Voluntary Skills Lab Practice and OSCE Performance: Graded Association, Station Heterogeneity, and Reduced Risk of Underperformance

Voluntary Skills Lab Practice and OSCE Performance: Graded Association, Station Heterogeneity, and Reduced Risk of Underperformance | 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 Voluntary Skills Lab Practice and OSCE Performance: Graded Association, Station Heterogeneity, and Reduced Risk of Underperformance Minoru Hattori, Naoko Hasunuma, Yuko Nakashima This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9107817/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Skills laboratories offer students the opportunity to practice clinical skills before high-stakes examinations. However, evidence regarding the correlation between voluntary (self-directed) practice and enhanced Objective Structured Clinical Examination (OSCE) performance remains limited, and few studies have examined graded associations. This study aims to investigate the graded association between voluntary skills lab practice frequency and OSCE performance, including the potential association between practice and a diminished risk of underperformance. Methods We analyzed data from 126 Japanese medical school fourth-year students (2022 cohort). We recorded practice frequency (0–8 sessions over a two-week time-frame), Computer-Based Testing (CBT) scores, sex, admission pathway, and grade retention history; no data were missing. The OSCE scores from eight stations were analyzed using generalized estimating equations (GEE) with an exchangeable working correlation structure and cluster-robust standard errors. Secondary analyses examined at-risk performance (score ≤ 3, corresponding to “borderline” or below on the CATO global rating scale) using a linear probability model within the GEE framework. Station-specific effects were assessed through a practice-by-station interaction model. Results Fifty-two percent of students (n = 65) participated in voluntary practice, with a mean of 2.6 sessions among practitioners. In the GEE analysis, adjusting for CBT, sex, admission pathway, and grade retention, each additional practice session correlated with a 0.034-point increase in OSCE station scores (95% CI: 0.003–0.066, p = 0.032). Practice was also correlated with a reduced probability of at-risk performance (≤ 3 on the 6-point scale), with each session linked to a risk difference of − 0.7 percentage points (95% CI: −1.3 to − 0.0, p = 0.044). Station-specific analyses revealed the most significant correlation for clinical procedures (β = 0.112, 95% CI: 0.029–0.195, p = 0.037). The overall effect size was Cohen’s d = 0.36. Conclusions Voluntary skills lab practice correlates with higher OSCE performance in a graded fashion. The correlation is particularly evident in procedural skills and is associated with a reduced risk of underperformance. Although self-selection bias cannot be entirely eliminated, these findings support institutional policies that enhance access to voluntary practice opportunities. OSCE skills laboratory voluntary practice graded association GEE medical education clinical skills Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Clinical skills education is fundamental to undergraduate medical training, with the Objective Structured Clinical Examination (OSCE) emerging as the global benchmark for assessing clinical competence [ 1 , 2 ]. Skills laboratories offer controlled environments where students can practice physical examination techniques, clinical procedures, and communication skills. Although structured, curriculum-embedded practice sessions are standard, numerous institutions also offer voluntary (self-directed) practice opportunities beyond scheduled classes [ 3 ]. Despite the intuitive appeal of “practice makes perfect,” empirical evidence correlating voluntary skills lab practice to OSCE outcomes is unexpectedly scarce. Although randomized trials have demonstrated that structured simulation training with deliberate practice enhances procedural skills, [ 4 , 5 ] these studies focused on curriculum-integrated, instructor-led interventions rather than voluntary, self-directed practice. Voluntary practice may also be understood through the lens of self-regulated learning, as students who choose to attend additional sessions are likely identifying their own weaknesses and proactively seeking opportunities to address them [ 6 ]. A few studies have examined self-directed practice, including evidence of a graded association for hands-on “study hall” practice in central line insertion [ 7 ]. However, the most pertinent research is that of Tsikas et al. (2024) [ 8 ], who employed propensity score matching at Hannover Medical School and discovered that voluntary practice correlated with higher overall OSCE scores, with variations depending on station type. However, the Tsikas study, like most previous research, classified practice as a binary variable (any vs. none), thereby leaving several significant questions unresolved. First, is there a graded association, such that increased practice correlates with progressively enhanced outcomes? Second, is practice correlated not only with a higher mean performance but also with a lower risk of underperformance? Third, do these associations differ among various types of clinical skills? This study addresses these gaps by utilizing data from a Japanese medical school, where daily practice logs enabled us to consider practice as a continuous rather than a binary variable. We adopt a multilevel analytical framework that concurrently evaluates performance across all OSCE stations, thereby leveraging information that is overlooked when stations are analyzed in isolation. Besides examining mean performance, we assess whether practice correlates with a diminished risk of underperformance, a clinically significant outcome that has been largely overlooked in the literature. From an educational perspective, underperformance is important not only because it signals students who may require additional support, but also because deficits in basic clinical and communication skills may have implications for the quality and safety of future patient care [ 9 ]. Our specific research questions are (1) What is the graded association between voluntary practice frequency and OSCE station scores, after controlling for baseline academic ability and demographic factors? (2) Does practice correlate with a reduced probability of at-risk performance? (3) Does the correlation between practice and performance differ among various types of OSCE stations? Methods Setting and Participants This retrospective cohort study was conducted at Hiroshima School of Medicine, Japan. All 126 fourth-year medical students from the 2022 academic year who participated in the OSCE in November 2022 were included. Data were complete for all students; no variables exhibited missing values. In the Japanese medical education system, the OSCE serves as a prerequisite for clinical clerkships. At this institution, the examination is conducted at the conclusion of the fourth year in a six-year curriculum. Ethical approval was granted by the Institutional Review Board (E2025-0274). Outcome: OSCE Performance The OSCE comprised eight stations: medical interview (communication), head and neck examination, chest examination, vital signs assessment, abdominal examination, neurological examination, basic clinical procedures, and emergency care. Trained examiners scored each station using a 6-point global rating scale (1–6). Despite this scale being technically ordinal, scores were regarded as approximately continuous for the primary analyses, aligning with common practice in medical education research [ 10 ]. The sensitivity analyses and limitations sections address the robustness of this assumption. Exposure: Voluntary Practice The skills laboratory was available for voluntary group practice sessions over an 11-day period (October 19–November 1, 2022) preceding the OSCE (November 4–5). Student attendance was documented daily through sign-in logs. The primary exposure variable was the total count of attended practice sessions (range: 0–8). Secondary classifications comprised a binary variable (any practice vs. none) and a three-level categorical variable (0, 1, or ≥ 2 sessions). Covariates The following potential confounders were included: (a) CBT score, a nationally standardized computer-based knowledge examination administered before the OSCE, which provides a baseline academic ability measure (standardized as z-scores for analysis); (b) sex; (c) admission pathway, specifically regional quota ( chiiki-waku , a preferential admission pathway for students committed to practicing in underserved areas) versus general admission; and (d) grade retention history (whether the student had repeated an academic year). Statistical Analysis Primary analysis. Given the clustered data structure (eight station scores nested within each student), we utilized generalized estimating equations (GEE) with a Gaussian family and an identity link function. An exchangeable working correlation structure was specified, indicating the assumption that the correlation between any pair of stations within a student remains relatively constant. The estimated working correlation was ρ = 0.14. Cluster-robust (sandwich) standard errors were employed to ensure valid inference despite potential misspecification of the working correlation structure [ 11 ]. The model incorporated station fixed effects (dummy variables for stations 2–8, with the medical interview station as the reference), the practice count (continuous), and all covariates. This approach leverages all 1,008 observations (126 students × 8 stations) while effectively addressing within-student correlation. To verify robustness, we conducted sensitivity analyses using independence and first-order autoregressive correlation structures (AR). Secondary analysis: at-risk performance. At-risk performance was defined as a station score of ≤ 3 on the 6-point global rating scale utilized in the Japanese national OSCE. This threshold aligns with a rating of “borderline” or lower on the scale defined by the Common Achievement Tests Organization (CATO), where 4 denotes “pass level,” 3 denotes “borderline,” 2 denotes “fail but improvable,” and 1 denotes “clear fail.” The probability of at-risk performance was modeled using a linear probability model (LPM) within the GEE framework, employing the same covariates and structure as in the primary analysis. This model’s regression coefficient directly estimates the risk difference (RD), which is defined as the change in the probability of at-risk performance per additional practice session, and is presented with 95% confidence intervals. Station heterogeneity. To formally assess whether the practice–performance relationship differed among stations, we fitted a GEE model with practice-by-station interaction terms and conducted a Wald test to evaluate the joint significance of the seven interaction parameters (stations 2–8 vs. the reference station). Station-specific practice effects with 95% confidence intervals were obtained from this interaction model. Station-specific effect sizes (Cohen’s d) were derived from individual ordinary least squares (OLS) regression models that compared students who engaged in (any) practice versus those who did not. Sensitivity analyses. To assess robustness, we conducted two sensitivity analyses. First, inverse probability of treatment weighting (IPTW) was employed to estimate the causal effect of practice within a marginal structural model framework. Stabilized weights were obtained from propensity scores for binary treatment or the generalized propensity score for continuous treatment, utilizing the ratio of the marginal to the conditional density of practice count. The IPTW-weighted GEE model incorporated station fixed effects and the practice variable, excluding additional covariates, as confounding was mitigated through weighting. Covariate balance was assessed through standardized mean differences (SMD). Second, a three-level categorical exposure variable (0, 1, or ≥ 2 sessions) was analyzed within the GEE framework, incorporating covariates and station fixed effects, with non-practitioners serving as the reference group. A Jonckheere–Terpstra test was employed to assess the significance of a monotonic trend across groups. Model diagnostics. Variance inflation factors (VIFs) were computed to assess multicollinearity (all VIFs < 1.3). Non-linearity was assessed by incorporating a quadratic term for the practice count (p = 0.827). Analyses were conducted using Python 3.11 (scipy, numpy) and verified in R 4.3 (geepack). All statistical tests were bilateral, with a significance level of α = 0.05. Results Participant Characteristics Table 1 delineates participant characteristics categorized by practice group. Out of 126 students, 65 (51.6%) participated in at least one voluntary practice session, with a mean of 2.6 sessions among practitioners (SD = 1.8). The practice frequency distribution was right-skewed (Fig. 1 A): 61 students (48.4%) did not practice, 24 (19.0%) practiced once, 16 (12.7%) practiced twice, and 25 (19.8%) practiced three or more times. Most practitioners (72%) practiced only during the final week preceding the OSCE (Fig. 1 B). Table 1 Participant Characteristics by Voluntary Practice Group Variable All (N = 126) Practice (n = 65) No Practice (n = 61) p Effect size CBT score, M ± SD 507 ± 104 534 ± 100 479 ± 100 .003 0.54 OSCE mean, M ± SD 4.44 ± 0.44 4.51 ± 0.37 4.36 ± 0.49 .074 0.36 Female, n (%) 40 (31.7) 20 (30.8) 20 (32.8) .850 0.04 Regional quota, n (%) 23 (18.3) 14 (21.5) 9 (14.8) .363 0.17 Grade retention, n (%) 13 (10.3) 7 (10.8) 6 (9.8) 1.00 0.03 Practice sessions, M ± SD 2.6 ± 1.8 2.6 ± 1.8 — 0 sessions, n (%) 61 (48.4) 61 (48.4) 1 session 24 (19.0) 24 (19.0) 2 sessions 16 (12.7) 16 (12.7) ≥3 sessions 25 (19.8) 25 (19.8) Note. Data were complete for all 126 students (no missing values). CBT = Computer-Based Testing. Continuous variables: Mann-Whitney U test. Categorical variables: Fisher’s exact test. Effect size= Cohen’s d (continuous variables) or Cohen’s h (proportions). (A) Histogram illustrating the number of practice sessions attended by each student. Approximately half (48.4%) of the students did not attend sessions. Among practitioners, the distribution was right-skewed (median = 2, range 1–8). (B) Pie chart showing practice timing: 37% of students practiced only during the late period (10/27–11/1), while 13% practiced during both early and late periods (10/19–11/1). Students who practiced had significantly higher CBT scores than non-practitioners (534 ± 100 vs. 479 ± 100, p = 0.003, d = 0.54). No significant differences were observed in sex distribution (p = 0.850), admission pathway (p = 0.363), or grade retention rates (p = 1.000) among the groups. In a logistic regression predicting practice participation, CBT was the only significant predictor (OR = 1.97 per SD, 95% CI: 1.28–3.03, p = 0.002; Table S1 ); practice rates increased across CBT tertiles (low: 38%, middle: 52%, high: 64%). Among practitioners, however, CBT exhibited no significant correlation with the number of sessions attended (r = 0.18, p = 0.160). Primary Analysis: GEE Table 2 displays the GEE results. In the fully adjusted model (Model 2), each additional practice session correlated with a 0.034-point increase in the OSCE station score (robust SE = 0.016, 95% CI: 0.003–0.065, p = 0.032). A monotonic upward trend in mean scores among practice groups was noted (Fig. 2 A; Spearman ρ=+0.194, p=.029). This correlation was independent of CBT score (β = 0.129, p = 0.007), sex (β = 0.168, p = 0.024), and grade retention (β=−0.330, p = 0.034). The regional quota admission pathway exhibited no significant correlation with performance (β = 0.059, p = 0.475). The results were essentially identical under both independence and AR working correlation structures. The overall effect size comparing any practice to no practice was Cohen’s d = 0.36. To contextualize the magnitude, a student attending three sessions (approximately the median number among practitioners) is predicted to score 0.10 points higher per station than a non-practitioner, while a student attending five sessions is predicted to score 0.17 points higher, which is equivalent to approximately 0.4 SD of the observed score distribution (Fig. 2 B). Table 2 GEE Analysis: Association Between Practice Frequency and OSCE Station Score Variable Model 1 Model 2 β SE p β SE p Female 0.163 0.074 .030* 0.168 0.074 .024* Regional quota 0.073 0.082 .372 0.059 0.082 .475 Grade retention −0.302 0.149 .044* −0.330 0.154 .034* CBT (standardized) 0.149 0.046 .001** 0.129 0.047 .007** Practice count — — — 0.034 0.016 .032* Note. GEE with exchangeable working correlation (ρ = 0.14) and cluster-robust standard errors. N = 1,008 observations (126 students × 8 stations). Station fixed effects included but not shown. Model 1: covariates only. Model 2: covariates + practice count. Results were essentially identical under independence and AR correlation structures. *p<.05, **p<.01. (A) Boxplots illustrating mean OSCE scores categorized by practice group (0, 1, or 2 + sessions), featuring individual data points (jittered) and group means (diamonds). (B) Scatter plot depicting individual OSCE mean scores against practice session count, featuring group means (diamonds) and OLS regression line (β = 0.050, unadjusted). Secondary Analysis: At-Risk Reduction Figure 3 presents descriptive station-level at-risk rates in the practitioner and non-practitioner groups. Overall, practitioners showed a lower proportion of at-risk performances than non-practitioners (4.6% vs 9.4%). The magnitude of this difference varied across stations, indicating that the association between voluntary practice and reduced risk of underperformance was not uniform across the OSCE. Visual inspection suggested more pronounced differences in some performance-intensive stations, particularly clinical procedures and emergency. In the adjusted GEE linear probability model, each additional practice session was associated with a 0.7-percentage-point reduction in the probability of at-risk performance (95% CI: −1.3 to − 0.0, p = .044). Station-Specific Effects and Heterogeneity Table 3 presents station-specific outcomes derived from two complementary approaches. The GEE interaction model (practice × station) produced station-specific practice effect estimates, whereas individual OLS models generated effect sizes (Cohen’s d) for each station (Fig. 4 ). The formal Wald test for the joint significance of the seven practice-by-station interaction terms yielded an non-significant result (χ ² (7) = 7.77, p = 0.353). The interaction model’s point estimates indicated the strongest association for basic clinical procedures (β = 0.112, 95% CI: 0.029–0.195), while the association for emergency care was more modest (β = 0.052, 95% CI: −0.037–0.140). Communication and physical examination stations exhibited smaller, non-significant associations. Table 3 Station-Specific Practice Effects: GEE Interaction Model and Individual OLS Models Station GEE interaction OLS Type β 95% CI β p d [95% CI] Medical interview Comm. 0.024 [− 0.037, 0.085] 0.020 .590 0.23 [− 0.12, 0.58] Head & neck PE 0.022 [− 0.031, 0.075] 0.032 .232 0.49 [0.14, 0.84] Chest PE 0.009 [− 0.042, 0.060] 0.018 .588 −0.20 [− 0.55, 0.15] Vitals PE 0.035 [− 0.044, 0.113] 0.031 .492 0.16 [− 0.19, 0.51] Abdomen PE −0.009 [− 0.058, 0.039] −0.001 .976 −0.12 [− 0.47, 0.23] Neurological PE 0.030 [− 0.025, 0.085] 0.031 .338 0.24 [− 0.11, 0.59] Clin. procedures Proc. 0.112 [0.029, 0.195] 0.112 .037* 0.40 [0.04, 0.75] Emergency Proc. 0.052 [− 0.037, 0.140] 0.031 .499 0.29 [− 0.06, 0.64] Note. GEE interaction model: practice × station interaction terms included; β represents the estimated practice effect for each station derived from the full interaction model. Wald test for heterogeneity across stations: χ ² (7) = 7.77, p=.353. OLS: adjusted for sex, regional quota, grade retention, and CBT (z-score). d = Cohen’s d (any practice vs. none) with 95% CI. *OLS p<.05 (covariate-adjusted). Note that the 95% CI for d is based on the unadjusted comparison; Head & neck (d = + 0.49) was significant by unadjusted t-test (p=.007) but not after covariate adjustment (OLS p=.232). Marker shapes indicate station type: square = communication, circles = physical examination, triangles = procedural, diamond = overall GEE-based estimate. The 95% CIs for d are based on the unadjusted group comparison; Head and neck (d = + 0.49) was significant in the unadjusted t-test (p = .007) but not after covariate adjustment (OLS p = .232). When categorizing stations by type, procedural stations (clinical procedures, emergency care) exhibited the most significant practice effects (d = 0.40 and 0.29, respectively), while physical examination stations demonstrated variable results (d ranging from − 0.20 to 0.49), and the communication station (medical interview) revealed a minor effect (d = 0.23) (Fig. 5 ). Sensitivity Analyses IPTW using the generalized propensity score for continuous treatment achieved excellent covariate balance, with all standardized mean differences below 0.03 (CBT: 0.545→0.012; see Table S2). The IPTW-weighted estimate aligned with the primary analysis (β = 0.040, SE = 0.020, 95% CI: 0.000–0.079, p = 0.051). The IPTW-weighted estimate for binary treatment (any practice vs. none) was directionally consistent but non-significant (β = 0.072, SE = 0.080, p = 0.371). In the three-level categorical analysis (0, 1, or ≥ 2 sessions), the adjusted mean differences relative to non-practitioners were 0.066 (p = 0.390) for one session and 0.099 (p = 0.219) for two or more sessions. Despite individual group comparisons lacking significance, the Jonckheere–Terpstra test validated a significant monotonic trend (z = 2.06, p = 0.039). Effect sizes increased with session count (d = 0.26 for one session; d = 0.39 for two or more sessions compared to non-practitioners). Discussion Summary of Findings This study demonstrates a positive graded association between voluntary skills lab practice and OSCE performance. Utilizing GEE to leverage the comprehensive data from eight OSCE stations, we determined that each additional practice session correlated with a 0.034-point increase in station score (p = 0.032) and a 0.7 percentage-point decrease in the risk of at-risk performance (p = 0.044), after controlling for baseline academic ability, sex, admission pathway, and grade retention history. The crude at-risk rate for non-practitioners (9.4%) was approximately twice that of practitioners (4.6%). Point estimates indicated the strongest correlation for procedural skills. Comparison with Prior Literature The correlation between skills lab training and clinical performance have been thoroughly examined in controlled contexts. Randomized trials have demonstrated that structured simulation-based training with deliberate practice enhances procedural skill execution [ 5 , 12 ]. Moreover, a meta-analysis by McGaghie et al. confirmed that simulation with deliberate practice yields superior outcomes than those of traditional clinical education [ 13 ]. However, these studies focused on instructor-led, curriculum-embedded training rather than voluntary, self-directed practice; therefore, the findings may not be applicable to self-directed contexts. The evidence on voluntary practice and OSCE outcomes is remarkably sparse. Tsikas et al. employed propensity score matching at Hannover Medical School and found that voluntary practice correlated with increased overall OSCE performance, particularly benefiting procedural skills, while showing no significant disparity in communication stations [ 8 ]. Our findings generally align with this pattern, and the convergence across two distinct educational systems (Germany and Japan) bolsters confidence in its generalizability. Notably, the Japanese pre-clinical OSCE is a nationally standardized examination administered by CATO across all 82 medical schools, featuring uniform station designs, certified examiners, and a national passing standard [ 14 ], whereas most previous studies, including Tsikas et al., focused on institution-specific assessments. This distinction may enhance the internal validity of our findings, while also prompting inquiries regarding direct comparability with institution-designed OSCEs abroad. Our study enhances the current evidence in two specific ways. First, although Tsikas et al. considered practice as a binary variable, our continuous measure reveals a graded association without any indication of a ceiling effect. This graded pattern was corroborated by a significant monotonic trend across the three practice groups (Jonckheere–Terpstra p = 0.039) and by the IPTW analysis, which yielded a nearly identical estimate (β = 0.040) after attaining excellent covariate balance. In conjunction with Shafer et al.’s finding of a graded association for self-directed practice in central line insertion [ 7 ], this implies that graded associations may be a general feature of self-directed clinical practice. Second, the at-risk analysis introduces a dimension that, to the best of our knowledge, has not been examined in prior studies of voluntary practice: the crude at-risk rate among non-practitioners (9.4%) was approximately double that of practitioners (4.6%), indicating that practice may particularly assist students in avoiding the lower tail of performance. The formal heterogeneity test was non-significant (p = 0.353), thus caution is advised regarding station-type differences. Communication skills may be less amenable to self-directed practice because they require interactive feedback, whereas procedural skills can be more effectively rehearsed with simulation equipment. This interpretation aligns with Tsikas et al.’s findings. Self-Selection Bias A significant constraint in all observational studies of voluntary practice is self-selection. In our analysis, CBT was the only significant predictor of practice participation (OR = 1.97 per SD, p = 0.002), with practice rates ranging from 38% in the lowest CBT tertile to 64% in the highest. Notably, among those practiced, CBT did not predict the number of sessions (r = 0.18, p = 0.160), indicating that baseline ability influences the choice to practice but not the intensity of engagement. One interpretation posits that CBT performance partially reflects self-regulated learning (SRL) capacity: a scoping review revealed that higher SRL levels in medical students correlated with both stronger academic achievement and improved clinical skills [ 15 ]. Students excelling in comprehensive knowledge examinations may also demonstrate greater proficiency in identifying gaps and pursuing practice opportunities. If so, the observed correlation between voluntary practice and OSCE performance may partially reflect a shared underlying trait of self-regulation rather than a purely causal effect of practice itself. Practical Implications Although these considerations preclude causal claims, the consistency of the association across various analytical approaches (GEE, IPTW, categorical trend test) and its alignment with international evidence indicate that the findings possess practical significance. Medical schools should ensure accessibility to skills laboratories for voluntary practice before OSCE examinations. Students should be encouraged to participate in multiple sessions due to the graded pattern. Practice resources should be strategically allocated to procedural stations where the correlation seems most pronounced. Considering the self-selection pattern, targeted encouragement of underperforming students may aid in reducing performance disparities. Limitations Despite its contributions, this study has several limitations. First, this is an observational study, and unmeasured confounders (such as motivation and study habits) may bias the results despite covariate adjustment and IPTW. Second, our exposure metric captured only attendance at the skills laboratory. Practices conducted elsewhere, such as self-study using textbooks or videos at home, were not recorded. This exposure misclassification may attenuate the estimated association. Third, the use of a single-institution sample (N = 126) limits the generalizability of the findings and reduces the statistical power to detect interaction effects. A major limitation of this study, as well as the broader literature, is that practice was measured solely by session counts, with no information on content, duration, or pedagogical structure. Our effect estimates probably reflect an average across heterogeneous practice experiences, and the actual advantage of high-quality practice may be greater. Future research should progress beyond frequency counts to encompass richer characterizations of practice behavior, incorporating structured logs, direct observation, and comparison of various practice formats. Finally, the limited 11-day practice period prevented the analysis of longer-term patterns. Conclusion Voluntary skills lab practice correlates positively with OSCE performance in a graded fashion among Japanese medical students. The association correlates with a reduced risk of underperformance and is most pronounced for procedural skills, aligning with international evidence. Although the observational design precludes causal inferences, these findings endorse institutional initiatives to facilitate and promote voluntary practice opportunities, emphasizing equitable access for the entire student population. Abbreviations AR First-order autoregressive CBT Computer-Based Testing CATO Common Achievement Tests Organization CI Confidence interval GEE Generalized estimating equations IPTW Inverse probability of treatment weighting LPM Linear probability model OLS Ordinary least squares OR Odds ratio OSCE Objective Structured Clinical Examination RD Risk difference SD Standard deviation SE Standard error SMD Standardized mean difference SRL Self-regulated learning VIF Variance inflation factor Declarations Acknowledgements We gratefully acknowledge Yukari Shinjo for her assistance in compiling the data. The authors used ChatGPT (OpenAI, GPT-5.2 Thinking) during manuscript preparation to assist with language editing, wording refinement, and organization of draft text. The tool was used to improve clarity and readability. All generated output was carefully reviewed, verified, and revised by the authors, who are fully responsible for the content of the manuscript. Ethics approval and consent to participate This study was approved by the Institutional Review Board of Hiroshima University (Approval No. E2025-0274). The study was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. The requirement for informed consent was waived by the Institutional Review Board because this retrospective study used existing educational records and involved minimal risk to participants. Consent for publication Not applicable. Data availability The datasets used and/or analyzed during the current study are not publicly available because they contain potentially identifiable educational information and are subject to institutional and ethical restrictions. Competing interests The authors declare no conflicts of interest associated with this manuscript Funding This work was supported by JSPS KAKENHI Grant Number 23K09550. Authors’ contributions MH conceived the study, analyzed the data, and drafted the manuscript. NH contributed to the study design, interpretation of the data, and critical revision of the manuscript. YN contributed to data interpretation and critical revision of the manuscript. All authors read and approved the final manuscript. References Harden RM, Gleeson FA. Assessment of clinical competence using an objective structured clinical examination (OSCE). Med Educ. 1979;13(1):41–54. Khan KZ, Ramachandran S, Gaunt K, Pushkar P. The Objective Structured Clinical Examination (OSCE): AMEE Guide 81. Part I: an historical and theoretical perspective. Med Teach. 2013;35(9):e1437–1446. Lynagh M, Burton R, Sanson-Fisher R. A systematic review of medical skills laboratory training: where to from here? Med Educ. 2007;41(9):879–87. Brydges R, Nair P, Ma I, Shanks D, Hatala R. Directed self-regulated learning versus instructor-regulated learning in simulation training. Med Educ. 2012;46(7):648–56. Bosse HM, Mohr J, Buss B, Krautter M, Weyrich P, Herzog W, Junger J, Nikendei C. The benefit of repetitive skills training and frequency of expert feedback in the early acquisition of procedural skills. BMC Med Educ. 2015;15:22. Patel R, Tarrant C, Bonas S, Yates J, Sandars J. The struggling student: a thematic analysis from the self-regulated learning perspective. Med Educ. 2015;49(4):417–26. Diederich E, Lineberry M, Schott V, Broski J, Alsayer A, Eckels KA, Murray MJ, Huynh W, Thomas LA. Putting the learning in pre-learning: effects of a self-directed study hall on skill acquisition in a simulation-based central line insertion course. Adv Simul (Lond). 2023;8(1):21. Tsikas SA, Afshar K, Fischer V. Does voluntary practice improve the outcome of an OSCE in undergraduate medical studies? A Propensity Score Matching approach. PLoS ONE. 2024;19(10):e0312387. Ochsmann EB, Zier U, Drexler H, Schmid K. Well prepared for work? Junior doctors' self-assessment after medical education. BMC Med Educ. 2011;11:99. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42(1):121–30. Liang KY, Zeger SL. Longitudinal Data-Analysis Using Generalized Linear-Models. Biometrika. 1986;73(1):13–22. Lund F, Schultz JH, Maatouk I, Krautter M, Moltner A, Werner A, Weyrich P, Junger J, Nikendei C. Effectiveness of IV cannulation skills laboratory training and its transfer into clinical practice: a randomized, controlled trial. PLoS ONE. 2012;7(3):e32831. McGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Acad Med. 2011;86(6):706–11. Nishigori H. Medical education in Japan. Med Teach. 2024;46(sup1):S4–10. Cho KK, Marjadi B, Langendyk V, Hu W. The self-regulated learning of medical students in the clinical environment - a scoping review. BMC Med Educ. 2017;17(1):112. Additional Declarations No competing interests reported. Supplementary Files supplementary2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9107817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622400939,"identity":"f2884a35-e3b1-4746-83a3-964f9be577ae","order_by":0,"name":"Minoru Hattori","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Minoru","middleName":"","lastName":"Hattori","suffix":""},{"id":622400941,"identity":"6724db27-a403-419a-88eb-c5485a1313e3","order_by":1,"name":"Naoko Hasunuma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACAxiDX4KxAUgd4IFLgfn4tEjOIFmLwQ0wdYCww8zZjz+TLmzbJm98u7ntw4c/d2R02w+wSTDU2DEwz8ZujWVPjpn0zLbbhtvuHGyeObPtGY/ZmQSglmPJDIxzsFtpcCCHTZq37TbjthuJzcy8DYd5zA7kf5NgYDvAwDgjAbuW88+fgbTYb54B1MLzB6jl/AOgLf/waLmRYAbSkrhBAqSFDajlBtBhjG34tLwxtuY5dzt5BtBhjDPbQFoeMFsk9iXz4PTL+fSHt3nKbtv2z0h/zPDhz2F7s/MJjDc+fLOTM8QRYjgA0Ek8hjNI0QEG8hIkaxkFo2AUjILhCQD2X2JF44cLuQAAAABJRU5ErkJggg==","orcid":"","institution":"Hiroshima University","correspondingAuthor":true,"prefix":"","firstName":"Naoko","middleName":"","lastName":"Hasunuma","suffix":""},{"id":622400942,"identity":"4ea777f4-be0e-450f-9416-1649e1716ec4","order_by":2,"name":"Yuko Nakashima","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Yuko","middleName":"","lastName":"Nakashima","suffix":""}],"badges":[],"createdAt":"2026-03-12 19:08:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9107817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9107817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106978552,"identity":"022933b2-f9d6-4b7f-8586-781484217bac","added_by":"auto","created_at":"2026-04-15 11:17:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129129,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of voluntary practice sessions and timing patterns.\u003c/p\u003e\n\u003cp\u003e(A) Histogram illustrating the number of practice sessions attended by each student. Approximately half (48.4%) of the students did not attend sessions. Among practitioners, the distribution was right-skewed (median = 2, range 1–8). (B) Pie chart showing practice timing: 37% of students practiced only during the late period (10/27–11/1), while 13% practiced during both early and late periods (10/19–11/1).\u003c/p\u003e","description":"","filename":"Figure1practicedistribution.png","url":"https://assets-eu.researchsquare.com/files/rs-9107817/v1/82e07c021ab0855f7eceb4b0.png"},{"id":106994192,"identity":"28dc3f55-f09e-42b8-8824-67ae3743e53a","added_by":"auto","created_at":"2026-04-15 15:06:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156756,"visible":true,"origin":"","legend":"\u003cp\u003eGraded association between practice frequency and OSCE performance.\u003c/p\u003e\n\u003cp\u003e(A) Boxplots illustrating mean OSCE scores categorized by practice group (0, 1, or 2+ sessions), featuring individual data points (jittered) and group means (diamonds). (B) Scatter plot depicting individual OSCE mean scores against practice session count, featuring group means (diamonds) and OLS regression line (β = 0.050, unadjusted).\u003c/p\u003e","description":"","filename":"Figure2doseresponse.png","url":"https://assets-eu.researchsquare.com/files/rs-9107817/v1/47e0c960ba2dd125d2134204.png"},{"id":106994274,"identity":"4bd92f9a-8da0-4a24-a880-df3a1f7ec3ad","added_by":"auto","created_at":"2026-04-15 15:07:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113840,"visible":true,"origin":"","legend":"\u003cp\u003eStation-specific at-risk rates (score ≤ 3 on the 6-point scale) for both practitioners and non-practitioners. The most significant reductions were observed in clinical procedures and emergency stations. Overall, the at-risk rate was 9.4% for non-practitioners and 4.6% for practitioners. The adjusted risk difference was −0.7 percentage points for each additional session (GEE linear probability model, p = .044).\u003c/p\u003e","description":"","filename":"Figure3atriskrates.png","url":"https://assets-eu.researchsquare.com/files/rs-9107817/v1/cee4f4b66dccc32b803b0ec8.png"},{"id":106994239,"identity":"f0f0a298-169c-424b-b6bf-9ca4e399e160","added_by":"auto","created_at":"2026-04-15 15:06:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138905,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of station-specific practice effect sizes with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eMarker shapes indicate station type: square = communication, circles = physical examination, triangles = procedural, diamond = overall GEE-based estimate. The 95% CIs for d are based on the unadjusted group comparison; Head and neck (d = +0.49) was significant in the unadjusted t-test (p = .007) but not after covariate adjustment (OLS p = .232).\u003c/p\u003e","description":"","filename":"Figure4forestplot.png","url":"https://assets-eu.researchsquare.com/files/rs-9107817/v1/d8718146adafa8dd80cc19c7.png"},{"id":107704827,"identity":"fe09626a-585d-4465-a243-dc3387603216","added_by":"auto","created_at":"2026-04-24 08:59:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":248830,"visible":true,"origin":"","legend":"\u003cp\u003eRadar chart depicting standardized performance profiles for practitioners and non-practitioners across eight OSCE stations. The practice group consistently exhibited higher scores on procedural, interview, and head and neck stations, whereas both groups exhibited comparable performance or inverse trends on chest and abdominal examination stations.\u003c/p\u003e","description":"","filename":"Figure5radarchart.png","url":"https://assets-eu.researchsquare.com/files/rs-9107817/v1/4221cdb3b9ed67a1028d617a.png"},{"id":107868837,"identity":"5dee1c81-ceed-4617-b7a3-43ea399fee9d","added_by":"auto","created_at":"2026-04-27 07:34:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9107817/v1/5c780bf8-e0a9-4348-a926-967c32181937.pdf"},{"id":107480313,"identity":"5a8b2294-4893-41a3-8ca8-19a3ad3b9d08","added_by":"auto","created_at":"2026-04-22 02:08:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18126,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9107817/v1/728f5fe4f1877f30c2e68208.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Voluntary Skills Lab Practice and OSCE Performance: Graded Association, Station Heterogeneity, and Reduced Risk of Underperformance","fulltext":[{"header":"Background","content":"\u003cp\u003eClinical skills education is fundamental to undergraduate medical training, with the Objective Structured Clinical Examination (OSCE) emerging as the global benchmark for assessing clinical competence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Skills laboratories offer controlled environments where students can practice physical examination techniques, clinical procedures, and communication skills. Although structured, curriculum-embedded practice sessions are standard, numerous institutions also offer voluntary (self-directed) practice opportunities beyond scheduled classes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the intuitive appeal of \u0026ldquo;practice makes perfect,\u0026rdquo; empirical evidence correlating voluntary skills lab practice to OSCE outcomes is unexpectedly scarce. Although randomized trials have demonstrated that structured simulation training with deliberate practice enhances procedural skills, [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] these studies focused on curriculum-integrated, instructor-led interventions rather than voluntary, self-directed practice. Voluntary practice may also be understood through the lens of self-regulated learning, as students who choose to attend additional sessions are likely identifying their own weaknesses and proactively seeking opportunities to address them [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A few studies have examined self-directed practice, including evidence of a graded association for hands-on \u0026ldquo;study hall\u0026rdquo; practice in central line insertion [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the most pertinent research is that of Tsikas et al. (2024) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], who employed propensity score matching at Hannover Medical School and discovered that voluntary practice correlated with higher overall OSCE scores, with variations depending on station type.\u003c/p\u003e \u003cp\u003eHowever, the Tsikas study, like most previous research, classified practice as a binary variable (any vs. none), thereby leaving several significant questions unresolved. First, is there a graded association, such that increased practice correlates with progressively enhanced outcomes? Second, is practice correlated not only with a higher mean performance but also with a lower risk of underperformance? Third, do these associations differ among various types of clinical skills?\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by utilizing data from a Japanese medical school, where daily practice logs enabled us to consider practice as a continuous rather than a binary variable. We adopt a multilevel analytical framework that concurrently evaluates performance across all OSCE stations, thereby leveraging information that is overlooked when stations are analyzed in isolation. Besides examining mean performance, we assess whether practice correlates with a diminished risk of underperformance, a clinically significant outcome that has been largely overlooked in the literature. From an educational perspective, underperformance is important not only because it signals students who may require additional support, but also because deficits in basic clinical and communication skills may have implications for the quality and safety of future patient care [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur specific research questions are (1) What is the graded association between voluntary practice frequency and OSCE station scores, after controlling for baseline academic ability and demographic factors? (2) Does practice correlate with a reduced probability of at-risk performance? (3) Does the correlation between practice and performance differ among various types of OSCE stations?\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting and Participants\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study was conducted at Hiroshima School of Medicine, Japan. All 126 fourth-year medical students from the 2022 academic year who participated in the OSCE in November 2022 were included. Data were complete for all students; no variables exhibited missing values. In the Japanese medical education system, the OSCE serves as a prerequisite for clinical clerkships. At this institution, the examination is conducted at the conclusion of the fourth year in a six-year curriculum. Ethical approval was granted by the Institutional Review Board (E2025-0274).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome: OSCE Performance\u003c/h3\u003e\n\u003cp\u003eThe OSCE comprised eight stations: medical interview (communication), head and neck examination, chest examination, vital signs assessment, abdominal examination, neurological examination, basic clinical procedures, and emergency care. Trained examiners scored each station using a 6-point global rating scale (1\u0026ndash;6). Despite this scale being technically ordinal, scores were regarded as approximately continuous for the primary analyses, aligning with common practice in medical education research [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The sensitivity analyses and limitations sections address the robustness of this assumption.\u003c/p\u003e\n\u003ch3\u003eExposure: Voluntary Practice\u003c/h3\u003e\n\u003cp\u003eThe skills laboratory was available for voluntary group practice sessions over an 11-day period (October 19\u0026ndash;November 1, 2022) preceding the OSCE (November 4\u0026ndash;5). Student attendance was documented daily through sign-in logs. The primary exposure variable was the total count of attended practice sessions (range: 0\u0026ndash;8). Secondary classifications comprised a binary variable (any practice vs. none) and a three-level categorical variable (0, 1, or \u0026ge;\u0026thinsp;2 sessions).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe following potential confounders were included: (a) CBT score, a nationally standardized computer-based knowledge examination administered before the OSCE, which provides a baseline academic ability measure (standardized as z-scores for analysis); (b) sex; (c) admission pathway, specifically regional quota (\u003cem\u003echiiki-waku\u003c/em\u003e, a preferential admission pathway for students committed to practicing in underserved areas) versus general admission; and (d) grade retention history (whether the student had repeated an academic year).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePrimary analysis.\u003c/b\u003e Given the clustered data structure (eight station scores nested within each student), we utilized generalized estimating equations (GEE) with a Gaussian family and an identity link function. An exchangeable working correlation structure was specified, indicating the assumption that the correlation between any pair of stations within a student remains relatively constant. The estimated working correlation was ρ\u0026thinsp;=\u0026thinsp;0.14. Cluster-robust (sandwich) standard errors were employed to ensure valid inference despite potential misspecification of the working correlation structure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The model incorporated station fixed effects (dummy variables for stations 2\u0026ndash;8, with the medical interview station as the reference), the practice count (continuous), and all covariates. This approach leverages all 1,008 observations (126 students \u0026times; 8 stations) while effectively addressing within-student correlation. To verify robustness, we conducted sensitivity analyses using independence and first-order autoregressive correlation structures (AR).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecondary analysis: at-risk performance.\u003c/b\u003e At-risk performance was defined as a station score of \u0026le;\u0026thinsp;3 on the 6-point global rating scale utilized in the Japanese national OSCE. This threshold aligns with a rating of \u0026ldquo;borderline\u0026rdquo; or lower on the scale defined by the Common Achievement Tests Organization (CATO), where 4 denotes \u0026ldquo;pass level,\u0026rdquo; 3 denotes \u0026ldquo;borderline,\u0026rdquo; 2 denotes \u0026ldquo;fail but improvable,\u0026rdquo; and 1 denotes \u0026ldquo;clear fail.\u0026rdquo; The probability of at-risk performance was modeled using a linear probability model (LPM) within the GEE framework, employing the same covariates and structure as in the primary analysis. This model\u0026rsquo;s regression coefficient directly estimates the risk difference (RD), which is defined as the change in the probability of at-risk performance per additional practice session, and is presented with 95% confidence intervals.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStation heterogeneity.\u003c/b\u003e To formally assess whether the practice\u0026ndash;performance relationship differed among stations, we fitted a GEE model with practice-by-station interaction terms and conducted a Wald test to evaluate the joint significance of the seven interaction parameters (stations 2\u0026ndash;8 vs. the reference station). Station-specific practice effects with 95% confidence intervals were obtained from this interaction model. Station-specific effect sizes (Cohen\u0026rsquo;s d) were derived from individual ordinary least squares (OLS) regression models that compared students who engaged in (any) practice versus those who did not.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSensitivity analyses.\u003c/b\u003e To assess robustness, we conducted two sensitivity analyses. First, inverse probability of treatment weighting (IPTW) was employed to estimate the causal effect of practice within a marginal structural model framework. Stabilized weights were obtained from propensity scores for binary treatment or the generalized propensity score for continuous treatment, utilizing the ratio of the marginal to the conditional density of practice count. The IPTW-weighted GEE model incorporated station fixed effects and the practice variable, excluding additional covariates, as confounding was mitigated through weighting. Covariate balance was assessed through standardized mean differences (SMD). Second, a three-level categorical exposure variable (0, 1, or \u0026ge;\u0026thinsp;2 sessions) was analyzed within the GEE framework, incorporating covariates and station fixed effects, with non-practitioners serving as the reference group. A Jonckheere\u0026ndash;Terpstra test was employed to assess the significance of a monotonic trend across groups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel diagnostics.\u003c/b\u003e Variance inflation factors (VIFs) were computed to assess multicollinearity (all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;1.3). Non-linearity was assessed by incorporating a quadratic term for the practice count (p\u0026thinsp;=\u0026thinsp;0.827). Analyses were conducted using Python 3.11 (scipy, numpy) and verified in R 4.3 (geepack). All statistical tests were bilateral, with a significance level of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e delineates participant characteristics categorized by practice group. Out of 126 students, 65 (51.6%) participated in at least one voluntary practice session, with a mean of 2.6 sessions among practitioners (SD\u0026thinsp;=\u0026thinsp;1.8). The practice frequency distribution was right-skewed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA): 61 students (48.4%) did not practice, 24 (19.0%) practiced once, 16 (12.7%) practiced twice, and 25 (19.8%) practiced three or more times. Most practitioners (72%) practiced only during the final week preceding the OSCE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\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\u003eParticipant Characteristics by Voluntary Practice Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Practice\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBT score, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507\u0026thinsp;\u0026plusmn;\u0026thinsp;104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e534\u0026thinsp;\u0026plusmn;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e479\u0026thinsp;\u0026plusmn;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSCE mean, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional quota, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade retention, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice sessions, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 sessions, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 sessions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3 sessions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. Data were complete for all 126 students (no missing values). CBT\u0026thinsp;=\u0026thinsp;Computer-Based Testing. Continuous variables: Mann-Whitney U test. Categorical variables: Fisher\u0026rsquo;s exact test. Effect size= Cohen\u0026rsquo;s d (continuous variables) or Cohen\u0026rsquo;s h (proportions).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) Histogram illustrating the number of practice sessions attended by each student. Approximately half (48.4%) of the students did not attend sessions. Among practitioners, the distribution was right-skewed (median\u0026thinsp;=\u0026thinsp;2, range 1\u0026ndash;8). (B) Pie chart showing practice timing: 37% of students practiced only during the late period (10/27\u0026ndash;11/1), while 13% practiced during both early and late periods (10/19\u0026ndash;11/1).\u003c/p\u003e \u003cp\u003eStudents who practiced had significantly higher CBT scores than non-practitioners (534\u0026thinsp;\u0026plusmn;\u0026thinsp;100 vs. 479\u0026thinsp;\u0026plusmn;\u0026thinsp;100, p\u0026thinsp;=\u0026thinsp;0.003, d\u0026thinsp;=\u0026thinsp;0.54). No significant differences were observed in sex distribution (p\u0026thinsp;=\u0026thinsp;0.850), admission pathway (p\u0026thinsp;=\u0026thinsp;0.363), or grade retention rates (p\u0026thinsp;=\u0026thinsp;1.000) among the groups. In a logistic regression predicting practice participation, CBT was the only significant predictor (OR\u0026thinsp;=\u0026thinsp;1.97 per SD, 95% CI: 1.28\u0026ndash;3.03, p\u0026thinsp;=\u0026thinsp;0.002; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e); practice rates increased across CBT tertiles (low: 38%, middle: 52%, high: 64%). Among practitioners, however, CBT exhibited no significant correlation with the number of sessions attended (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.160).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary Analysis: GEE\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the GEE results. In the fully adjusted model (Model 2), each additional practice session correlated with a 0.034-point increase in the OSCE station score (robust SE\u0026thinsp;=\u0026thinsp;0.016, 95% CI: 0.003\u0026ndash;0.065, p\u0026thinsp;=\u0026thinsp;0.032). A monotonic upward trend in mean scores among practice groups was noted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Spearman ρ=+0.194, p=.029). This correlation was independent of CBT score (β\u0026thinsp;=\u0026thinsp;0.129, p\u0026thinsp;=\u0026thinsp;0.007), sex (β\u0026thinsp;=\u0026thinsp;0.168, p\u0026thinsp;=\u0026thinsp;0.024), and grade retention (β=\u0026minus;0.330, p\u0026thinsp;=\u0026thinsp;0.034). The regional quota admission pathway exhibited no significant correlation with performance (β\u0026thinsp;=\u0026thinsp;0.059, p\u0026thinsp;=\u0026thinsp;0.475). The results were essentially identical under both independence and AR working correlation structures. The overall effect size comparing any practice to no practice was Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.36. To contextualize the magnitude, a student attending three sessions (approximately the median number among practitioners) is predicted to score 0.10 points higher per station than a non-practitioner, while a student attending five sessions is predicted to score 0.17 points higher, which is equivalent to approximately 0.4 SD of the observed score distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\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\u003eGEE Analysis: Association Between Practice Frequency and OSCE Station Score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.030*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional quota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade retention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.044*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBT (standardized)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.032*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. GEE with exchangeable working correlation (ρ\u0026thinsp;=\u0026thinsp;0.14) and cluster-robust standard errors. N\u0026thinsp;=\u0026thinsp;1,008 observations (126 students \u0026times; 8 stations). Station fixed effects included but not shown. Model 1: covariates only. Model 2: covariates\u0026thinsp;+\u0026thinsp;practice count. Results were essentially identical under independence and AR correlation structures. *p\u0026lt;.05, **p\u0026lt;.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) Boxplots illustrating mean OSCE scores categorized by practice group (0, 1, or 2\u0026thinsp;+\u0026thinsp;sessions), featuring individual data points (jittered) and group means (diamonds). (B) Scatter plot depicting individual OSCE mean scores against practice session count, featuring group means (diamonds) and OLS regression line (β\u0026thinsp;=\u0026thinsp;0.050, unadjusted).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSecondary Analysis: At-Risk Reduction\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents descriptive station-level at-risk rates in the practitioner and non-practitioner groups. Overall, practitioners showed a lower proportion of at-risk performances than non-practitioners (4.6% vs 9.4%). The magnitude of this difference varied across stations, indicating that the association between voluntary practice and reduced risk of underperformance was not uniform across the OSCE. Visual inspection suggested more pronounced differences in some performance-intensive stations, particularly clinical procedures and emergency. In the adjusted GEE linear probability model, each additional practice session was associated with a 0.7-percentage-point reduction in the probability of at-risk performance (95% CI: \u0026minus;1.3 to \u0026minus;\u0026thinsp;0.0, p = .044).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStation-Specific Effects and Heterogeneity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents station-specific outcomes derived from two complementary approaches. The GEE interaction model (practice \u0026times; station) produced station-specific practice effect estimates, whereas individual OLS models generated effect sizes (Cohen\u0026rsquo;s d) for each station (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe formal Wald test for the joint significance of the seven practice-by-station interaction terms yielded an non-significant result (χ\u003csup\u003e\u0026sup2;\u003c/sup\u003e(7)\u0026thinsp;=\u0026thinsp;7.77, p\u0026thinsp;=\u0026thinsp;0.353). The interaction model\u0026rsquo;s point estimates indicated the strongest association for basic clinical procedures (β\u0026thinsp;=\u0026thinsp;0.112, 95% CI: 0.029\u0026ndash;0.195), while the association for emergency care was more modest (β\u0026thinsp;=\u0026thinsp;0.052, 95% CI: \u0026minus;0.037\u0026ndash;0.140). Communication and physical examination stations exhibited smaller, non-significant associations.\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\u003eStation-Specific Practice Effects: GEE Interaction Model and Individual OLS Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGEE interaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical interview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.037, 0.085]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23 [\u0026minus;\u0026thinsp;0.12, 0.58]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead \u0026amp; neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.031, 0.075]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49 [0.14, 0.84]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.042, 0.060]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.20 [\u0026minus;\u0026thinsp;0.55, 0.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.044, 0.113]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 [\u0026minus;\u0026thinsp;0.19, 0.51]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.058, 0.039]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.12 [\u0026minus;\u0026thinsp;0.47, 0.23]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.025, 0.085]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24 [\u0026minus;\u0026thinsp;0.11, 0.59]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClin. procedures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.029, 0.195]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.037*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40 [0.04, 0.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.037, 0.140]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29 [\u0026minus;\u0026thinsp;0.06, 0.64]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. GEE interaction model: practice \u0026times; station interaction terms included; β represents the estimated practice effect for each station derived from the full interaction model. Wald test for heterogeneity across stations: χ\u003csup\u003e\u0026sup2;\u003c/sup\u003e(7)\u0026thinsp;=\u0026thinsp;7.77, p=.353. OLS: adjusted for sex, regional quota, grade retention, and CBT (z-score). d\u0026thinsp;=\u0026thinsp;Cohen\u0026rsquo;s d (any practice vs. none) with 95% CI. *OLS p\u0026lt;.05 (covariate-adjusted). Note that the 95% CI for d is based on the unadjusted comparison; Head \u0026amp; neck (d\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.49) was significant by unadjusted t-test (p=.007) but not after covariate adjustment (OLS p=.232).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMarker shapes indicate station type: square\u0026thinsp;=\u0026thinsp;communication, circles\u0026thinsp;=\u0026thinsp;physical examination, triangles\u0026thinsp;=\u0026thinsp;procedural, diamond\u0026thinsp;=\u0026thinsp;overall GEE-based estimate. The 95% CIs for d are based on the unadjusted group comparison; Head and neck (d\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.49) was significant in the unadjusted t-test (p = .007) but not after covariate adjustment (OLS p = .232).\u003c/p\u003e \u003cp\u003eWhen categorizing stations by type, procedural stations (clinical procedures, emergency care) exhibited the most significant practice effects (d\u0026thinsp;=\u0026thinsp;0.40 and 0.29, respectively), while physical examination stations demonstrated variable results (d ranging from \u0026minus;\u0026thinsp;0.20 to 0.49), and the communication station (medical interview) revealed a minor effect (d\u0026thinsp;=\u0026thinsp;0.23) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e \u003cp\u003eIPTW using the generalized propensity score for continuous treatment achieved excellent covariate balance, with all standardized mean differences below 0.03 (CBT: 0.545\u0026rarr;0.012; see Table S2). The IPTW-weighted estimate aligned with the primary analysis (β\u0026thinsp;=\u0026thinsp;0.040, SE\u0026thinsp;=\u0026thinsp;0.020, 95% CI: 0.000\u0026ndash;0.079, p\u0026thinsp;=\u0026thinsp;0.051). The IPTW-weighted estimate for binary treatment (any practice vs. none) was directionally consistent but non-significant (β\u0026thinsp;=\u0026thinsp;0.072, SE\u0026thinsp;=\u0026thinsp;0.080, p\u0026thinsp;=\u0026thinsp;0.371).\u003c/p\u003e \u003cp\u003eIn the three-level categorical analysis (0, 1, or \u0026ge;\u0026thinsp;2 sessions), the adjusted mean differences relative to non-practitioners were 0.066 (p\u0026thinsp;=\u0026thinsp;0.390) for one session and 0.099 (p\u0026thinsp;=\u0026thinsp;0.219) for two or more sessions. Despite individual group comparisons lacking significance, the Jonckheere\u0026ndash;Terpstra test validated a significant monotonic trend (z\u0026thinsp;=\u0026thinsp;2.06, p\u0026thinsp;=\u0026thinsp;0.039). Effect sizes increased with session count (d\u0026thinsp;=\u0026thinsp;0.26 for one session; d\u0026thinsp;=\u0026thinsp;0.39 for two or more sessions compared to non-practitioners).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Findings\u003c/h2\u003e \u003cp\u003eThis study demonstrates a positive graded association between voluntary skills lab practice and OSCE performance. Utilizing GEE to leverage the comprehensive data from eight OSCE stations, we determined that each additional practice session correlated with a 0.034-point increase in station score (p\u0026thinsp;=\u0026thinsp;0.032) and a 0.7 percentage-point decrease in the risk of at-risk performance (p\u0026thinsp;=\u0026thinsp;0.044), after controlling for baseline academic ability, sex, admission pathway, and grade retention history. The crude at-risk rate for non-practitioners (9.4%) was approximately twice that of practitioners (4.6%). Point estimates indicated the strongest correlation for procedural skills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Prior Literature\u003c/h2\u003e \u003cp\u003eThe correlation between skills lab training and clinical performance have been thoroughly examined in controlled contexts. Randomized trials have demonstrated that structured simulation-based training with deliberate practice enhances procedural skill execution [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, a meta-analysis by McGaghie et al. confirmed that simulation with deliberate practice yields superior outcomes than those of traditional clinical education [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, these studies focused on instructor-led, curriculum-embedded training rather than voluntary, self-directed practice; therefore, the findings may not be applicable to self-directed contexts.\u003c/p\u003e \u003cp\u003eThe evidence on voluntary practice and OSCE outcomes is remarkably sparse. Tsikas et al. employed propensity score matching at Hannover Medical School and found that voluntary practice correlated with increased overall OSCE performance, particularly benefiting procedural skills, while showing no significant disparity in communication stations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our findings generally align with this pattern, and the convergence across two distinct educational systems (Germany and Japan) bolsters confidence in its generalizability. Notably, the Japanese pre-clinical OSCE is a nationally standardized examination administered by CATO across all 82 medical schools, featuring uniform station designs, certified examiners, and a national passing standard [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], whereas most previous studies, including Tsikas et al., focused on institution-specific assessments. This distinction may enhance the internal validity of our findings, while also prompting inquiries regarding direct comparability with institution-designed OSCEs abroad.\u003c/p\u003e \u003cp\u003eOur study enhances the current evidence in two specific ways. First, although Tsikas et al. considered practice as a binary variable, our continuous measure reveals a graded association without any indication of a ceiling effect. This graded pattern was corroborated by a significant monotonic trend across the three practice groups (Jonckheere\u0026ndash;Terpstra p\u0026thinsp;=\u0026thinsp;0.039) and by the IPTW analysis, which yielded a nearly identical estimate (β\u0026thinsp;=\u0026thinsp;0.040) after attaining excellent covariate balance. In conjunction with Shafer et al.\u0026rsquo;s finding of a graded association for self-directed practice in central line insertion [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], this implies that graded associations may be a general feature of self-directed clinical practice. Second, the at-risk analysis introduces a dimension that, to the best of our knowledge, has not been examined in prior studies of voluntary practice: the crude at-risk rate among non-practitioners (9.4%) was approximately double that of practitioners (4.6%), indicating that practice may particularly assist students in avoiding the lower tail of performance.\u003c/p\u003e \u003cp\u003eThe formal heterogeneity test was non-significant (p\u0026thinsp;=\u0026thinsp;0.353), thus caution is advised regarding station-type differences. Communication skills may be less amenable to self-directed practice because they require interactive feedback, whereas procedural skills can be more effectively rehearsed with simulation equipment. This interpretation aligns with Tsikas et al.\u0026rsquo;s findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Selection Bias\u003c/h2\u003e \u003cp\u003eA significant constraint in all observational studies of voluntary practice is self-selection. In our analysis, CBT was the only significant predictor of practice participation (OR\u0026thinsp;=\u0026thinsp;1.97 per SD, p\u0026thinsp;=\u0026thinsp;0.002), with practice rates ranging from 38% in the lowest CBT tertile to 64% in the highest. Notably, among those practiced, CBT did not predict the number of sessions (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.160), indicating that baseline ability influences the choice to practice but not the intensity of engagement. One interpretation posits that CBT performance partially reflects self-regulated learning (SRL) capacity: a scoping review revealed that higher SRL levels in medical students correlated with both stronger academic achievement and improved clinical skills [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Students excelling in comprehensive knowledge examinations may also demonstrate greater proficiency in identifying gaps and pursuing practice opportunities. If so, the observed correlation between voluntary practice and OSCE performance may partially reflect a shared underlying trait of self-regulation rather than a purely causal effect of practice itself.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePractical Implications\u003c/h2\u003e \u003cp\u003eAlthough these considerations preclude causal claims, the consistency of the association across various analytical approaches (GEE, IPTW, categorical trend test) and its alignment with international evidence indicate that the findings possess practical significance. Medical schools should ensure accessibility to skills laboratories for voluntary practice before OSCE examinations. Students should be encouraged to participate in multiple sessions due to the graded pattern. Practice resources should be strategically allocated to procedural stations where the correlation seems most pronounced. Considering the self-selection pattern, targeted encouragement of underperforming students may aid in reducing performance disparities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite its contributions, this study has several limitations. First, this is an observational study, and unmeasured confounders (such as motivation and study habits) may bias the results despite covariate adjustment and IPTW. Second, our exposure metric captured only attendance at the skills laboratory. Practices conducted elsewhere, such as self-study using textbooks or videos at home, were not recorded. This exposure misclassification may attenuate the estimated association. Third, the use of a single-institution sample (N\u0026thinsp;=\u0026thinsp;126) limits the generalizability of the findings and reduces the statistical power to detect interaction effects.\u003c/p\u003e \u003cp\u003eA major limitation of this study, as well as the broader literature, is that practice was measured solely by session counts, with no information on content, duration, or pedagogical structure. Our effect estimates probably reflect an average across heterogeneous practice experiences, and the actual advantage of high-quality practice may be greater. Future research should progress beyond frequency counts to encompass richer characterizations of practice behavior, incorporating structured logs, direct observation, and comparison of various practice formats. Finally, the limited 11-day practice period prevented the analysis of longer-term patterns.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eVoluntary skills lab practice correlates positively with OSCE performance in a graded fashion among Japanese medical students. The association correlates with a reduced risk of underperformance and is most pronounced for procedural skills, aligning with international evidence. Although the observational design precludes causal inferences, these findings endorse institutional initiatives to facilitate and promote voluntary practice opportunities, emphasizing equitable access for the entire student population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFirst-order autoregressive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputer-Based Testing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCATO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommon Achievement Tests Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized estimating equations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIPTW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse probability of treatment weighting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear probability model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrdinary least squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOSCE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eObjective Structured Clinical Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRisk difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized mean difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-regulated learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance inflation factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003cbr\u003e\u003c/strong\u003eWe gratefully acknowledge Yukari Shinjo for her assistance in compiling the data.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eThe authors used ChatGPT (OpenAI, GPT-5.2 Thinking) during manuscript preparation to assist with language editing, wording refinement, and organization of draft text. The tool was used to improve clarity and readability. All generated output was carefully reviewed, verified, and revised by the authors, who are fully responsible for the content of the manuscript.\u003c/p\u003e\n\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Hiroshima University (Approval No. E2025-0274). The study was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. The requirement for informed consent was waived by the Institutional Review Board because this retrospective study used existing educational records and involved minimal risk to participants.\u003cbr\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are not publicly available because they contain potentially identifiable educational information and are subject to institutional and ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest associated with this manuscript\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by JSPS KAKENHI Grant Number 23K09550.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMH conceived the study, analyzed the data, and drafted the manuscript. NH contributed to the study design, interpretation of the data, and critical revision of the manuscript. YN contributed to data interpretation and critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHarden RM, Gleeson FA. Assessment of clinical competence using an objective structured clinical examination (OSCE). Med Educ. 1979;13(1):41\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan KZ, Ramachandran S, Gaunt K, Pushkar P. The Objective Structured Clinical Examination (OSCE): AMEE Guide 81. Part I: an historical and theoretical perspective. Med Teach. 2013;35(9):e1437\u0026ndash;1446.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynagh M, Burton R, Sanson-Fisher R. A systematic review of medical skills laboratory training: where to from here? Med Educ. 2007;41(9):879\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrydges R, Nair P, Ma I, Shanks D, Hatala R. Directed self-regulated learning versus instructor-regulated learning in simulation training. Med Educ. 2012;46(7):648\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosse HM, Mohr J, Buss B, Krautter M, Weyrich P, Herzog W, Junger J, Nikendei C. The benefit of repetitive skills training and frequency of expert feedback in the early acquisition of procedural skills. BMC Med Educ. 2015;15:22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel R, Tarrant C, Bonas S, Yates J, Sandars J. The struggling student: a thematic analysis from the self-regulated learning perspective. Med Educ. 2015;49(4):417\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiederich E, Lineberry M, Schott V, Broski J, Alsayer A, Eckels KA, Murray MJ, Huynh W, Thomas LA. Putting the learning in pre-learning: effects of a self-directed study hall on skill acquisition in a simulation-based central line insertion course. Adv Simul (Lond). 2023;8(1):21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsikas SA, Afshar K, Fischer V. Does voluntary practice improve the outcome of an OSCE in undergraduate medical studies? A Propensity Score Matching approach. PLoS ONE. 2024;19(10):e0312387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOchsmann EB, Zier U, Drexler H, Schmid K. Well prepared for work? Junior doctors' self-assessment after medical education. BMC Med Educ. 2011;11:99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42(1):121\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang KY, Zeger SL. Longitudinal Data-Analysis Using Generalized Linear-Models. Biometrika. 1986;73(1):13\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLund F, Schultz JH, Maatouk I, Krautter M, Moltner A, Werner A, Weyrich P, Junger J, Nikendei C. Effectiveness of IV cannulation skills laboratory training and its transfer into clinical practice: a randomized, controlled trial. PLoS ONE. 2012;7(3):e32831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Acad Med. 2011;86(6):706\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishigori H. Medical education in Japan. Med Teach. 2024;46(sup1):S4\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho KK, Marjadi B, Langendyk V, Hu W. The self-regulated learning of medical students in the clinical environment - a scoping review. BMC Med Educ. 2017;17(1):112.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":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":"OSCE, skills laboratory, voluntary practice, graded association, GEE, medical education, clinical skills","lastPublishedDoi":"10.21203/rs.3.rs-9107817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9107817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSkills laboratories offer students the opportunity to practice clinical skills before high-stakes examinations. However, evidence regarding the correlation between voluntary (self-directed) practice and enhanced Objective Structured Clinical Examination (OSCE) performance remains limited, and few studies have examined graded associations. This study aims to investigate the graded association between voluntary skills lab practice frequency and OSCE performance, including the potential association between practice and a diminished risk of underperformance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from 126 Japanese medical school fourth-year students (2022 cohort). We recorded practice frequency (0\u0026ndash;8 sessions over a two-week time-frame), Computer-Based Testing (CBT) scores, sex, admission pathway, and grade retention history; no data were missing. The OSCE scores from eight stations were analyzed using generalized estimating equations (GEE) with an exchangeable working correlation structure and cluster-robust standard errors. Secondary analyses examined at-risk performance (score\u0026thinsp;\u0026le;\u0026thinsp;3, corresponding to \u0026ldquo;borderline\u0026rdquo; or below on the CATO global rating scale) using a linear probability model within the GEE framework. Station-specific effects were assessed through a practice-by-station interaction model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifty-two percent of students (n\u0026thinsp;=\u0026thinsp;65) participated in voluntary practice, with a mean of 2.6 sessions among practitioners. In the GEE analysis, adjusting for CBT, sex, admission pathway, and grade retention, each additional practice session correlated with a 0.034-point increase in OSCE station scores (95% CI: 0.003\u0026ndash;0.066, p\u0026thinsp;=\u0026thinsp;0.032). Practice was also correlated with a reduced probability of at-risk performance (\u0026le;\u0026thinsp;3 on the 6-point scale), with each session linked to a risk difference of \u0026minus;\u0026thinsp;0.7 percentage points (95% CI: \u0026minus;1.3 to \u0026minus;\u0026thinsp;0.0, p\u0026thinsp;=\u0026thinsp;0.044). Station-specific analyses revealed the most significant correlation for clinical procedures (β\u0026thinsp;=\u0026thinsp;0.112, 95% CI: 0.029\u0026ndash;0.195, p\u0026thinsp;=\u0026thinsp;0.037). The overall effect size was Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.36.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eVoluntary skills lab practice correlates with higher OSCE performance in a graded fashion. The correlation is particularly evident in procedural skills and is associated with a reduced risk of underperformance. Although self-selection bias cannot be entirely eliminated, these findings support institutional policies that enhance access to voluntary practice opportunities.\u003c/p\u003e","manuscriptTitle":"Voluntary Skills Lab Practice and OSCE Performance: Graded Association, Station Heterogeneity, and Reduced Risk of Underperformance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 11:16:53","doi":"10.21203/rs.3.rs-9107817/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-16T09:07:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316923285151113243274179285503371959307","date":"2026-04-08T15:30:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T08:14:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T04:55:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T02:39:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T02:39:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-03-12T18:57:45+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":"b149d673-48c1-40fc-9743-3b19e63325aa","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T11:16:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 11:16:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9107817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9107817","identity":"rs-9107817","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}