding a response rate of 98%. For psychometric analysis, a subsample of 299 students was used, resulting in an n/p ratio of 33.2, where n is the sample size and p represents the number of items in the questionnaire undergoing psychometric evaluation. The inclusion criteria required participants to be actively enrolled in one of the listed healthcare programmes and to provide informed consent prior to participation. Study Design and Procedure This study employed a cross-sectional design with simple random sampling. The questionnaire, structured into three sections comprising informed consent, sociodemographic characteristics, and the nine-item MMSS was administered using Google Forms. The survey link was shared via email or WhatsApp to selected participants. Since English is the official language of instruction at the University of Ibadan, the questionnaire was provided in English. The informed consent section detailed the study objectives, procedures, and participants’ rights. Participants were encouraged to seek clarification from the researchers if they had any concerns before completing the survey. Survey Instrument The Mizan Meta-Memory and Meta-Concentration Scale (MMSS) was used to assess meta-memory and meta-concentration abilities among students. The MMSS is a psychometrically validated scale designed to measure two core components of metacognitive function: meta-memory and meta-concentration ( 17 ). Two previous studies have validated this scale among a sample of university students and health professionals respectively ( 10 , 11 ). The scale consists of nine items divided into two subscales: the Meta-Memory Subscale (BMMS) and the Meta-Concentration Subscale (BMCS). The BMMS consists of five items that assess self-perceived memory awareness and control, while the BMCS consists of four items evaluating awareness of concentration ability. Each item is rated on a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with higher scores indicating better meta-memory and meta-concentration abilities. The total MMSS score ranges from 9 to 45, with subscale scores ranging from 5 to 25 for BMMS and 4 to 20 for BMCS. Statistical Analysis All statistical analyses were conducted using SPSS Version 23.0. Descriptive statistics were computed for continuous variables using means, standard deviations, and ranges, while categorical variables were summarized as percentages and frequencies. The reliability of the MMSS was assessed using Cronbach’s alpha and McDonald’s Omega to evaluate internal consistency. Item-total correlations and Cronbach’s alpha if item deleted values were examined to assess item discrimination. Factor analysis was conducted to examine the underlying structure of the MMSS. The Kaiser-Meyer-Olkin (KMO) test and Bartlett’s Test of Sphericity were applied to assess the suitability of the dataset for factor analysis. Exploratory Factor Analysis (EFA) was performed to identify the factor structure of the MMSS, followed by Confirmatory Factor Analysis (CFA) to validate the factor solution. Structural Equation Modelling (SEM) was carried out to evaluate model fit, with two models tested: Model A representing the initial structure and Model B incorporating covariance adjustments. Model fit indices, including Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), were used to assess the adequacy of the factor structure. Inter-item correlations were analysed using Pearson’s correlation matrix to evaluate the relationships between MMSS items. Ethical Consideration Ethical approval for the study was obtained from the University of Ibadan / University College Hospital Ethics Committee (UI/UCH). Informed consent was obtained from all participants before data collection, and the study adhered to the ethical guidelines of the Declaration of Helsinki. Confidentiality and anonymity of participant data were strictly maintained. RESULTS Table 1 Participants characteristics Sociodemographic parameter Frequency Percent Age - Mean (SD): 22.02 (3.137) Minimum: 16 Maximum: 41 Sex Female 115 38.5 Male 184 61.5 Department Dentistry 42 14.0 Medicine and Surgery 218 72.9 Physiotherapy 39 13.0 Level 100 Level 41 13.7 200 Level 51 17.1 300 Level 59 19.7 400 Level 52 17.4 500 Level 48 16.1 600 Level 48 16.1 Participant’s characteristics are presented in Table 1 . A total of 299 university students participated in the study, with an age range of 16 to 41 years (Mean = 22.02, SD = 3.137). The sample was composed of 115 females (38.5%) and 184 males (61.5%). The participants were enrolled in various academic programmes: Dentistry (14.0%), Medicine and Surgery (72.9%), and Physiotherapy (13.0%). The sample was distributed across different academic levels, with students from 100 to 600 levels, ranging from 13.7–19.7% across levels. Table 2 Internal consistency: Cronbach’s alpha and McDonald omega of the MMSS Cronbach’s alpha McDonald’s omega MMSS 0.875 0.871 Meta-memory 0.808 0.805 Meta-concentration 0.857 0.859 The internal consistency of the MMSS was assessed using Cronbach’s alpha and McDonald’s omega. The overall MMSS demonstrated excellent reliability, with Cronbach’s alpha of 0.875 and McDonald’s omega of 0.871. The subscales also exhibited strong internal consistency: the Meta-Memory (BMMS) subscale had Cronbach’s alpha of 0.808 and McDonald’s omega of 0.805, while the Meta-Concentration (BMCS) subscale had values of 0.857 and 0.859, respectively (Table 2 ). Table 3 Sample size adequacy measures of the MMSS Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.880 Bartlett's Test of Sphericity X 2 = 1161.5 ; df = 36 ; p < 0.0001 Communalities 0.498–0.750 Determinant 0.019 Data confirming suitability for analysis are shown in Table 3 . The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.880, indicating that the dataset was highly suitable for factor analysis. Bartlett’s Test of Sphericity was statistically significant (χ² = 1161.5, df = 36, p < 0.0001), confirming that the correlation matrix was appropriate for factor extraction. Communalities ranged from 0.498 to 0.750, suggesting that the extracted factors retained a substantial proportion of the variance for each item. The determinant of the correlation matrix was 0.019, further supporting the absence of multicollinearity. Exploratory factor analysis revealed a two-factor solution, accounting for 63.04% of the total variance. The first factor (Meta-Memory, BMMS) explained 50.26% of the variance, while the second factor (Meta-Concentration, BMCS) accounted for 12.78% (Table 6 ). Factor loadings were strong, ranging from 0.638 to 0.854 for BMMS and from − 0.787 to -0.872 for BMCS (Table 7 ). The component correlation matrix indicated a moderate negative correlation between the two subscales (r = -0.560), suggesting some degree of independence while maintaining conceptual relatedness (Table 5 ). The scree plot analysis supported the two-factor solution, as the plot exhibited a sharp decline after the second component, indicating the presence of two dominant factors. This further validates the factor structure of the MMSS and aligns with the parallel analysis results (Fig. 1 ). Table 4 Descritive statistics of the MMSS Mean SD Cronbach's Alpha if Item Deleted Corrected Item-Total Correlation Skewness Statistic (SE) Z_skewness Kurtosis Statistic (SE) Z_kurtosis Percentage distribution across items 1 2 3 4 5 BMM_1 3.48 1.082 .868 .541 − .576 (.141) -4.085 − .118 (.281) -0.42 6.7 9.4 29.8 37.8 16.4 BMM_2 3.69 1.001 .861 .613 − .490 (.141) -3.475 − .158 (.281) -0.562 2.7 8.4 29.4 36.8 22.7 BMM_3 3.81 1.075 .870 .518 − .790 (.141) -5.603 .015 (.281) 0.053 3.7 9.4 18.7 38.8 29.4 BMM_4 3.38 1.021 .856 .672 − .209 (.141) -1.482 − .429 (.281) -1.527 2.7 14.7 35.8 31.4 14.4 BMM_5 3.46 1.056 .865 .577 − .242 (.141) -1.716 − .589 (.281) -2.096 3.3 15.1 32.1 31.4 18.1 BMC_1 3.22 1.096 .857 .665 − .162 (.141) -1.149 − .589 (.281) -2.096 6.7 18.1 34.4 27.8 13.0 BMC_2 3.05 1.032 .860 .630 .022 (.141) 0.156 − .530 (.281) -1.886 6.0 24.1 36.8 24.7 8.4 BMC_3 3.35 1.081 .854 .693 − .294 (.141) -2.085 − .540 (.281) -1.922 5.4 16.1 31.1 32.8 14.7 BMC_4 3.03 1.088 .861 .622 .113 (.141) 0.801 − .514 (.281) -1.829 7.7 23.1 39.1 18.7 11.4 BMMS 17.81 3.938 − .396 (.141) -2.809 .470 (.281) 1.673 BMCS 12.66 3.594 − .037 (.141) -0.262 − .306 (.281) 1.089 Table 5 Component Correlation Matrix Component 1 2 BMMS 1.000 − .560 BMCS − .560 1.000 Table 6 Total Variance Explained for the MMSS Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings a Total % of Variance Cumulative % Total % of Variance Cumulative % Total 1 4.524 50.262 50.262 4.524 50.262 50.262 3.735 2 1.150 12.776 63.038 1.150 12.776 63.038 3.828 3 .805 8.947 71.986 4 .608 6.760 78.745 5 .466 5.180 83.926 6 .434 4.817 88.743 7 .403 4.478 93.221 8 .318 3.530 96.750 9 .292 3.250 100.000 Table 7 Factor loading of the MMSS Items BMMS BMCS BMM_1 .680 BMM_2 .854 BMM_3 .817 BMM_4 .649 BMM_5 .638 BMC_1 − .872 BMC_2 − .800 BMC_3 − .852 BMC_4 − .787 Table 8 Fit statistics of the Mizan meta-memory and meta-concentration scale A B CFI 0.938 0.974 TLI 0.955 0.959 RMSEA 0.081 0.066 df 26 23 p value < 0.001 0.001 X 2 76.8 52.5 PCLOSE 0.008 0.127 GFI 0.944 0.965 SRMR 0.048 0.041 X 2 /df 2.952 2.284 Structural equation modeling (SEM) was conducted to evaluate model fit. In the initial model (Model A – Table 8 , Fig. 2 ), the comparative fit index (CFI) was 0.938, Tucker-Lewis index (TLI) was 0.955, root mean square error of approximation (RMSEA) was 0.081, and standardized root mean square residual (SRMR) was 0.048. After covariance adjustments, Model B (Table 8 , Fig. 3 ) demonstrated improved fit indices: CFI = 0.974, TLI = 0.959, RMSEA = 0.066, and SRMR = 0.041. The chi-square/degrees of freedom ratio (χ²/df) was also lower in Model B (2.284) compared to Model A (2.952), suggesting a better model fit. Table 9 Inter-item Correlation matrix BMM_1 BMM_2 BMM_3 BMM_4 BMM_5 BMC_1 BMC_2 BMC_3 BMC_4 BMM_1 .480 * .460 * .430 * .307 * .388 * .386 * .388 * .290 * BMM_2 .493 * .532 * .515 * .343 * .361 * .404 * .372 * BMM_3 .427 * .358 * .378 * .239 * .350 * .298 * BMM_4 .594 * .406 * .503 * .446 * .467 * BMM_5 .352 * .384 * .398 * .417 * BMC_1 .604 * .692 * .588 * BMC_2 .603 * .495 * BMC_3 .610 * BMC_4 * p < 0.001 Inter-item correlations ranged from 0.239 to 0.692, with all correlations statistically significant (p < 0.001). These findings indicate strong internal consistency, supporting the construct validity of the MMSS (Table 9 ). DISCUSSION This study aimed to validate the Mizan Meta-Memory and Meta-Concentration Scale (MMSS) and assess its psychometric properties among medical students. The findings indicate that the MMSS demonstrates strong internal consistency, a well-defined factor structure, and good model fit, suggesting its utility as a measure of metacognitive self-regulation. The MMSS exhibited high internal consistency, with a Cronbach’s alpha of 0.875 and McDonald’s omega of 0.871, both exceeding the established threshold of 0.7 for good reliability ( 18 ). The subscales also demonstrated strong reliability, with values of 0.808 for meta-memory (BMMS) and 0.857 for meta-concentration (BMCS). Previous study validated the MMSS among university students and confirmed its reliability and validity ( 10 ). These findings are comparable to previous studies analysing other scales, assessing metacognitive constructs, such as the Metacognitive Awareness Inventory (MAI) ( 19 ) and the Prospective and Retrospective Memory Questionnaire (PRMQ) ( 20 ), which reported similar or slightly lower reliability scores. The results suggest that the MMSS effectively captures individual differences in memory monitoring and concentration regulation. Given the importance of metacognition in academic performance, particularly in medical education ( 21 ), the scale could be useful for identifying students who may benefit from metacognitive training interventions. Exploratory Factor Analysis (EFA) confirmed a two-factor structure, with the meta-memory (BMMS) subscale accounting for 50.26% of variance and the meta-concentration (BMCS) subscale accounting for 12.78%. The Kaiser-Meyer-Olkin (KMO) measure of 0.880 and a significant Bartlett’s test (p < 0.0001) confirm that the dataset was suitable for factor analysis. The strong factor loadings observed (BMMS: 0.638–0.854, BMCS: -0.787 to -0.872) further validate the scale’s structure. The emergence of two distinct but related factors aligns with Flavell’s model of metacognition, which distinguishes between metacognitive knowledge (self-awareness of memory processes) and metacognitive regulation (the ability to control and sustain attention) ( 22 ). Manzar et al. (2018) similarly found that these two components could be effectively measured as separate but interdependent constructs, reinforcing the idea that metacognitive self-awareness and regulation of cognitive processes are distinct yet complementary abilities ( 10 ). Similarly, Nelson and Narens (1994) proposed a dual-component framework for metacognition, consisting of monitoring and control, which closely parallels the two-factor structure identified in the MMSS ( 23 ). While prior research has often examined meta-memory and concentration separately, the MMSS provides a more integrated approach, capturing the interaction between these constructs within a single scale. The moderate negative correlation observed between the two MMSS subscales in this study (r = -0.560) is consistent with findings from both Manzar et al. (2018) and Albougami et al. (2020), who suggested that excessive cognitive monitoring could interfere with real-time cognitive performance ( 10 , 11 ). This finding is consistent with dual-process theories of cognition, which propose that excessive cognitive monitoring can interfere with real-time cognitive performance ( 24 ). Structural Equation Modelling (SEM) confirmed the validity of the MMSS’s two-factor structure. The initial model (Model A) demonstrated acceptable fit (CFI = 0.938, RMSEA = 0.081, SRMR = 0.048), while Model B, which included covariance adjustments, showed an improved fit (CFI = 0.974, RMSEA = 0.066, SRMR = 0.041). These values align with established psychometric criteria for good model fit, particularly the recommendation that CFI and TLI should exceed 0.95, and RMSEA should be below 0.08 ( 25 ). The improved fit following covariance adjustments suggests that correlated error terms may reflect shared variance due to item wording or conceptual overlap. Beyond its value as a research tool, the MMSS has practical applications in medical education. Given the well-documented relationship between metacognition and academic success, as well as its role in reducing burnout among medical students ( 26 ), the scale could serve as a diagnostic tool for identifying students who may require additional support in memory encoding or attention control. Manzar et al. (2018) emphasized the potential for using MMSS scores to guide cognitive training interventions, such as mindfulness-based strategies for enhancing sustained attention or spaced repetition techniques for improving memory retention ( 10 ). Similarly, Albougami et al. (2020) highlighted the importance of metacognitive screening in healthcare professionals, suggesting that addressing deficits in metamemory and meta concentration could enhance patient care by improving clinical decision-making and task performance ( 11 ). Although this study provides strong psychometric evidence for the MMSS, some limitations should be acknowledged. First, test-retest reliability, convergent validity, divergent validity and concurrent validity were not assessed. Second, the sample consisted primarily of healthcare students, limiting the generalisability of the findings to other student populations. Future studies should validate the scale across diverse disciplines and non-student populations. Finally, although model fit indices were strong, the moderate negative correlation between BMMS and BMCS suggests that further exploration of their interaction is warranted. Experimental studies could investigate whether improving one metacognitive component (e.g., memory awareness) influences the other (e.g., concentration control), providing insights into metacognitive training interventions. The MMSS demonstrates strong psychometric properties, with high reliability, a well-defined factor structure, and good model fit. The two-factor structure aligns with established theoretical models of metacognition, confirming the distinct but related nature of meta-memory and meta-concentration. Given the importance of metacognitive self-regulation in medical education and cognitive performance, the MMSS represents a valuable tool for both research and applied settings. Future studies should explore its predictive validity, cross-cultural applicability, and potential role in cognitive training interventions. Abbreviations MMSS Mizan meta-memory and meta-concentration scale for students BMMS Brief meta-memory subscale BMCS Brief meta-concentration subscale CFA Confirmatory factor analysis EFA Exploratory factor analysis SEM Structural equation modelling CFI Comparative fit index TLI Tucker-Lewis index RMSEA Root mean square error of approximation SRMR Standardized root mean square residual BMM_1 - BMM_5 Brief meta memory items 1 to 5 BMC_1 - BMC_4 Brief meta concentration items 1 to 4 Declarations CONSENT FOR PUBLICATION The participants provided informed written consent. ETHICAL CONSIDERATION Ethical approval for the study was obtained from the University of Ibadan / University College Hospital Ethics Committee (UI/UCH). Informed consent was obtained from all participants before data collection, and the study adhered to the ethical guidelines of the Declaration of Helsinki. Confidentiality and anonymity of participant data were strictly maintained. AVAILABILITY OF DATA AND MATERIALS This information will be made available upon a reasonable request from the corresponding author. CONFLICT OF INTEREST The authors declare that they have no conflicts of interest with respect to the publication of this article. FUNDING The authors received no external funding for this work. CLINICAL TRIAL NUMBER Not Applicable ACKNOWLEDGEMENT Nil AUTHORS’ CONTRIBUTION AAA conceptualised the study and analysed the data. AAA AOO and DMA wrote the manuscript. All the authors read and approved the manuscript. References Amoah DK. Advances in the understanding and enhancement of the human cognitive functions of learning and memory. Brain Sci Adv [Internet]. 2022 Dec 1 [cited 2025 Mar 16];8(4):276–97. Available from: https://www.sciopen.com/article/ 10.26599/BSA.2022.9050023 Brandt M, de Carvalho RLS, Belfort T, Dourado MCN. Metamemory monitoring in Alzheimer’s disease A systematic review. Dement Neuropsychol. 2018;12(4):337–52. Drigas A, Mitsea E. The 8 Pillars of Metacognition. Int J Emerg Technol Learn IJET [Internet]. 2020 Nov 16 [cited 2025 Mar 16];15(21):162–78. Available from: https://online-journals.org/index.php/i-jet/article/view/14907 Drigas A, Mitsea E. 8 Pillars X 8 Layers Model of Metacognition: Educational Strategies, Exercises &Trainings. Int J Online Biomed Eng IJOE [Internet]. 2021 Aug 16 [cited 2025 Mar 16];17(08):115–34. Available from: https://online-journals.org/index.php/i-joe/article/view/23563 McDonough IM, McDougall GJ, LaRocca M, Dalmida SG, Arheart KL. Refining the metamemory in adulthood questionnaire: a 20-item version of change and capacity designed for research and clinical settings. Aging Ment Health [Internet]. 2020 Jul [cited 2025 Mar 16];24(7):1054–63. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779492/ Kancharla K, Kanagaraj S, Ramdoss S, Gopal CNR. Development and validation of the multi-dimensional metamemory skills (MDMS) scale for students in an Indian sample. Cogn Process. 2023;24(3):375–86. Saraiva R, Boeijen I, Hope L, Horselenberg R, Sauerland M, Koppen P. Development and validation of the Eyewitness Metamemory Scale. Appl Cogn Psychol. 2019;33. Troyer AK, Rich JB. Psychometric properties of a new metamemory questionnaire for older adults. J Gerontol B Psychol Sci Soc Sci. 2002;57(1):P19–27. Faustino B, Branco Vasco A, Oliveira J, Lopes P, Fonseca I. Metacognitive self-assessment scale: psychometric properties and clinical implications. Appl Neuropsychol Adult. 2021;28(5):596–606. Manzar MD, Albougami A, Salahuddin M, Sony P, Spence DW, Pandi-Perumal SR. The Mizan meta-memory and meta-concentration scale for students (MMSS): a test of its psychometric validity in a sample of university students. BMC Psychol [Internet]. 2018 Dec [cited 2025 Mar 15];6(1):59. Available from: https://bmcpsychology.biomedcentral.com/articles/ 10.1186/s40359-018-0275-7 Albougami A, Manzar D, Almansour AM, Alrasheadi BA. Metamemory and Metaconcentration Scale (MMS) for Health Professionals: A Psychometric Investigation in Nurses. Voltmer E, Köslich-Strumann S, Voltmer JB, Kötter T. Stress and behavior patterns throughout medical education – a six year longitudinal study. BMC Med Educ [Internet]. 2021 Aug 28 [cited 2025 Mar 16];21(1):454. Available from: https://doi.org/10.1186/s12909-021-02862-x Esan O, Esan A, Folasire A, Oluwajulugbe P. Mental health and wellbeing of medical students in Nigeria: a systematic review. Int Rev Psychiatry Abingdon Engl. 2019;31(7–8):661–72. Balogh EP, Miller BT, Ball JR, Care C. on DE in H, Services B on HC, Medicine I of, The Diagnostic Process. In: Improving Diagnosis in Health Care [Internet]. National Academies Press (US); 2015 [cited 2025 Mar 16]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK338593/ Boudoulas KD, Triposkiadis F, Stefanadis C, Boudoulas H. The endlessness evolution of medicine, continuous increase in life expectancy and constant role of the physician. Hell J Cardiol HJC Hell Kardiologike Epitheorese. 2017;58(5):322–30. Natterson-Horowitz B, Aktipis A, Fox M, Gluckman PD, Low FM, Mace R et al. The future of evolutionary medicine: sparking innovation in biomedicine and public health. Front Sci [Internet]. 2023 Feb 28 [cited 2025 Mar 16];1. Available from: https://www.frontiersin.org/journals/science/articles/ 10.3389/fsci.2023.997136/full Klusmann V, Evers A, Schwarzer R, Heuser I. A brief questionnaire on metacognition: Psychometric properties. Aging Ment Health [Internet]. 2011 Nov [cited 2025 Mar 15];15(8):1052–62. Available from: http://www.tandfonline.com/doi/abs/ 10.1080/13607863.2011.583624 Nunnally JC, Bernstein IH. (1994). Psychometric theory (3rd ed.). New York McGraw-Hill. - References - Scientific Research Publishing [Internet]. [cited 2025 Mar 16]. Available from: https://www.scirp.org/reference/referencespapers?referenceid=1017362 Schraw G, Dennison RS. Assessing Metacognitive Awareness. Contemp Educ Psychol [Internet]. 1994 Oct [cited 2025 Mar 15];19(4):460–75. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0361476X84710332 Crawford JR, Smith G, Maylor EA, Della Sala S, Logie RH. The Prospective and Retrospective Memory Questionnaire (PRMQ): Normative data and latent structure in a large non-clinical sample. Memory. 2003;11(3):261–75. Siqueira MAM, Gonçalves JP, Mendonça VS, Kobayasi R, Arantes-Costa FM, Tempski PZ et al. Relationship between metacognitive awareness and motivation to learn in medical students. BMC Med Educ [Internet]. 2020 Oct 30 [cited 2025 Mar 15];20(1):393. Available from: https://doi.org/10.1186/s12909-020-02318-8 Flavell JH. Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. Am Psychol [Internet]. 1979 Oct [cited 2025 Mar 15];34(10):906–11. Available from: https://doi.apa.org/doi/10.1037/0003-066X.34.10.906 Nelson TO. Metamemory: A Theoretical Framework and New Findings. In: Psychology of Learning and Motivation [Internet]. Elsevier; 1990 [cited 2025 Mar 15]. pp. 125–73. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0079742108600535 Evans JSBT, Stanovich KE. Dual-Process Theories of Higher Cognition: Advancing the Debate. Perspect Psychol Sci J Assoc Psychol Sci. 2013;8(3):223–41. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model Multidiscip J [Internet]. 1999 Jan 1 [cited 2025 Mar 15];6(1):1–55. Available from: https://doi.org/10.1080/10705519909540118 Cleary TJ, Zimmerman BJ. Self-Regulation Empowerment Program: A School-Based Program to Enhance Self-Regulated and Self-Motivated Cycles of Student Learning. Psychol Sch. 2004;41(5):537–50. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2026 Read the published version in BMC Psychology → Version 1 posted Editorial decision: Revision requested 17 Jun, 2025 Reviews received at journal 29 May, 2025 Reviews received at journal 25 May, 2025 Reviews received at journal 23 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 05 May, 2025 Editor invited by journal 22 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 16 Mar, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6238647","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":429524036,"identity":"3f9ddaed-410f-42f9-901c-395fd3ac558b","order_by":0,"name":"Adeniyi Abraham Adesola","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFCCBCAuAOIDIGRgIwcSO/CAoBYDuJY0Y7CWBGK1AMHhxAaYIC7A3558+MMHAzt5vuO9Bw/zFDCnzw87/BBoi52cbgN2LRJnnqVJzjBINpx55lzCYR4DttyNt9MMgFqSjc0O4LDmRo4ZM48BM+OGGzkGQC08uRtnJ4C0HEjchkOL/I38z595DOrtN9x/A9IikW44O/0DXi0GN3IYpHkMDiduuMED0mKQIC+dg98WwzPPzIB+OZ4880yOwcE5BgmGG6RzCg4kGOD2i9zx5McfPlRU2/YdP2P84c2f//Lys9M3A0Xs5HB6Hxkw8YCcClZpQIRyEGD8ASTkG4hUPQpGwSgYBSMGAAATR2mKmJITpgAAAABJRU5ErkJggg==","orcid":"","institution":"College of Medicine, University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Adeniyi","middleName":"Abraham","lastName":"Adesola","suffix":""},{"id":429524039,"identity":"839747b9-6069-4dd2-b5c2-b92591f2b1e9","order_by":1,"name":"Abigail Olawumi Oyedokun","email":"","orcid":"","institution":"College of Medicine, University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"Olawumi","lastName":"Oyedokun","suffix":""},{"id":429524041,"identity":"009afa25-004b-45ac-9ea5-76852a386d5b","order_by":2,"name":"David Mobolaji Akoki","email":"","orcid":"","institution":"College of Medicine, University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"Mobolaji","lastName":"Akoki","suffix":""}],"badges":[],"createdAt":"2025-03-16 16:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6238647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6238647/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-026-04647-7","type":"published","date":"2026-04-25T15:59:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78734781,"identity":"18361c78-cc4e-4c44-8199-8d0068f2297d","added_by":"auto","created_at":"2025-03-18 08:04:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eScree Plot of the MMSS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6238647/v1/3bfb99abcabfe206f50ce2af.png"},{"id":78734782,"identity":"4af6fd02-b26d-47e3-b767-78e0c3bce833","added_by":"auto","created_at":"2025-03-18 08:04:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSEM Image of the MMSS (Model A)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6238647/v1/995826a68fe53019a18a0a6e.png"},{"id":78734784,"identity":"e3f2c7a2-96bc-4196-b8a9-c222fd02ef80","added_by":"auto","created_at":"2025-03-18 08:04:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSEM B image of the MMSS (Model B – with Covariance Adjustments)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6238647/v1/3fbe1ceccda122dc5eeca6ec.png"},{"id":108008665,"identity":"feb345a5-390c-4e00-bfaa-fdd256d59a2f","added_by":"auto","created_at":"2026-04-28 13:07:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6238647/v1/32fb6a6f-d060-4953-b228-304ae80381d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePsychometric Validation of the Mizan Meta-memory and Meta-Concentration Scale for Students (MMSS) Among a Sample of Health Care Students in Nigeria\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMemory and cognition are fundamental to human functioning, shaping learning, decision-making, and overall well-being. These processes enable individuals to acquire, store, and retrieve information while also regulating their thoughts and adapting to new situations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Two interrelated concepts, meta-memory and meta-cognition, play a crucial role in self-regulating cognitive processes. Meta-memory refers to an individual's awareness and understanding of their memory abilities, including the capacity to assess, monitor, and control memory performance (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This awareness allows people to develop strategies that enhance recall and optimise information retention. Meta-cognition, on the other hand, encompasses a broader set of cognitive control mechanisms that enable individuals to plan, monitor, and adjust their thought processes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Engaging in meta-cognitive strategies can improve problem-solving skills, self-regulation, and adaptive thinking, ultimately enhancing cognitive efficiency and daily functioning.\u003c/p\u003e \u003cp\u003eSeveral scales have been developed to assess meta-memory and metacognition, including the meta-memory in adults questionnaire, the multidimensional metamemory skills scale (MDMS), the eyewitness metamemory scale (EMS), the multifactorial memory questionnaire (MMQ), the metacognition self-assessment scale (MSAS), and the mizan meta-memory meta-concentration scale for students (MMSS) (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDeveloped by a multidisciplinary team of experts, the MMSS serves as a concise yet effective tool for evaluating key aspects of metacognition, particularly meta-memory and meta-concentration, among university students (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). What sets it apart is its ability to integrate both components within a single scale while remaining succinct. Unlike other instruments with extensive questionnaires that may overwhelm students with shorter attention spans, the MMSS offers a more streamlined yet comprehensive approach. Although the MMSS has been psychometrically validated in a study among Ethiopian university students and in a sample of Nurses in Saudi Arabia, its applicability to some other cultural and educational contexts remains unexamined (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Psychometric validation is essential to ensure that an instrument maintains its reliability and validity across different populations, as cultural, linguistic, and educational differences can influence how individuals interpret and respond to assessment items.\u003c/p\u003e \u003cp\u003eCurrently, no research exists on the MMSS among Nigerian healthcare students, a population with distinct academic demands and cognitive challenges. Medical education is one of the most cognitively demanding fields globally, requiring students to rapidly acquire, retain, and apply vast amounts of information in high-pressure situations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Healthcare students with strong meta-memory skills are better equipped to retain medical concepts, recall essential information during exams, and adapt their learning approaches based on their self-assessment of memory strengths and weaknesses.\u003c/p\u003e \u003cp\u003eBeyond academics, meta-memory plays a pivotal role in clinical reasoning and patient care. In medical and health sciences, diagnostic accuracy depends on the swift and precise recall of symptoms, disease mechanisms, and treatment protocols (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A healthcare student with well-developed meta-memory can efficiently retrieve relevant medical information, make informed treatment decisions, and minimise errors. Conversely, poor meta-memory skills may lead to overconfidence in incorrect knowledge or underestimation of one\u0026rsquo;s memory abilities, increasing the likelihood of medical errors, misdiagnoses, and compromised patient safety.\u003c/p\u003e \u003cp\u003eMoreover, the demands of lifelong learning in healthcare necessitate strong meta-memory abilities. Medicine is an ever-evolving field, requiring practitioners to continuously update their knowledge and skills (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Those with well-developed meta-memory can effectively self-monitor their learning, identify areas requiring improvement, and engage in targeted professional development. This enhances long-term competence, ensuring that healthcare professionals remain adept at integrating new medical knowledge into practice.\u003c/p\u003e \u003cp\u003eGiven the intense cognitive demands of medical education, validating the MMSS among Nigerian healthcare students is essential. This study will evaluate its reliability and relevance, offering insights to enhance learning strategies, optimise memory regulation, and improve academic performance. Strengthening meta-memory skills can reduce cognitive errors, sharpen clinical decision-making, and ultimately promote safer, more competent healthcare delivery.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Sample Size\u003c/h2\u003e \u003cp\u003eThis study included 299 students enrolled in Medicine and Surgery, Dentistry, and Physiotherapy degree programmes at the University of Ibadan. The age range of participants was 16 to 41 years, with a mean age of 22.02 (SD\u0026thinsp;=\u0026thinsp;3.137). The sample size was calculated using Slovin\u0026rsquo;s formula for a finite population: n\u0026thinsp;=\u0026thinsp;N / 1 + (Ne\u0026sup2;), where n represents the required sample size, N is the total population, and e is the margin of error. Given a total population of 1,280 students and a margin of error of 5%, the computed sample size was 305 students. A total of 305 students were invited to participate; however, 299 completed the survey, yielding a response rate of 98%. For psychometric analysis, a subsample of 299 students was used, resulting in an n/p ratio of 33.2, where n is the sample size and p represents the number of items in the questionnaire undergoing psychometric evaluation. The inclusion criteria required participants to be actively enrolled in one of the listed healthcare programmes and to provide informed consent prior to participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Procedure\u003c/h3\u003e\n\u003cp\u003eThis study employed a cross-sectional design with simple random sampling. The questionnaire, structured into three sections comprising informed consent, sociodemographic characteristics, and the nine-item MMSS was administered using Google Forms. The survey link was shared via email or WhatsApp to selected participants. Since English is the official language of instruction at the University of Ibadan, the questionnaire was provided in English. The informed consent section detailed the study objectives, procedures, and participants\u0026rsquo; rights. Participants were encouraged to seek clarification from the researchers if they had any concerns before completing the survey.\u003c/p\u003e\n\u003ch3\u003eSurvey Instrument\u003c/h3\u003e\n\u003cp\u003eThe Mizan Meta-Memory and Meta-Concentration Scale (MMSS) was used to assess meta-memory and meta-concentration abilities among students. The MMSS is a psychometrically validated scale designed to measure two core components of metacognitive function: meta-memory and meta-concentration (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Two previous studies have validated this scale among a sample of university students and health professionals respectively (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The scale consists of nine items divided into two subscales: the Meta-Memory Subscale (BMMS) and the Meta-Concentration Subscale (BMCS). The BMMS consists of five items that assess self-perceived memory awareness and control, while the BMCS consists of four items evaluating awareness of concentration ability. Each item is rated on a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with higher scores indicating better meta-memory and meta-concentration abilities. The total MMSS score ranges from 9 to 45, with subscale scores ranging from 5 to 25 for BMMS and 4 to 20 for BMCS.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using SPSS Version 23.0. Descriptive statistics were computed for continuous variables using means, standard deviations, and ranges, while categorical variables were summarized as percentages and frequencies. The reliability of the MMSS was assessed using Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s Omega to evaluate internal consistency. Item-total correlations and Cronbach\u0026rsquo;s alpha if item deleted values were examined to assess item discrimination.\u003c/p\u003e \u003cp\u003eFactor analysis was conducted to examine the underlying structure of the MMSS. The Kaiser-Meyer-Olkin (KMO) test and Bartlett\u0026rsquo;s Test of Sphericity were applied to assess the suitability of the dataset for factor analysis. Exploratory Factor Analysis (EFA) was performed to identify the factor structure of the MMSS, followed by Confirmatory Factor Analysis (CFA) to validate the factor solution. Structural Equation Modelling (SEM) was carried out to evaluate model fit, with two models tested: Model A representing the initial structure and Model B incorporating covariance adjustments. Model fit indices, including Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), were used to assess the adequacy of the factor structure. Inter-item correlations were analysed using Pearson\u0026rsquo;s correlation matrix to evaluate the relationships between MMSS items.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Consideration\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e for the study was obtained from the University of Ibadan / University College Hospital Ethics Committee (UI/UCH). Informed consent was obtained from all participants before data collection, and the study adhered to the ethical guidelines of the Declaration of Helsinki. Confidentiality and anonymity of participant data were strictly maintained.\u003c/p\u003e \u003c/p\u003e"},{"header":"RESULTS","content":"\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\u003eParticipants characteristics\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge - Mean (SD): 22.02 (3.137)\u003c/p\u003e \u003cp\u003eMinimum: 16\u003c/p\u003e \u003cp\u003eMaximum: 41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDentistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedicine and Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.1\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\u003eParticipant\u0026rsquo;s characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 299 university students participated in the study, with an age range of 16 to 41 years (Mean\u0026thinsp;=\u0026thinsp;22.02, SD\u0026thinsp;=\u0026thinsp;3.137). The sample was composed of 115 females (38.5%) and 184 males (61.5%). The participants were enrolled in various academic programmes: Dentistry (14.0%), Medicine and Surgery (72.9%), and Physiotherapy (13.0%). The sample was distributed across different academic levels, with students from 100 to 600 levels, ranging from 13.7\u0026ndash;19.7% across levels.\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\u003eInternal consistency: Cronbach\u0026rsquo;s alpha and McDonald omega of the MMSS\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\u0026nbsp;\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\u003eMcDonald\u0026rsquo;s omega\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeta-memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeta-concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\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\u003eThe internal consistency of the MMSS was assessed using Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega. The overall MMSS demonstrated excellent reliability, with Cronbach\u0026rsquo;s alpha of 0.875 and McDonald\u0026rsquo;s omega of 0.871. The subscales also exhibited strong internal consistency: the Meta-Memory (BMMS) subscale had Cronbach\u0026rsquo;s alpha of 0.808 and McDonald\u0026rsquo;s omega of 0.805, while the Meta-Concentration (BMCS) subscale had values of 0.857 and 0.859, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eSample size adequacy measures of the MMSS\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\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett's Test of Sphericity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1161.5 ; df\u0026thinsp;=\u0026thinsp;36 ; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.498\u0026ndash;0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeterminant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019\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\u003eData confirming suitability for analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.880, indicating that the dataset was highly suitable for factor analysis. Bartlett\u0026rsquo;s Test of Sphericity was statistically significant (χ\u0026sup2; = 1161.5, df\u0026thinsp;=\u0026thinsp;36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), confirming that the correlation matrix was appropriate for factor extraction. Communalities ranged from 0.498 to 0.750, suggesting that the extracted factors retained a substantial proportion of the variance for each item. The determinant of the correlation matrix was 0.019, further supporting the absence of multicollinearity.\u003c/p\u003e \u003cp\u003eExploratory factor analysis revealed a two-factor solution, accounting for 63.04% of the total variance. The first factor (Meta-Memory, BMMS) explained 50.26% of the variance, while the second factor (Meta-Concentration, BMCS) accounted for 12.78% (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Factor loadings were strong, ranging from 0.638 to 0.854 for BMMS and from \u0026minus;\u0026thinsp;0.787 to -0.872 for BMCS (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The component correlation matrix indicated a moderate negative correlation between the two subscales (r = -0.560), suggesting some degree of independence while maintaining conceptual relatedness (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe scree plot analysis supported the two-factor solution, as the plot exhibited a sharp decline after the second component, indicating the presence of two dominant factors. This further validates the factor structure of the MMSS and aligns with the parallel analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescritive statistics of the MMSS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\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\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach's Alpha if Item Deleted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCorrected Item-Total Correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness Statistic (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ_skewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKurtosis Statistic (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eZ_kurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003ePercentage distribution across items\u003c/p\u003e \u003cp\u003e1 2 3 4 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.576 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.118 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.490 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.158 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.790 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.015 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.209 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.429 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.242 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.589 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.162 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.589 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.022 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.530 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.294 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.540 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.113 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.514 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.396 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.470 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.037 (.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.306 (.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\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=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComponent Correlation Matrix\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \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\u003eTotal Variance Explained for the MMSS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eInitial Eigenvalues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eExtraction Sums of Squared Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRotation Sums of Squared Loadings\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% of Variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.828\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\u003e.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \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=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor loading of the MMSS\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\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMCS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.680\u003c/p\u003e \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\u003eBMM_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.854\u003c/p\u003e \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\u003eBMM_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.817\u003c/p\u003e \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\u003eBMM_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.649\u003c/p\u003e \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\u003eBMM_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.638\u003c/p\u003e \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\u003eBMC_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.787\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 \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \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\u003eFit statistics of the Mizan meta-memory and meta-concentration scale\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\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.974\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\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959\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\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCLOSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.965\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\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.284\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\u003eStructural equation modeling (SEM) was conducted to evaluate model fit. In the initial model (Model A \u0026ndash; Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the comparative fit index (CFI) was 0.938, Tucker-Lewis index (TLI) was 0.955, root mean square error of approximation (RMSEA) was 0.081, and standardized root mean square residual (SRMR) was 0.048.\u003c/p\u003e \u003cp\u003eAfter covariance adjustments, Model B (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrated improved fit indices: CFI\u0026thinsp;=\u0026thinsp;0.974, TLI\u0026thinsp;=\u0026thinsp;0.959, RMSEA\u0026thinsp;=\u0026thinsp;0.066, and SRMR\u0026thinsp;=\u0026thinsp;0.041. The chi-square/degrees of freedom ratio (χ\u0026sup2;/df) was also lower in Model B (2.284) compared to Model A (2.952), suggesting a better model fit.\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\u003eInter-item Correlation matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMM_1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMM_2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMM_3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBMM_4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMM_5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBMC_1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBMC_2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBMC_3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBMC_4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.480\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.460\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.430\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.307\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.388\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.386\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.388\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.290\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_2\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.493\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.532\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.515\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.343\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.361\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.404\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.372\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.427\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.358\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.378\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.239\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.350\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.298\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.594\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.406\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.503\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.446\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.467\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMM_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.352\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.384\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.398\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.417\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.604\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.692\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.588\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.603\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.495\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.610\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMC_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInter-item correlations ranged from 0.239 to 0.692, with all correlations statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate strong internal consistency, supporting the construct validity of the MMSS (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to validate the Mizan Meta-Memory and Meta-Concentration Scale (MMSS) and assess its psychometric properties among medical students. The findings indicate that the MMSS demonstrates strong internal consistency, a well-defined factor structure, and good model fit, suggesting its utility as a measure of metacognitive self-regulation.\u003c/p\u003e \u003cp\u003eThe MMSS exhibited high internal consistency, with a Cronbach\u0026rsquo;s alpha of 0.875 and McDonald\u0026rsquo;s omega of 0.871, both exceeding the established threshold of 0.7 for good reliability (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The subscales also demonstrated strong reliability, with values of 0.808 for meta-memory (BMMS) and 0.857 for meta-concentration (BMCS). Previous study validated the MMSS among university students and confirmed its reliability and validity (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). These findings are comparable to previous studies analysing other scales, assessing metacognitive constructs, such as the Metacognitive Awareness Inventory (MAI) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and the Prospective and Retrospective Memory Questionnaire (PRMQ) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), which reported similar or slightly lower reliability scores. The results suggest that the MMSS effectively captures individual differences in memory monitoring and concentration regulation. Given the importance of metacognition in academic performance, particularly in medical education (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), the scale could be useful for identifying students who may benefit from metacognitive training interventions.\u003c/p\u003e \u003cp\u003eExploratory Factor Analysis (EFA) confirmed a two-factor structure, with the meta-memory (BMMS) subscale accounting for 50.26% of variance and the meta-concentration (BMCS) subscale accounting for 12.78%. The Kaiser-Meyer-Olkin (KMO) measure of 0.880 and a significant Bartlett\u0026rsquo;s test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) confirm that the dataset was suitable for factor analysis. The strong factor loadings observed (BMMS: 0.638\u0026ndash;0.854, BMCS: -0.787 to -0.872) further validate the scale\u0026rsquo;s structure.\u003c/p\u003e \u003cp\u003eThe emergence of two distinct but related factors aligns with Flavell\u0026rsquo;s model of metacognition, which distinguishes between metacognitive knowledge (self-awareness of memory processes) and metacognitive regulation (the ability to control and sustain attention) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Manzar et al. (2018) similarly found that these two components could be effectively measured as separate but interdependent constructs, reinforcing the idea that metacognitive self-awareness and regulation of cognitive processes are distinct yet complementary abilities (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Similarly, Nelson and Narens (1994) proposed a dual-component framework for metacognition, consisting of monitoring and control, which closely parallels the two-factor structure identified in the MMSS (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). While prior research has often examined meta-memory and concentration separately, the MMSS provides a more integrated approach, capturing the interaction between these constructs within a single scale.\u003c/p\u003e \u003cp\u003eThe moderate negative correlation observed between the two MMSS subscales in this study (r = -0.560) is consistent with findings from both Manzar et al. (2018) and Albougami et al. (2020), who suggested that excessive cognitive monitoring could interfere with real-time cognitive performance (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This finding is consistent with dual-process theories of cognition, which propose that excessive cognitive monitoring can interfere with real-time cognitive performance (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStructural Equation Modelling (SEM) confirmed the validity of the MMSS\u0026rsquo;s two-factor structure. The initial model (Model A) demonstrated acceptable fit (CFI\u0026thinsp;=\u0026thinsp;0.938, RMSEA\u0026thinsp;=\u0026thinsp;0.081, SRMR\u0026thinsp;=\u0026thinsp;0.048), while Model B, which included covariance adjustments, showed an improved fit (CFI\u0026thinsp;=\u0026thinsp;0.974, RMSEA\u0026thinsp;=\u0026thinsp;0.066, SRMR\u0026thinsp;=\u0026thinsp;0.041). These values align with established psychometric criteria for good model fit, particularly the recommendation that CFI and TLI should exceed 0.95, and RMSEA should be below 0.08 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The improved fit following covariance adjustments suggests that correlated error terms may reflect shared variance due to item wording or conceptual overlap.\u003c/p\u003e \u003cp\u003eBeyond its value as a research tool, the MMSS has practical applications in medical education. Given the well-documented relationship between metacognition and academic success, as well as its role in reducing burnout among medical students (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), the scale could serve as a diagnostic tool for identifying students who may require additional support in memory encoding or attention control. Manzar et al. (2018) emphasized the potential for using MMSS scores to guide cognitive training interventions, such as mindfulness-based strategies for enhancing sustained attention or spaced repetition techniques for improving memory retention (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Similarly, Albougami et al. (2020) highlighted the importance of metacognitive screening in healthcare professionals, suggesting that addressing deficits in metamemory and meta concentration could enhance patient care by improving clinical decision-making and task performance (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough this study provides strong psychometric evidence for the MMSS, some limitations should be acknowledged. First, test-retest reliability, convergent validity, divergent validity and concurrent validity were not assessed. Second, the sample consisted primarily of healthcare students, limiting the generalisability of the findings to other student populations. Future studies should validate the scale across diverse disciplines and non-student populations. Finally, although model fit indices were strong, the moderate negative correlation between BMMS and BMCS suggests that further exploration of their interaction is warranted. Experimental studies could investigate whether improving one metacognitive component (e.g., memory awareness) influences the other (e.g., concentration control), providing insights into metacognitive training interventions.\u003c/p\u003e \u003cp\u003eThe MMSS demonstrates strong psychometric properties, with high reliability, a well-defined factor structure, and good model fit. The two-factor structure aligns with established theoretical models of metacognition, confirming the distinct but related nature of meta-memory and meta-concentration. Given the importance of metacognitive self-regulation in medical education and cognitive performance, the MMSS represents a valuable tool for both research and applied settings. Future studies should explore its predictive validity, cross-cultural applicability, and potential role in cognitive training interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMizan meta-memory and meta-concentration scale for students\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrief meta-memory subscale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrief meta-concentration subscale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructural equation modelling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative fit index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTucker-Lewis index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot mean square error of approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized root mean square residual\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMM_1 - BMM_5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrief meta memory items 1 to 5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMC_1 - BMC_4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrief meta concentration items 1 to 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants provided informed written consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL CONSIDERATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the University of Ibadan / University College Hospital Ethics Committee (UI/UCH). Informed consent was obtained from all participants before data collection, and the study adhered to the ethical guidelines of the Declaration of Helsinki. Confidentiality and anonymity of participant data were strictly maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis information will be made available upon a reasonable request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest with respect to the publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no external funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNil\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS’ CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAA conceptualised the study and analysed the data. AAA AOO and DMA wrote the manuscript. All the authors read and approved the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmoah DK. Advances in the understanding and enhancement of the human cognitive functions of learning and memory. Brain Sci Adv [Internet]. 2022 Dec 1 [cited 2025 Mar 16];8(4):276\u0026ndash;97. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciopen.com/article/\u003c/span\u003e\u003cspan address=\"https://www.sciopen.com/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.26599/BSA.2022.9050023\u003c/span\u003e\u003cspan address=\"10.26599/BSA.2022.9050023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrandt M, de Carvalho RLS, Belfort T, Dourado MCN. Metamemory monitoring in Alzheimer\u0026rsquo;s disease A systematic review. Dement Neuropsychol. 2018;12(4):337\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrigas A, Mitsea E. The 8 Pillars of Metacognition. Int J Emerg Technol Learn IJET [Internet]. 2020 Nov 16 [cited 2025 Mar 16];15(21):162\u0026ndash;78. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://online-journals.org/index.php/i-jet/article/view/14907\u003c/span\u003e\u003cspan address=\"https://online-journals.org/index.php/i-jet/article/view/14907\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrigas A, Mitsea E. 8 Pillars X 8 Layers Model of Metacognition: Educational Strategies, Exercises \u0026amp;Trainings. Int J Online Biomed Eng IJOE [Internet]. 2021 Aug 16 [cited 2025 Mar 16];17(08):115\u0026ndash;34. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://online-journals.org/index.php/i-joe/article/view/23563\u003c/span\u003e\u003cspan address=\"https://online-journals.org/index.php/i-joe/article/view/23563\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonough IM, McDougall GJ, LaRocca M, Dalmida SG, Arheart KL. Refining the metamemory in adulthood questionnaire: a 20-item version of change and capacity designed for research and clinical settings. Aging Ment Health [Internet]. 2020 Jul [cited 2025 Mar 16];24(7):1054\u0026ndash;63. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779492/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779492/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKancharla K, Kanagaraj S, Ramdoss S, Gopal CNR. Development and validation of the multi-dimensional metamemory skills (MDMS) scale for students in an Indian sample. Cogn Process. 2023;24(3):375\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaraiva R, Boeijen I, Hope L, Horselenberg R, Sauerland M, Koppen P. Development and validation of the Eyewitness Metamemory Scale. Appl Cogn Psychol. 2019;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroyer AK, Rich JB. Psychometric properties of a new metamemory questionnaire for older adults. J Gerontol B Psychol Sci Soc Sci. 2002;57(1):P19\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaustino B, Branco Vasco A, Oliveira J, Lopes P, Fonseca I. Metacognitive self-assessment scale: psychometric properties and clinical implications. Appl Neuropsychol Adult. 2021;28(5):596\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManzar MD, Albougami A, Salahuddin M, Sony P, Spence DW, Pandi-Perumal SR. The Mizan meta-memory and meta-concentration scale for students (MMSS): a test of its psychometric validity in a sample of university students. BMC Psychol [Internet]. 2018 Dec [cited 2025 Mar 15];6(1):59. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bmcpsychology.biomedcentral.com/articles/\u003c/span\u003e\u003cspan address=\"https://bmcpsychology.biomedcentral.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40359-018-0275-7\u003c/span\u003e\u003cspan address=\"10.1186/s40359-018-0275-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbougami A, Manzar D, Almansour AM, Alrasheadi BA. Metamemory and Metaconcentration Scale (MMS) for Health Professionals: A Psychometric Investigation in Nurses.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoltmer E, K\u0026ouml;slich-Strumann S, Voltmer JB, K\u0026ouml;tter T. Stress and behavior patterns throughout medical education \u0026ndash; a six year longitudinal study. BMC Med Educ [Internet]. 2021 Aug 28 [cited 2025 Mar 16];21(1):454. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12909-021-02862-x\u003c/span\u003e\u003cspan address=\"10.1186/s12909-021-02862-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsan O, Esan A, Folasire A, Oluwajulugbe P. Mental health and wellbeing of medical students in Nigeria: a systematic review. Int Rev Psychiatry Abingdon Engl. 2019;31(7\u0026ndash;8):661\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalogh EP, Miller BT, Ball JR, Care C. on DE in H, Services B on HC, Medicine I of, The Diagnostic Process. In: Improving Diagnosis in Health Care [Internet]. National Academies Press (US); 2015 [cited 2025 Mar 16]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK338593/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK338593/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoudoulas KD, Triposkiadis F, Stefanadis C, Boudoulas H. The endlessness evolution of medicine, continuous increase in life expectancy and constant role of the physician. Hell J Cardiol HJC Hell Kardiologike Epitheorese. 2017;58(5):322\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNatterson-Horowitz B, Aktipis A, Fox M, Gluckman PD, Low FM, Mace R et al. The future of evolutionary medicine: sparking innovation in biomedicine and public health. Front Sci [Internet]. 2023 Feb 28 [cited 2025 Mar 16];1. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/science/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/science/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fsci.2023.997136/full\u003c/span\u003e\u003cspan address=\"10.3389/fsci.2023.997136/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlusmann V, Evers A, Schwarzer R, Heuser I. A brief questionnaire on metacognition: Psychometric properties. Aging Ment Health [Internet]. 2011 Nov [cited 2025 Mar 15];15(8):1052\u0026ndash;62. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tandfonline.com/doi/abs/\u003c/span\u003e\u003cspan address=\"http://www.tandfonline.com/doi/abs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13607863.2011.583624\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2011.583624\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunnally JC, Bernstein IH. (1994). Psychometric theory (3rd ed.). New York McGraw-Hill. - References - Scientific Research Publishing [Internet]. [cited 2025 Mar 16]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scirp.org/reference/referencespapers?referenceid=1017362\u003c/span\u003e\u003cspan address=\"https://www.scirp.org/reference/referencespapers?referenceid=1017362\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchraw G, Dennison RS. Assessing Metacognitive Awareness. Contemp Educ Psychol [Internet]. 1994 Oct [cited 2025 Mar 15];19(4):460\u0026ndash;75. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S0361476X84710332\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S0361476X84710332\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrawford JR, Smith G, Maylor EA, Della Sala S, Logie RH. The Prospective and Retrospective Memory Questionnaire (PRMQ): Normative data and latent structure in a large non-clinical sample. Memory. 2003;11(3):261\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiqueira MAM, Gon\u0026ccedil;alves JP, Mendon\u0026ccedil;a VS, Kobayasi R, Arantes-Costa FM, Tempski PZ et al. Relationship between metacognitive awareness and motivation to learn in medical students. BMC Med Educ [Internet]. 2020 Oct 30 [cited 2025 Mar 15];20(1):393. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12909-020-02318-8\u003c/span\u003e\u003cspan address=\"10.1186/s12909-020-02318-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlavell JH. Metacognition and cognitive monitoring: A new area of cognitive\u0026ndash;developmental inquiry. Am Psychol [Internet]. 1979 Oct [cited 2025 Mar 15];34(10):906\u0026ndash;11. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.apa.org/doi/10.1037/0003-066X.34.10.906\u003c/span\u003e\u003cspan address=\"https://doi.apa.doi/10.1037/0003-066X.34.10.906\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson TO. Metamemory: A Theoretical Framework and New Findings. In: Psychology of Learning and Motivation [Internet]. Elsevier; 1990 [cited 2025 Mar 15]. pp. 125\u0026ndash;73. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S0079742108600535\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S0079742108600535\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans JSBT, Stanovich KE. Dual-Process Theories of Higher Cognition: Advancing the Debate. Perspect Psychol Sci J Assoc Psychol Sci. 2013;8(3):223\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model Multidiscip J [Internet]. 1999 Jan 1 [cited 2025 Mar 15];6(1):1\u0026ndash;55. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10705519909540118\u003c/span\u003e\u003cspan address=\"10.1080/10705519909540118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCleary TJ, Zimmerman BJ. Self-Regulation Empowerment Program: A School-Based Program to Enhance Self-Regulated and Self-Motivated Cycles of Student Learning. Psychol Sch. 2004;41(5):537\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Meta-memory, meta-cognition, medical education, psychometric validation, MMSS","lastPublishedDoi":"10.21203/rs.3.rs-6238647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6238647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Meta-memory and meta-cognition play crucial roles in self-regulating cognitive processes, impacting learning and problem-solving abilities. The Mizan meta-memory and meta-concentration scale for students (MMSS) was developed as a concise tool to assess these cognitive functions. While the MMSS has been validated in Ethiopian and Saudi Arabian populations, its applicability to Nigerian healthcare students remains unexplored. Given the intense cognitive demands of medical education, this study aims to validate the MMSS among Nigerian healthcare students, assessing its psychometric properties and reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional study using simple random sampling was conducted among 299 healthcare students (Medicine and Surgery, Dentistry and Physiotherapy) at the University of Ibadan. Participants completed an online survey containing the MMSS, a nine-item questionnaire divided into two subscales: meta-memory and meta-concentration. Internal consistency was evaluated using Cronbach’s alpha and McDonald’s omega. Exploratory factor analysis (EFA) was performed to assess construct validity while confirmatory factor analysis (CFA) was used to determine model fit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The MMSS demonstrated strong internal consistency both for the MMSS global and subscales (Cronbach’s alpha = 0.875, 0.808, 0.857; McDonald’s omega = 0.871, 0.805, 0.859). EFA confirmed a two-factor structure, with the meta-memory subscale explaining 50.26% of the variance and the meta-concentration subscale accounting for 12.78%. CFA results indicated a good model fit (CFI = 0.974, TLI = 0.959, RMSEA = 0.066, SRMR = 0.041, X\u003csup\u003e2\u003c/sup\u003e/df \u0026lt; 2.284, PCLOSE = 0.127), supporting the scale’s validity. The MMSS was found to be a reliable measure of cognitive self-regulation among Nigerian healthcare students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The findings support the use of the MMSS as a valid tool for assessing meta-memory and meta-concentration in Nigerian healthcare students. Given its strong psychometric properties, the MMSS can be applied in educational settings to enhance learning strategies and cognitive self-regulation.\u003c/p\u003e","manuscriptTitle":"Psychometric Validation of the Mizan Meta-memory and Meta-Concentration Scale for Students (MMSS) Among a Sample of Health Care Students in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 08:04:10","doi":"10.21203/rs.3.rs-6238647/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-17T08:47:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T20:49:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-25T17:05:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T06:08:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250325344120451722684847595968834832476","date":"2025-05-19T06:53:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332998173725471773129743675893839641529","date":"2025-05-13T13:48:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149259013855140197484667777923368116720","date":"2025-05-08T14:08:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-05T10:28:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-22T14:57:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T08:10:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T08:05:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-03-16T16:13:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01c5139d-e5cf-482d-9162-75e0d0708719","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T12:57:27+00:00","versionOfRecord":{"articleIdentity":"rs-6238647","link":"https://doi.org/10.1186/s40359-026-04647-7","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2026-04-25 15:59:52","publishedOnDateReadable":"April 25th, 2026"},"versionCreatedAt":"2025-03-18 08:04:10","video":"","vorDoi":"10.1186/s40359-026-04647-7","vorDoiUrl":"https://doi.org/10.1186/s40359-026-04647-7","workflowStages":[]},"version":"v1","identity":"rs-6238647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6238647","identity":"rs-6238647","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}