Self-Regulated Learning in Transitional Year Physicians: The Mediating Role of Resource Management Strategies

Self-Regulated Learning in Transitional Year Physicians: The Mediating Role of Resource Management Strategies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Self-Regulated Learning in Transitional Year Physicians: The Mediating Role of Resource Management Strategies Kung-Pei Tang, Shih-Ping Huang, Yun-Chu Wang, Yuan-Jen Tsai, Yuan-Chun Ko, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7738977/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Self-regulated learning (SRL) is a dynamic process closely linked to work tasks and plays a critical role in helping physicians adapt during the transitional year of medical training. While SRL has been extensively studied in residency, its mechanisms among postgraduate trainees remain underexplored. This study investigates SRL strategies among transitional year physicians and evaluates a structured assessment tool tailored for this population. A mixed-methods approach was adopted, integrating quantitative and qualitative analyses. The quantitative phase examined clinical learning experiences before, during, and after the transition from undergraduate to postgraduate training. The qualitative phase involved semi-structured interviews exploring five SRL components: resource management strategies, cognitive and metacognitive strategies, expectancy, value, and affective components. A total of 76 transitional year physicians completed the questionnaire, and 17 participated in focus group discussions. Data analysis included item refinement and linear regression modeling to assess the applicability of SRL. Results indicated that resource management strategies fully mediated the relationship between value components and cognitive/metacognitive strategies, supported by Pearson’s correlation analysis. Qualitative findings reinforced these relationships, highlighting the role of structured learning strategies in enhancing self-efficacy during the transition. This study underscores the significance of SRL in medical education, particularly in facilitating the transition from undergraduate to postgraduate training. The findings emphasize the critical role of resource management strategies in promoting effective learning and adapting to the demands of clinical practice. Self-Regulated Learning Resource Management Strategies Transitional Year Postgraduate Training Learning Strategies Figures Figure 1 Introduction Self-regulated learning (SRL) is influenced by various theories explaining how individuals control their learning, with social cognitive theory emphasizing self-efficacy as a key factor(Bandura, 1989 ; Pintrich, 1995 ; Zimmerman, 1989 ). Effective learning occurs in well-structured environments where learners can experiment, experience success and failure, and refine their skills, particularly in practical fields like clinical training(Irvine, Williams, Özmen, & McKenna, 2019 ). Extensive evidence indicates that self-regulated learning is widely employed in both workplace settings and clinical training among physicians(Margaryan, Littlejohn, & Milligan, 2013 ; Sitzmann & Ely, 2011 ). Key constructs of self-regulated learning, such as goal-setting, persistence, effort, and self-efficacy, have been shown to have particularly strong effects on learning outcomes(Sitzmann & Ely, 2011 ). For medical students, the effective application of self-regulated learning strategies is associated with higher academic achievement, improved clinical skills, and better mental health(Cho, Marjadi, Langendyk, & Hu, 2017 ). Strategies such as setting student-generated weekly goals, enhancing metacognitive skills, and promoting reflective learning practices have been found to facilitate self-regulated learning in daily activities(Larsen, Naismith, & Margolis, 2017 ; Learning). Previous studies have demonstrated that self-regulated learning skills can be cultivated through factors such as the specific goals perceived by students, the autonomy they experience, the learning opportunities provided or created by themselves, and the anticipated outcomes of an activity(Joris J. Berkhout et al., 2015 ). In environments where residents and peers offer guidance and help students navigate a new learning context, medical students can spontaneously develop self-regulated learning skills(Joris J Berkhout, Helmich, Teunissen, van der Vleuten, & Jaarsma, 2017 ). These skills encompass individual dimensions (e.g., goal setting), contextual dimensions (e.g., managing time pressure, patient care, and supervision), and social dimensions (e.g., support from supervisors and peers)(van Houten-Schat et al., 2018 ). Moreover, after entering medical school, students demonstrate significant growth in reflective skills, highlighting the bidirectional relationship between medical education and self-regulated learning(Lucieer, Jonker, Visscher, Rikers, & Themmen, 2016 ). This interplay underscores the importance of fostering self-regulated learning throughout medical training. The transitional year in medical education bridges undergraduate training and specialized residency, equipping graduates with essential clinical skills and fostering professional development(Demiroren, Atilgan, Tasdelen Teker, & Turan, 2021). Recognized by the Accreditation Council for Graduate Medical Education (ACGME), this phase addresses the gap between preclinical and clinical training, which often leaves students feeling unprepared for clinical practice, negatively impacting their well-being (Hurst, Kahan, Ruetalo, & Edwards, 2013 ; Malau-Aduli et al., 2020 ). In Taiwan, medical education comprises a six-year undergraduate program (UGY) followed by a two-year postgraduate training (PGY) with rotations in different elective disciplines. Upon completion, students qualify for a resident of one specialization. To adapt to the transition from UGY to PGY, trainees develop coping strategies, yet the lack of clinical competence increases the risk of medical errors. Recognizing this challenge, authorities emphasize the need for structured support mechanisms, such as early clinical exposure and goal-setting, to enhance patient safety and professional development (Liu, Tang, Wang, & Chiu, 2022 ; O'Brien, 2018 ; Swails et al., 2023 ). SRL is a highly social process, deeply integrated with and structured by work tasks, making it a promising approach to ease the transition during medical training (Margaryan, Milligan, Littlejohn, Hendrix, & Graeb-Koenneker, 2009 ). Various mechanisms of SRL have also been identified throughout residency training over the years (Lee et al., 2020 ). Current evidence suggests that metacognitive self-regulation significantly improves during the transitional year, indicating that SRL may play a pivotal role in supporting trainees during this period (Cho et al., 2017 ). While previous studies have examined SRL strategies among UGY students, there remains a gap in understanding SRL processes in PGY trainees (Brydges et al., 2020 ). He, Liu, Ran, and Zhang ( 2023 ) found that students’ perception of teacher feedback positively influences self-regulated learning, with self-efficacy and achievement goals mediating this relationship, highlighting the role of feedback in self-regulation within the framework of social cognitive theory. This study aims to explore SRL strategies among trainees and verify a measurable questionnaire specifically tailored to transitional year medical trainees. Methods Study Design This study utilizes a mixed-methods approach to examine self-regulated learning (SRL) strategies among transitional year physicians. The quantitative phase is guided by SRL and self-directed learning theories (Charokar & Dulloo, 2022 ; He et al., 2023 ; Pintrich, 1991 ) and includes a questionnaire structured around the three phases of SRL—before, during, and after learning—to assess trainees' experiences in undergraduate and postgraduate training. According to SRL theory, learning is regulated through five components: (1) affective component, (2) expectancy component, (3) value component, (4) cognitive and metacognitive strategies, and (5) resource management strategies(Pintrich & De Groot, 1990 ). The affective, expectancy, and value components influence the initial learning phase, while resource management strategies support self-monitoring during learning. Metacognitive strategies emerge in the final phase to refine future learning processes. The qualitative phase utilizes semi-structured interviews to examine key SRL components, including resource management, cognitive and metacognitive strategies, expectancy, value, and affective components. Data analysis included item refinement and linear regression modeling to evaluate the applicability of the SRL framework. Results indicated that resource management strategies mediated the relationship between learning value and cognitive/metacognitive strategies, highlighting their critical role in enhancing self-efficacy (Kittel & Seufert, 2023 ; Versteeg et al., 2021 ). The qualitative findings further validated this relationship, emphasizing the importance of structured learning strategies in facilitating the transition in medical training. The informed consent was described in TMU-Joint Institutional Review Board (No. N 202203192). Data Analysis The questionnaire underwent item analysis following a three-step process: independent t-tests to compare extreme groups, correlation tests to assess homogeneity, and Cronbach’s Alpha calculation to evaluate reliability(Harris & Subkoviak, 1986 ; McCowan & McCowan, 1999 ). A 95% confidence interval was applied to the t-tests, and items showing significant differences were excluded. Items with correlation coefficients below 0.4 were also removed. Furthermore, any item whose exclusion increased the overall Cronbach’s Alpha was eliminated to enhance reliability. To evaluate the applicability of the self-regulated learning model during the transitional year of postgraduate medical training (Fig. 1 ), linear regression analyses were conducted using various self-regulated learning components. Metacognitive strategies were strongly associated with informal learning behaviors in clinical practice (Kittel & Seufert, 2023 ) and have been shown to predict self-regulated learning among preclinical students (Versteeg et al., 2021 ). Additionally, cognitive and metacognitive skills play a crucial role in academic development and contribute to reflective clinical reasoning in medical and nursing education (Chang et al., 2021 ; Kuiper & Pesut, 2004 ). Given these associations, cognitive and metacognitive strategies were examined as indicators of self-reflection and self-evaluation. A linear regression model was developed to assess how self-regulated learning components explain metacognitive processes during the transitional year of postgraduate medical training. Finally, the quantitative results were further validated through qualitative interviews. Audio recordings from each focus group were transcribed verbatim, and initial data analysis was conducted using the template method. The interviews aimed to: (1) further explore their self-regulated learning strategies and (2) clarify the relationship between cognitive and metacognitive learning skills and self-regulated learning to verify the findings of the linear regression model. The initial analysis involved independent coding by two researchers (CIL and SPH). In cases of disagreement, a third investigator (KPT) was consulted to discuss the codes and categories and reach a consensus. Results Demographics A total of 76 transitional year physicians completed the questionnaire and were included in the analysis. The participants' ages ranged from 23 to 37 years, with a mean age of 26.47 years (standard deviation 2.82). Of these participants, 48 were male (63.16%) and 28 were female (36.84%). The average university ranking was 50%. Following the questionnaire, 17 participants took part in five focus group interviews, with each group consisting of 3–4 interviewees. Item analysis The original questionnaire in this study consisted of 81 items divided into five components. To further validate its applicability to the participants, it was reorganized into three categories: (1) affective components (AC), (2) expectancy components (EC) and value components (VC), and (3) cognitive and metacognitive strategies (CMS) and resource management strategies (RMS). In the item analysis, eight items were excluded, including one from expectancy components, two from cognitive and metacognitive strategies, and five from resource management strategies. The Cronbach’s Alpha for the three clusters was 0.964 for expectancy and value components, 0.781 for affective components, and 0.966 for cognitive, metacognitive, and resource management strategies. A total of 73 items were retained (EC: 11, VC: 14, AC: 5, CMS: 29, RMS: 14). Most excluded items were reverse-scored, with the majority belonging to resource management strategies. Further analysis of the retained items revealed no significant differences between males and females across the five components or the total score (Table 1 ). Table 1 Summary of the statistical data after item analysis Male N = 49 Female N = 27 All N = 76 Age, Mean (SD) 26.70 (3.10) 26.48 (2.86) 26.47 (2.28) College ranking* 4.88 (2.69) 4.92 (2.63) 50% (2.76) Total MSLQ score, Mean (SD) 351.04 (73.06) 337.41 (48.76) 346.68 (66.99) Value component, Mean (SD) 4.89 (1.11) 4.70 (0.73) 4.82 (0.99) Expectancy component, Mean (SD) 4.63 (1.01) 4.34 (0.81) 4.53 (0.96) Affective component, Mean (SD) 4.58 (1.11) 4.53 (1.09) 4.56 (1.10) Cognitive and metacognitive strategies component, Mean (SD) 4.85 (1.17) 4.67 (0.83) 4.79 (1.06) Resource management strategies component, Mean (SD) 4.89 (1.05) 4.75 (0.74) 4.84 (0.96) *Data on college ranking were missing for three participants. A comparison of scores across different academic performance groups was conducted, categorizing high academic performance as the top 0–30%, average performance as 31–70%, and low performance as the bottom 71–100%. No significant differences were found in the components or total score among the groups (Table 2 ). Table 2 Summary of Statistical Data Categorized by Academic Performance Low college ranking* N = 22 Average college ranking N = 30 High college ranking N = 21 Age, Mean (SD) 26.00 (2.65) 26.30 (2.97) 25.90 (6.07) Sex, Male (%) 16 (72.7%) 17 (56.67%) 15 (71.43%) College ranking* 8.68 (0.78) 5.27 (1.05) 2.10 (0.83) Total MSLQ score, Mean (SD) 356.27 (69.88) 339.5 (64.46) 346.90 (67.35) Value component, Mean (SD) 4.86 (1.02) 4.75 (1.01) 4.90 (1.01) Expectancy component, Mean (SD) 4.58 (1.02) 4.42 (0.99) 4.57 (0.89) Affective component, Mean (SD) 4.57 (0.84) 4.76 (1.17) 4.39 (1.16) Cognitive and metacognitive strategies component, Mean (SD) 5.06 (1.13) 4.65 (1.08) 4.73 (1.01) Resource management strategies component, Mean (SD) 4.96 (1.10) 4.74 (0.88) 4.90 (0.97) * High college ranking was defined as the top 0–30%, average ranking as 31–70%, and low ranking as the bottom 71–100%. Linear Regression Model of Self-Regulated Learning Framework To assess the applicability of the self-regulated learning model for transitional year postgraduate physicians, linear regression models were developed with the cognitive and metacognitive strategies score as the dependent variable (Table 3 ). In Model 1, the independent variables included the scores of the value, expectancy, and affective components. This model yielded an adjusted R² of 0.693, with an F-statistic of 55.796 (p < .001). Among the predictors, the value component showed a significant positive coefficient (0.861, p < .001), demonstrating a strong predictive relationship with cognitive and metacognitive strategies. However, expectancy and affective components did not exhibit significant predictive power. In Model 2, the resource management strategies score was added as an independent variable, resulting in an improved adjusted R² of 0.825 and an F-statistic of 61.993 (p < .001). Resource management strategies had a significant positive coefficient (1.021, p < .001), strongly predicting cognitive and metacognitive strategies. However, the coefficient for the value component turned negative (-0.041) and non-significant. Collinearity diagnostics confirmed this issue, with the condition index in Model 2 for Dimension 5 elevated (41.988). Variance proportions indicated that the value component had a high proportion (0.98) with moderate overlap with resource management strategies (0.56), pointing to severe multicollinearity. To further confirm the full mediating effect of resource management strategies between cognitive and metacognitive strategies and the value component, Pearson’s correlation analysis was performed. Results showed strong correlations between value and resource management strategies (r = 0.917, p < .001), value and cognitive and metacognitive strategies (r = 0.832, p < .001), and resource management strategies and cognitive and metacognitive strategies (r = 0.913, p < .001). Table 3 Description of the linear regression models Model 1 Model 2 Adjusted R 2 0.681 0.825 F-statistic 55.796 61.993 P value for F-statistic < .001 < .001 Value component coefficient 0.861 *** -0.041 Expectation component coefficient -0.01 -0.013 Affect component coefficient 0.041 0.028 Resource management skill component coefficient - 1.021 *** Intercept 0.493 -0.24 *Significance levels are indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001. Values denote statistically significant correlations or differences as per the specified thresholds. Interview process A total of 17 participants were included in the interview, within 5 focus groups. All the interview began by focusing on the confidence that PGY physicians have in handling the clinical materials they encounter. Participants were encouraged to openly share their learning experiences, with the interviewer probing into their learning strategies, including resource management and metacognitive awareness related to self-directed learning. Participants were asked about their perceptions of the value of clinical learning, their anxiety about their ability to adapt to the professional clinical role, and how they valued the clinical knowledge they acquired. The interview outline is presented in Supplement 1. The qualitative analysis revealed a clear positive relationship between the CMS and RMS. For example, Case No. 25 and Case No. 3 demonstrated a high capacity for acquiring and utilizing learning resources, coupled with metacognitive awareness of their growth and learning processes. Case No. 25 exhibited effective resource management by “asking others for help, researching independently, or checking how similar cases were managed,” and he evaluated his growth by “checking later to see if my initial management was changed or remained the same,” which he noted as a way to “review and improve my own management skills.” Similarly, Case No. 3 underscored his practical resource utilization, stating that he regularly “checks the system to see what medication changes were made” and consults references such as “papers or UpToDate.("UpToDate," 2024)” He also highlighted significant progress in patient communication, noting that he could now “explain things and even show concern for their well-being.” Both cases showcase the integration of resource management strategies with reflective practices to enhance their clinical learning. In contrast, Case No. 22 and Case No. 76 displayed limited focus on the quality of their learning and a reduced ability to manage learning resources effectively. Case No. 22 demonstrated a high learning value, recognizing the importance for basic and emergent clinical skills: “ACLS is a must, followed by intubation, focused ultrasound, physical exams, emergency assessments, blood draws, and interpreting EKGs.” However, his low RMS and CMS were evident, as he noted, “I don’t usually review my learning explicitly… Unless I’m particularly interested in a topic or a teacher assigns me something to look up, I don’t go out of my way to review extra literature.” In contrast, Case No. 76 exhibited a low learning value: “I feel like many things in life are futile. For example, even if you’ve mastered a procedure, you still have to do three or four EKGs in a day.” He treated the transitional year as merely observational and emphasized personal satisfaction over learning outcomes, “I feel like I’m making effort to do my part… I think it’s enough as long as I’m satisfied with my own effort.” Similarly, although Case No. 25 demonstrated a high learning value, emphasizing the importance of “being responsible in every department I rotate through, even if something isn’t directly related to me.” , Case No. 3 displayed a lower learning value: “I’m not really interested in pursuing surgery.” While he acknowledged growth in patient communication and resource utilization, his selective approach reflected a narrower scope of learning engagement. Both Case No. 3 and Case No. 25 ranked between the 20th and 40th percentile of their college scores, while Case No. 22 and Case No. 76 ranked between the 80th and 100th percentile in their college graduation scores. Discussion This study provides insights into the learning motivations and self-regulated learning strategies of physicians transitioning from UGY to PGY. Notably, RMS emerged as predictor of self-evaluation and self-reflection for the SRL in clinical setting (indicated by CMS). The high adjusted R² of 0.825 in the regression model underscores the significant explanatory power of these components. Interestingly, while the VC was initially a strong predictor, it became non-significant when RMS were introduced, suggesting a mediating effect. Correlation analysis further confirmed that RMS bridged the relationship between CMS and VC, consistent with prior research on learning motivation and resource management (Kittel & Seufert, 2023 ). These findings highlight the need for further investigation into optimizing training interventions. In the qualitative phase, a positive relationship between RMS and CMS was evident; however, variations in VC were observed. For example, two participants with high RMS and CMS differed in their learning motivations—one valued learning for its future utility, while the other showed minimal appreciation for its relevance to professional growth. Conversely, two participants with low RMS and CMS exhibited distinct attitudes—one had low motivation, while the other demonstrated strong dedication to clinical learning despite lacking structured learning strategies. Previous research has established strong links between learning anxiety and success expectancy across subjects (Dong, Liu, & Yang, 2022 ; Szucs & Toffalini, 2023 ). However, anxiety itself is not directly correlated with academic performance (Khasawneh, Gosling, & Williams, 2021 ). Additionally, while academic anxiety is prevalent, learning outcomes are more closely tied to self-efficacy(Ferreira É et al., 2020 ). In the regression analysis, only the VC significantly predicted CMS, whereas AC and EC did not, aligning with findings that these factors do not directly influence learning outcomes or self-efficacy. Studies have further suggested that learning motivation does not directly impact self-efficacy or self-appraisal (Al-Qadri, Mouas, Saraa, & Boudouaia, 2024 ; Li, Yang, Zhao, & Li, 2023 ; Wu, Li, Zheng, & Guo, 2020 ). While success expectancy often aligns with task value, the relationship between self-efficacy and learning value remains unclear (Schweder & Raufelder, 2022 ). Similarly, interventions promoting learning value have shown mixed effects on academic self-efficacy (Symes & Putwain, 2016 ). Regression analysis in this study revealed that the effects of VC on CMS were mediated by RMS, further supporting their role in linking motivational factors with self-regulated learning skills. Limitations and Future Directions This study has several limitations. First, it focused on metacognitive aspects of self-regulated learning, attributing learning outcomes to internal factors without considering external influences such as institutional differences, objective evaluations, or the learning environment. According to Weiner's attribution theory, success or failure can be attributed to both internal and external factors (Weiner, 1985 ). This study prioritized self-regulated learning processes rather than objective performance, assuming that self-appraisals best reflect students’ awareness of their learning. Second, the sample size for the qualitative phase was relatively small, potentially limiting generalizability. While focus groups provided rich insights, individual interviews might have captured more personalized learning experiences and strategies. Lastly, response bias cannot be ruled out, as participants may have felt compelled to present themselves favorably. Future research should explore the longitudinal impact of self-regulated learning interventions and examine how individual, institutional, and systemic factors shape learning motivations and strategies in medical training. Alternative methods, such as diary studies or real-time assessments, could provide a more nuanced understanding of how these strategies evolve in clinical environments. Further investigation into how specific self-regulated learning strategies contribute to distinct aspects of clinical performance would enhance their practical application. Conclusion This study highlights the strong relationship between metacognitive and resource management strategies in clinical learning, extending the applicability of self-regulated learning theory to the context of medical education, particularly during the transition year. During the transitional PGY, RMS were found to mediate the relationship between the VC and CMS. This suggests that the self-efficacy of learning may be predominantly influenced by the development and application of learning skills, emphasizing the critical role of RMS in facilitating effective self-regulated learning during this phase. Declarations Acknowledgements We sincerely appreciate all the participants to join the study. We also appreciate the grants from Wan Fang Hospital, Taipei Medical University. Authors’ contribution CIL, SPH and KPT designed and conceived the study. CIL, YCW, and YJT completed the participants recruitment. YCK assisted on the data analysis and administrative work. KTP responded to editorial and reviewer comments. All authors read and approved the final manuscript. Funding declaration This study was supported by Wan Fang Hospital, Taipei Medical University, which had no role in the study design, data collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. Grant No. were 112-WF-WME-02 and 111-WF-HHCE-13. There was no additional funding. Availability of data and materials No data have been submitted to any open-access databases. All data supporting the study are presented in the manuscript or are available from corresponding author upon reasonable request. Clinical trial number not applicable Ethics approval and consent to participate The Taipei Medical University-Joint Institutional Review approved the study protocol (No. N202203192). The work was carried out in accordance with the Declaration of Helsinki. 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(1999). Item Analysis for Criterion-Referenced Tests. Online Submission . O'Brien, B. C. (2018). What to Do About the Transition to Residency? Exploring Problems and Solutions From Three Perspectives. Acad Med, 93 (5), 681-684. doi:10.1097/acm.0000000000002150 Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Pintrich, P. R. (1995). Understanding self‐regulated learning. New directions for teaching and learning, 1995 (63), 3-12. Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82 (1), 33. Schweder, S., & Raufelder, D. (2022). Adolescents' expectancy-value profiles in school context: The impact of self-directed learning intervals. J Adolesc, 94 (4), 569-586. doi:10.1002/jad.12047 Sitzmann, T., & Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go. Psychol Bull, 137 (3), 421-442. doi:10.1037/a0022777 Swails, J. L., Angus, S., Barone, M. A., Bienstock, J., Burk-Rafel, J., Roett, M. A., & Hauer, K. E. (2023). The Undergraduate to Graduate Medical Education Transition as a Systems Problem: A Root Cause Analysis. Acad Med, 98 (2), 180-187. doi:10.1097/acm.0000000000005065 Symes, W., & Putwain, D. W. (2016). The role of attainment value, academic self-efficacy, and message frame in the appraisal of value-promoting messages. Br J Educ Psychol, 86 (3), 446-460. doi:10.1111/bjep.12117 Szucs, D., & Toffalini, E. (2023). Maths anxiety and subjective perception of control, value and success expectancy in mathematics. R Soc Open Sci, 10 (11), 231000. doi:10.1098/rsos.231000 UpToDate. (2024). Retrieved from https://www.uptodate.com/ van Houten-Schat, M. A., Berkhout, J. J., van Dijk, N., Endedijk, M. D., Jaarsma, A. D. C., & Diemers, A. D. (2018). Self-regulated learning in the clinical context: a systematic review. Med Educ, 52 (10), 1008-1015. doi:10.1111/medu.13615 Versteeg, M., Bressers, G., Wijnen-Meijer, M., Ommering, B. W. C., de Beaufort, A. J., & Steendijk, P. (2021). What Were You Thinking? Medical Students' Metacognition and Perceptions of Self-Regulated Learning. Teach Learn Med, 33 (5), 473-482. doi:10.1080/10401334.2021.1889559 Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological review, 92 (4), 548. Wu, H., Li, S., Zheng, J., & Guo, J. (2020). Medical students' motivation and academic performance: the mediating roles of self-efficacy and learning engagement. Med Educ Online, 25 (1), 1742964. doi:10.1080/10872981.2020.1742964 Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81 (3), 329. Additional Declarations No competing interests reported. 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10:38:01","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109449,"visible":true,"origin":"","legend":"","description":"","filename":"540ce1662f2f4ff0b5e19abfccf4ac031structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7738977/v1/47ddc77f7c5ccad88b6228db.xml"},{"id":96462444,"identity":"b97a8ae0-7ab9-4b7d-9f2f-c13901a313e1","added_by":"auto","created_at":"2025-11-21 10:38:01","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118483,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7738977/v1/fc380d3bda5d2d7cdd5e9c45.html"},{"id":96462437,"identity":"6f40ebea-260b-4b57-af0d-d0c81b563eaf","added_by":"auto","created_at":"2025-11-21 10:38:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48660,"visible":true,"origin":"","legend":"\u003cp\u003eThe hypothetical relationship of components within self-regulated learning\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7738977/v1/e40304a18a10ee3bfce39254.jpeg"},{"id":96708537,"identity":"dd4ecc6f-ac45-4ad0-b70c-a64816b4367a","added_by":"auto","created_at":"2025-11-25 10:04:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":680636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7738977/v1/82918c5b-301b-4562-87d7-0c14073bd612.pdf"},{"id":96462438,"identity":"2241c5f3-0a6b-455a-8fea-5e6f0f78b7d2","added_by":"auto","created_at":"2025-11-21 10:38:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15619,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7738977/v1/1248291a8ee13fae9aa6bc6f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Self-Regulated Learning in Transitional Year Physicians: The Mediating Role of Resource Management Strategies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSelf-regulated learning (SRL) is influenced by various theories explaining how individuals control their learning, with social cognitive theory emphasizing self-efficacy as a key factor(Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Pintrich, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Zimmerman, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Effective learning occurs in well-structured environments where learners can experiment, experience success and failure, and refine their skills, particularly in practical fields like clinical training(Irvine, Williams, \u0026Ouml;zmen, \u0026amp; McKenna, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Extensive evidence indicates that self-regulated learning is widely employed in both workplace settings and clinical training among physicians(Margaryan, Littlejohn, \u0026amp; Milligan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sitzmann \u0026amp; Ely, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Key constructs of self-regulated learning, such as goal-setting, persistence, effort, and self-efficacy, have been shown to have particularly strong effects on learning outcomes(Sitzmann \u0026amp; Ely, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For medical students, the effective application of self-regulated learning strategies is associated with higher academic achievement, improved clinical skills, and better mental health(Cho, Marjadi, Langendyk, \u0026amp; Hu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Strategies such as setting student-generated weekly goals, enhancing metacognitive skills, and promoting reflective learning practices have been found to facilitate self-regulated learning in daily activities(Larsen, Naismith, \u0026amp; Margolis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Learning). Previous studies have demonstrated that self-regulated learning skills can be cultivated through factors such as the specific goals perceived by students, the autonomy they experience, the learning opportunities provided or created by themselves, and the anticipated outcomes of an activity(Joris J. Berkhout et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In environments where residents and peers offer guidance and help students navigate a new learning context, medical students can spontaneously develop self-regulated learning skills(Joris J Berkhout, Helmich, Teunissen, van der Vleuten, \u0026amp; Jaarsma, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These skills encompass individual dimensions (e.g., goal setting), contextual dimensions (e.g., managing time pressure, patient care, and supervision), and social dimensions (e.g., support from supervisors and peers)(van Houten-Schat et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, after entering medical school, students demonstrate significant growth in reflective skills, highlighting the bidirectional relationship between medical education and self-regulated learning(Lucieer, Jonker, Visscher, Rikers, \u0026amp; Themmen, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This interplay underscores the importance of fostering self-regulated learning throughout medical training.\u003c/p\u003e\u003cp\u003eThe transitional year in medical education bridges undergraduate training and specialized residency, equipping graduates with essential clinical skills and fostering professional development(Demiroren, Atilgan, Tasdelen Teker, \u0026amp; Turan, 2021). Recognized by the Accreditation Council for Graduate Medical Education (ACGME), this phase addresses the gap between preclinical and clinical training, which often leaves students feeling unprepared for clinical practice, negatively impacting their well-being (Hurst, Kahan, Ruetalo, \u0026amp; Edwards, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Malau-Aduli et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Taiwan, medical education comprises a six-year undergraduate program (UGY) followed by a two-year postgraduate training (PGY) with rotations in different elective disciplines. Upon completion, students qualify for a resident of one specialization. To adapt to the transition from UGY to PGY, trainees develop coping strategies, yet the lack of clinical competence increases the risk of medical errors. Recognizing this challenge, authorities emphasize the need for structured support mechanisms, such as early clinical exposure and goal-setting, to enhance patient safety and professional development (Liu, Tang, Wang, \u0026amp; Chiu, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; O'Brien, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Swails et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSRL is a highly social process, deeply integrated with and structured by work tasks, making it a promising approach to ease the transition during medical training (Margaryan, Milligan, Littlejohn, Hendrix, \u0026amp; Graeb-Koenneker, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Various mechanisms of SRL have also been identified throughout residency training over the years (Lee et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Current evidence suggests that metacognitive self-regulation significantly improves during the transitional year, indicating that SRL may play a pivotal role in supporting trainees during this period (Cho et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While previous studies have examined SRL strategies among UGY students, there remains a gap in understanding SRL processes in PGY trainees (Brydges et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). He, Liu, Ran, and Zhang (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that students\u0026rsquo; perception of teacher feedback positively influences self-regulated learning, with self-efficacy and achievement goals mediating this relationship, highlighting the role of feedback in self-regulation within the framework of social cognitive theory. This study aims to explore SRL strategies among trainees and verify a measurable questionnaire specifically tailored to transitional year medical trainees.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e\u003cp\u003eThis study utilizes a mixed-methods approach to examine self-regulated learning (SRL) strategies among transitional year physicians. The quantitative phase is guided by SRL and self-directed learning theories (Charokar \u0026amp; Dulloo, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pintrich, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and includes a questionnaire structured around the three phases of SRL\u0026mdash;before, during, and after learning\u0026mdash;to assess trainees' experiences in undergraduate and postgraduate training.\u003c/p\u003e\u003cp\u003eAccording to SRL theory, learning is regulated through five components: (1) affective component, (2) expectancy component, (3) value component, (4) cognitive and metacognitive strategies, and (5) resource management strategies(Pintrich \u0026amp; De Groot, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The affective, expectancy, and value components influence the initial learning phase, while resource management strategies support self-monitoring during learning. Metacognitive strategies emerge in the final phase to refine future learning processes.\u003c/p\u003e\u003cp\u003eThe qualitative phase utilizes semi-structured interviews to examine key SRL components, including resource management, cognitive and metacognitive strategies, expectancy, value, and affective components. Data analysis included item refinement and linear regression modeling to evaluate the applicability of the SRL framework. Results indicated that resource management strategies mediated the relationship between learning value and cognitive/metacognitive strategies, highlighting their critical role in enhancing self-efficacy (Kittel \u0026amp; Seufert, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Versteeg et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The qualitative findings further validated this relationship, emphasizing the importance of structured learning strategies in facilitating the transition in medical training. The informed consent was described in TMU-Joint Institutional Review Board (No. N 202203192).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe questionnaire underwent item analysis following a three-step process: independent t-tests to compare extreme groups, correlation tests to assess homogeneity, and Cronbach\u0026rsquo;s Alpha calculation to evaluate reliability(Harris \u0026amp; Subkoviak, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; McCowan \u0026amp; McCowan, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). A 95% confidence interval was applied to the t-tests, and items showing significant differences were excluded. Items with correlation coefficients below 0.4 were also removed. Furthermore, any item whose exclusion increased the overall Cronbach\u0026rsquo;s Alpha was eliminated to enhance reliability.\u003c/p\u003e\u003cp\u003eTo evaluate the applicability of the self-regulated learning model during the transitional year of postgraduate medical training (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), linear regression analyses were conducted using various self-regulated learning components. Metacognitive strategies were strongly associated with informal learning behaviors in clinical practice (Kittel \u0026amp; Seufert, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and have been shown to predict self-regulated learning among preclinical students (Versteeg et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, cognitive and metacognitive skills play a crucial role in academic development and contribute to reflective clinical reasoning in medical and nursing education (Chang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kuiper \u0026amp; Pesut, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven these associations, cognitive and metacognitive strategies were examined as indicators of self-reflection and self-evaluation. A linear regression model was developed to assess how self-regulated learning components explain metacognitive processes during the transitional year of postgraduate medical training.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, the quantitative results were further validated through qualitative interviews. Audio recordings from each focus group were transcribed verbatim, and initial data analysis was conducted using the template method. The interviews aimed to: (1) further explore their self-regulated learning strategies and (2) clarify the relationship between cognitive and metacognitive learning skills and self-regulated learning to verify the findings of the linear regression model. The initial analysis involved independent coding by two researchers (CIL and SPH). In cases of disagreement, a third investigator (KPT) was consulted to discuss the codes and categories and reach a consensus.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eDemographics\u003c/h2\u003e\u003cp\u003eA total of 76 transitional year physicians completed the questionnaire and were included in the analysis. The participants' ages ranged from 23 to 37 years, with a mean age of 26.47 years (standard deviation 2.82). Of these participants, 48 were male (63.16%) and 28 were female (36.84%). The average university ranking was 50%. Following the questionnaire, 17 participants took part in five focus group interviews, with each group consisting of 3\u0026ndash;4 interviewees.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eItem analysis\u003c/h3\u003e\n\u003cp\u003eThe original questionnaire in this study consisted of 81 items divided into five components. To further validate its applicability to the participants, it was reorganized into three categories: (1) affective components (AC), (2) expectancy components (EC) and value components (VC), and (3) cognitive and metacognitive strategies (CMS) and resource management strategies (RMS).\u003c/p\u003e\u003cp\u003eIn the item analysis, eight items were excluded, including one from expectancy components, two from cognitive and metacognitive strategies, and five from resource management strategies. The Cronbach\u0026rsquo;s Alpha for the three clusters was 0.964 for expectancy and value components, 0.781 for affective components, and 0.966 for cognitive, metacognitive, and resource management strategies. A total of 73 items were retained (EC: 11, VC: 14, AC: 5, CMS: 29, RMS: 14). Most excluded items were reverse-scored, with the majority belonging to resource management strategies. Further analysis of the retained items revealed no significant differences between males and females across the five components or the total score (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the statistical data after item analysis\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;49\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;76\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)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.70 (3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.48 (2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.47 (2.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege ranking*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.88 (2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.92 (2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50% (2.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal MSLQ score, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351.04 (73.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e337.41 (48.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e346.68 (66.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.89 (1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.70 (0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.82 (0.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpectancy component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.63 (1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.34 (0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.53 (0.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.58 (1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.53 (1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.56 (1.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive and metacognitive strategies component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.85 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.67 (0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.79 (1.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResource management strategies component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.89 (1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.75 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.84 (0.96)\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*Data on college ranking were missing for three participants.\u003c/p\u003e\u003cp\u003eA comparison of scores across different academic performance groups was conducted, categorizing high academic performance as the top 0\u0026ndash;30%, average performance as 31\u0026ndash;70%, and low performance as the bottom 71\u0026ndash;100%. No significant differences were found in the components or total score among the groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Statistical Data Categorized by Academic Performance\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow college ranking*\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage college ranking\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh college ranking\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\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)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.00 (2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.30 (2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.90 (6.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, Male (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (72.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (56.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (71.43%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege ranking*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.68 (0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.27 (1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.10 (0.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal MSLQ score, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e356.27 (69.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e339.5 (64.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e346.90 (67.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.86 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.75 (1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.90 (1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpectancy component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.58 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.42 (0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.57 (0.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.57 (0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.76 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.39 (1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive and metacognitive strategies component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.06 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.65 (1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.73 (1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResource management strategies component, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.96 (1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.74 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.90 (0.97)\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* High college ranking was defined as the top 0\u0026ndash;30%, average ranking as 31\u0026ndash;70%, and low ranking as the bottom 71\u0026ndash;100%.\u003c/p\u003e\n\u003ch3\u003eLinear Regression Model of Self-Regulated Learning Framework\u003c/h3\u003e\n\u003cp\u003eTo assess the applicability of the self-regulated learning model for transitional year postgraduate physicians, linear regression models were developed with the cognitive and metacognitive strategies score as the dependent variable (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Model 1, the independent variables included the scores of the value, expectancy, and affective components. This model yielded an adjusted R\u0026sup2; of 0.693, with an F-statistic of 55.796 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Among the predictors, the value component showed a significant positive coefficient (0.861, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), demonstrating a strong predictive relationship with cognitive and metacognitive strategies. However, expectancy and affective components did not exhibit significant predictive power.\u003c/p\u003e\u003cp\u003eIn Model 2, the resource management strategies score was added as an independent variable, resulting in an improved adjusted R\u0026sup2; of 0.825 and an F-statistic of 61.993 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Resource management strategies had a significant positive coefficient (1.021, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), strongly predicting cognitive and metacognitive strategies. However, the coefficient for the value component turned negative (-0.041) and non-significant.\u003c/p\u003e\u003cp\u003eCollinearity diagnostics confirmed this issue, with the condition index in Model 2 for Dimension 5 elevated (41.988). Variance proportions indicated that the value component had a high proportion (0.98) with moderate overlap with resource management strategies (0.56), pointing to severe multicollinearity.\u003c/p\u003e\u003cp\u003eTo further confirm the full mediating effect of resource management strategies between cognitive and metacognitive strategies and the value component, Pearson\u0026rsquo;s correlation analysis was performed. Results showed strong correlations between value and resource management strategies (r\u0026thinsp;=\u0026thinsp;0.917, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), value and cognitive and metacognitive strategies (r\u0026thinsp;=\u0026thinsp;0.832, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and resource management strategies and cognitive and metacognitive strategies (r\u0026thinsp;=\u0026thinsp;0.913, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\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\u003eDescription of the linear regression models\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=\"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\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP value for F-statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue component coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.861\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpectation component coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffect component coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResource management skill component coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.021\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.24\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*Significance levels are indicated as follows: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Values denote statistically significant correlations or differences as per the specified thresholds.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInterview process\u003c/h2\u003e\u003cp\u003eA total of 17 participants were included in the interview, within 5 focus groups. All the interview began by focusing on the confidence that PGY physicians have in handling the clinical materials they encounter. Participants were encouraged to openly share their learning experiences, with the interviewer probing into their learning strategies, including resource management and metacognitive awareness related to self-directed learning. Participants were asked about their perceptions of the value of clinical learning, their anxiety about their ability to adapt to the professional clinical role, and how they valued the clinical knowledge they acquired. The interview outline is presented in Supplement 1.\u003c/p\u003e\u003cp\u003eThe qualitative analysis revealed a clear positive relationship between the CMS and RMS. For example, Case No. 25 and Case No. 3 demonstrated a high capacity for acquiring and utilizing learning resources, coupled with metacognitive awareness of their growth and learning processes. Case No. 25 exhibited effective resource management by \u003cem\u003e\u0026ldquo;asking others for help, researching independently, or checking how similar cases were managed,\u0026rdquo;\u003c/em\u003e and he evaluated his growth by \u003cem\u003e\u0026ldquo;checking later to see if my initial management was changed or remained the same,\u0026rdquo;\u003c/em\u003e which he noted as a way to \u003cem\u003e\u0026ldquo;review and improve my own management skills.\u0026rdquo;\u003c/em\u003e Similarly, Case No. 3 underscored his practical resource utilization, stating that he regularly \u003cem\u003e\u0026ldquo;checks the system to see what medication changes were made\u0026rdquo;\u003c/em\u003e and consults references such as \u003cem\u003e\u0026ldquo;papers or UpToDate.(\"UpToDate,\" 2024)\u0026rdquo;\u003c/em\u003e He also highlighted significant progress in patient communication, noting that he could now \u003cem\u003e\u0026ldquo;explain things and even show concern for their well-being.\u0026rdquo;\u003c/em\u003e Both cases showcase the integration of resource management strategies with reflective practices to enhance their clinical learning.\u003c/p\u003e\u003cp\u003eIn contrast, Case No. 22 and Case No. 76 displayed limited focus on the quality of their learning and a reduced ability to manage learning resources effectively. Case No. 22 demonstrated a high learning value, recognizing the importance for basic and emergent clinical skills: \u003cem\u003e\u0026ldquo;ACLS is a must, followed by intubation, focused ultrasound, physical exams, emergency assessments, blood draws, and interpreting EKGs.\u0026rdquo;\u003c/em\u003e However, his low RMS and CMS were evident, as he noted, \u003cem\u003e\u0026ldquo;I don\u0026rsquo;t usually review my learning explicitly\u0026hellip; Unless I\u0026rsquo;m particularly interested in a topic or a teacher assigns me something to look up, I don\u0026rsquo;t go out of my way to review extra literature.\u0026rdquo;\u003c/em\u003e In contrast, Case No. 76 exhibited a low learning value: \u003cem\u003e\u0026ldquo;I feel like many things in life are futile. For example, even if you\u0026rsquo;ve mastered a procedure, you still have to do three or four EKGs in a day.\u0026rdquo;\u003c/em\u003e He treated the transitional year as merely observational and emphasized personal satisfaction over learning outcomes, \u003cem\u003e\u0026ldquo;I feel like I\u0026rsquo;m making effort to do my part\u0026hellip; I think it\u0026rsquo;s enough as long as I\u0026rsquo;m satisfied with my own effort.\u0026rdquo;\u003c/em\u003e Similarly, although Case No. 25 demonstrated a high learning value, emphasizing the importance of \u003cem\u003e\u0026ldquo;being responsible in every department I rotate through, even if something isn\u0026rsquo;t directly related to me.\u0026rdquo;\u003c/em\u003e, Case No. 3 displayed a lower learning value: \u003cem\u003e\u0026ldquo;I\u0026rsquo;m not really interested in pursuing surgery.\u0026rdquo;\u003c/em\u003e While he acknowledged growth in patient communication and resource utilization, his selective approach reflected a narrower scope of learning engagement. Both Case No. 3 and Case No. 25 ranked between the 20th and 40th percentile of their college scores, while Case No. 22 and Case No. 76 ranked between the 80th and 100th percentile in their college graduation scores.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides insights into the learning motivations and self-regulated learning strategies of physicians transitioning from UGY to PGY. Notably, RMS emerged as predictor of self-evaluation and self-reflection for the SRL in clinical setting (indicated by CMS). The high adjusted R\u0026sup2; of 0.825 in the regression model underscores the significant explanatory power of these components. Interestingly, while the VC was initially a strong predictor, it became non-significant when RMS were introduced, suggesting a mediating effect. Correlation analysis further confirmed that RMS bridged the relationship between CMS and VC, consistent with prior research on learning motivation and resource management (Kittel \u0026amp; Seufert, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings highlight the need for further investigation into optimizing training interventions.\u003c/p\u003e\u003cp\u003eIn the qualitative phase, a positive relationship between RMS and CMS was evident; however, variations in VC were observed. For example, two participants with high RMS and CMS differed in their learning motivations\u0026mdash;one valued learning for its future utility, while the other showed minimal appreciation for its relevance to professional growth. Conversely, two participants with low RMS and CMS exhibited distinct attitudes\u0026mdash;one had low motivation, while the other demonstrated strong dedication to clinical learning despite lacking structured learning strategies.\u003c/p\u003e\u003cp\u003ePrevious research has established strong links between learning anxiety and success expectancy across subjects (Dong, Liu, \u0026amp; Yang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Szucs \u0026amp; Toffalini, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, anxiety itself is not directly correlated with academic performance (Khasawneh, Gosling, \u0026amp; Williams, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, while academic anxiety is prevalent, learning outcomes are more closely tied to self-efficacy(Ferreira \u0026Eacute; et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the regression analysis, only the VC significantly predicted CMS, whereas AC and EC did not, aligning with findings that these factors do not directly influence learning outcomes or self-efficacy.\u003c/p\u003e\u003cp\u003eStudies have further suggested that learning motivation does not directly impact self-efficacy or self-appraisal (Al-Qadri, Mouas, Saraa, \u0026amp; Boudouaia, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li, Yang, Zhao, \u0026amp; Li, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu, Li, Zheng, \u0026amp; Guo, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While success expectancy often aligns with task value, the relationship between self-efficacy and learning value remains unclear (Schweder \u0026amp; Raufelder, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, interventions promoting learning value have shown mixed effects on academic self-efficacy (Symes \u0026amp; Putwain, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Regression analysis in this study revealed that the effects of VC on CMS were mediated by RMS, further supporting their role in linking motivational factors with self-regulated learning skills.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations. First, it focused on metacognitive aspects of self-regulated learning, attributing learning outcomes to internal factors without considering external influences such as institutional differences, objective evaluations, or the learning environment. According to Weiner's attribution theory, success or failure can be attributed to both internal and external factors (Weiner, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). This study prioritized self-regulated learning processes rather than objective performance, assuming that self-appraisals best reflect students\u0026rsquo; awareness of their learning. Second, the sample size for the qualitative phase was relatively small, potentially limiting generalizability. While focus groups provided rich insights, individual interviews might have captured more personalized learning experiences and strategies. Lastly, response bias cannot be ruled out, as participants may have felt compelled to present themselves favorably.\u003c/p\u003e\u003cp\u003eFuture research should explore the longitudinal impact of self-regulated learning interventions and examine how individual, institutional, and systemic factors shape learning motivations and strategies in medical training. Alternative methods, such as diary studies or real-time assessments, could provide a more nuanced understanding of how these strategies evolve in clinical environments. Further investigation into how specific self-regulated learning strategies contribute to distinct aspects of clinical performance would enhance their practical application.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the strong relationship between metacognitive and resource management strategies in clinical learning, extending the applicability of self-regulated learning theory to the context of medical education, particularly during the transition year. During the transitional PGY, RMS were found to mediate the relationship between the VC and CMS. This suggests that the self-efficacy of learning may be predominantly influenced by the development and application of learning skills, emphasizing the critical role of RMS in facilitating effective self-regulated learning during this phase.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate all the participants to join the study. We also appreciate the grants from Wan Fang Hospital, Taipei Medical University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCIL, SPH and KPT designed and conceived the study. CIL, YCW, and YJT completed the participants recruitment. YCK assisted on the data analysis and administrative work. KTP responded to editorial and reviewer comments. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Wan Fang Hospital, Taipei Medical University, which had no role in the study design, data collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. Grant No. were 112-WF-WME-02 and 111-WF-HHCE-13. There was no additional funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo data have been submitted to any open-access databases. All data supporting the study are presented in the manuscript or are available from corresponding author upon reasonable request.\u0026nbsp;\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\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Taipei Medical University-Joint Institutional Review approved the study protocol (No. N202203192). The work was carried out in accordance with the Declaration of Helsinki. All the participants included in the study gave their oral and written informed consent. There was no potential harm to participants, and anonymity was maintained. All data and results are reported anonymously to ensure participant confidentiality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Qadri, A. H., Mouas, S., Saraa, N., \u0026amp; Boudouaia, A. (2024). Measuring academic self-efficacy and learning outcomes: the mediating role of university English students\u0026apos; academic commitment. \u003cem\u003eAsian-Pacific Journal of Second and Foreign Language Education, 9\u003c/em\u003e(1), 35. doi:10.1186/s40862-024-00253-5\u003c/li\u003e\n\u003cli\u003eBandura, A. (1989). 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A social cognitive view of self-regulated academic learning. \u003cem\u003eJournal of Educational Psychology, 81\u003c/em\u003e(3), 329. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Self-Regulated Learning, Resource Management Strategies, Transitional Year, Postgraduate Training, Learning Strategies","lastPublishedDoi":"10.21203/rs.3.rs-7738977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7738977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSelf-regulated learning (SRL) is a dynamic process closely linked to work tasks and plays a critical role in helping physicians adapt during the transitional year of medical training. While SRL has been extensively studied in residency, its mechanisms among postgraduate trainees remain underexplored. This study investigates SRL strategies among transitional year physicians and evaluates a structured assessment tool tailored for this population. A mixed-methods approach was adopted, integrating quantitative and qualitative analyses. The quantitative phase examined clinical learning experiences before, during, and after the transition from undergraduate to postgraduate training. The qualitative phase involved semi-structured interviews exploring five SRL components: resource management strategies, cognitive and metacognitive strategies, expectancy, value, and affective components. A total of 76 transitional year physicians completed the questionnaire, and 17 participated in focus group discussions. Data analysis included item refinement and linear regression modeling to assess the applicability of SRL. Results indicated that resource management strategies fully mediated the relationship between value components and cognitive/metacognitive strategies, supported by Pearson\u0026rsquo;s correlation analysis. Qualitative findings reinforced these relationships, highlighting the role of structured learning strategies in enhancing self-efficacy during the transition. This study underscores the significance of SRL in medical education, particularly in facilitating the transition from undergraduate to postgraduate training. The findings emphasize the critical role of resource management strategies in promoting effective learning and adapting to the demands of clinical practice.\u003c/p\u003e","manuscriptTitle":"Self-Regulated Learning in Transitional Year Physicians: The Mediating Role of Resource Management Strategies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 10:37:56","doi":"10.21203/rs.3.rs-7738977/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-01T22:48:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T18:40:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90307177306854328898440670058869339636","date":"2025-11-18T13:27:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162846777820837437079419483863407563652","date":"2025-11-11T15:09:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T14:32:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-15T10:15:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T10:12:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T10:10:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-09-29T07:00:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbf261b0-0af9-4e24-a246-40dc8c3f39d7","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-21T10:37:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 10:37:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7738977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7738977","identity":"rs-7738977","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}