Effectiveness of simulation-based learning in undergraduate Pharmacology: a quasi-experimental cohort study

Effectiveness of simulation-based learning in undergraduate Pharmacology: a quasi-experimental cohort study | 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 Effectiveness of simulation-based learning in undergraduate Pharmacology: a quasi-experimental cohort study Guillermo Insfran, Angilberto Paredes, Sara Ramirez, Rocio Bogado, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9357747/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Pharmacology is one of the most demanding basic science courses in health professions education, with high rates of poor performance and cognitive overload. Simulation-based learning and formative retrieval practice have each been shown to improve knowledge retention independently, but few studies have combined both strategies within a single semester-long intervention and compared outcomes against a historical control cohort. Methods A quasi-experimental study compared a prospective intervention cohort (August–December 2025; n = 139) with a retrospective historical control cohort (March–July 2025; n = 152) in the Pharmacology course at Universidad del Pacífico, Paraguay. The intervention consisted of weekly 4-hour sessions combining clinical simulation via role-playing and a 15-item formative test. Both cohorts sat the same three partial examinations (P1, P2, P3) and a final examination (FE). Bivariate comparisons used the Mann–Whitney U test and chi-square; within-cohort trajectories were assessed using the Friedman test with Wilcoxon post-hoc comparisons. Multivariate analyses included multiple linear regression and logistic regression adjusted for academic group and prior performance. Results Bivariate comparisons showed no statistically significant differences between cohorts in any examination score or final pass rate (FE pass rate: Control 50.0% vs. Intervention 57.6%; χ² = 1.376, p = 0.241). However, within-cohort trajectory analysis revealed divergent patterns: the control cohort showed a progressive and sustained decline throughout the semester, whereas the intervention cohort stabilised in the second half (P2→FE and P3→FE comparisons non-significant). After adjusting for academic group and prior performance, the intervention cohort scored 3.12 points higher on the FE (β = 3.12; 95% CI: 0.90–5.34; p = 0.006) and had 2.29-fold greater odds of passing (OR = 2.29; 95% CI: 1.21–4.47; p = 0.013). Conclusions Weekly clinical simulation combined with formative testing was associated with a protective effect against progressive performance decline and with significant improvements in final examination outcomes after adjustment for baseline differences. These findings support the feasibility and educational value of integrating simulation and retrieval practice within standard pharmacology curricula. Multicentre studies with factorial designs are needed to disentangle the contributions of each component. clinical simulation formative assessment pharmacology education quasi-experimental retrieval practice medical education Background Pharmacology is widely recognised as one of the most challenging basic science disciplines for health professions students. Learners frequently report cognitive overload, high anxiety related to the volume and clinical relevance of content, and difficulties integrating pharmacokinetics and pharmacodynamics with clinical decision-making, factors that may contribute to poor academic performance and subsequent prescribing risks [ 1 ]. Educators have therefore explored active, contextualised approaches to bridge conceptual knowledge and clinical application; simulation-based pedagogies — encompassing virtual patients, computer simulations, high-fidelity manikin scenarios and role-playing — are increasingly recommended as tools that create authentic contexts, enable deliberate practice, and promote the application of pharmacological principles to patient care [ 2 ]. Cognitive science provides a robust rationale for incorporating frequent, low-stakes testing into instruction. The testing effect — the finding that retrieval practice improves long-term retention more than additional study — is well established in both laboratory and classroom settings [ 3 , 4 ]. Classroom-based research has demonstrated that retrieval practice following lectures improves delayed retention and knowledge transfer [ 5 , 6 ]. Major synthesis papers and reviews of learning techniques conclude that retrieval practice and spacing are among the most evidence-based strategies educators can implement to promote durable learning [ 7 ]. Translational work in health professions training confirms that frequent, low-stakes formative quizzes and spaced practice produce measurable improvements in subsequent summative performance and engagement. Interventions incorporating frequent low-stakes quizzes — daily, weekly or spaced digital modules — correlate with better summative performance and greater study diligence; some studies show little difference between daily and weekly cadences, but a consistent benefit from engagement and spacing [ 8 – 10 ]. Laboratory and applied studies have also documented the forward testing effect, whereby interpolated retrieval practice improves the learning of subsequently presented new material, suggesting that routine formative testing may consolidate prior material and boost acquisition of future topics [ 11 ]. Simulation-based education has a broad and growing evidence base in health professions training. Systematic reviews and meta-analyses show that technology-enhanced simulation improves knowledge, skills and behaviours compared with no intervention, and can produce moderate translational effects on clinical outcomes when designed with deliberate practice, feedback and mastery-oriented goals [ 12 – 15 ]. Best-practice features — explicit objectives, repetitive practice, individualised feedback, increasing difficulty and validated assessment — amplify the educational yield of simulation and make it a plausible vehicle for integrating conceptual pharmacology with realistic prescribing or counselling tasks [ 13 , 14 ]. In pharmacology and pharmacy education, a growing number of studies and systematic reviews indicate that computer simulations, virtual patients and mixed-mode simulation improve student knowledge, clinical reasoning and confidence in medication-management tasks. A randomised comparative study with undergraduate students showed improved learning and retention with simulation approaches compared with traditional pharmacology teaching [ 16 – 18 ]. However, gaps remain: few studies have combined weekly simulation sessions with regular low-stakes formative retrieval practice and then compared outcomes against historical controls who received only conventional summative assessments. Although evidence separately supports the efficacy of simulation and frequent retrieval practice for improving learning and retention, few semester-long evaluations have integrated weekly simulation sessions with regular formative testing and compared outcomes with historically trained cohorts. The quasi-experimental design adopted here allows assessment of the realistic, feasible effect of an instructional package — clinically oriented simulation plus weekly 15-item formative tests — on relevant educational outcomes (partial and final examination scores, performance trajectory and pass rate), while maintaining the usual evaluative structure of the course. Methodologically, this strategy combines principles validated by cognitive psychology (retrieval practice and spacing) with best practices of simulation education (deliberate practice, structured feedback and debriefing), increasing the plausibility of gains in both immediate learning and medium-term retention. Comparison with a retrospective cohort avoids disruption of the course, is ethically appropriate and generates evidence directly applicable to curricular implementation if meaningful benefits are demonstrated. Methods Study design and setting A quasi-experimental study was conducted with a prospective intervention group and a retrospective historical control group, aimed at evaluating the effectiveness of clinical simulation-based learning applied to the Pharmacology course for medical students. The study was conducted in Paraguay, within the Pharmacology Department of Universidad del Pacífico. Population and study cohorts The study population comprised students enrolled in the Pharmacology course, distributed across two consecutive academic cohorts. The intervention cohort included students who attended the course during the August–December 2025 semester and participated in a structured clinical simulation educational strategy. The control cohort comprised students who attended the same course during the March–July 2025 semester under the traditional modality without clinical simulation, whose academic records were analysed retrospectively and de-identified. Eligibility criteria Students were included if they were officially enrolled in the course during the corresponding semester, had a recorded attendance rate of ≥ 75%, were aged ≥ 18 years, and had granted informed consent or accepted the use of de-identified academic data for research purposes. Students were excluded if they withdrew from the course before completing half the semester, failed for administrative reasons, were repeating the course, or whose final ordinary examination data could not be retrieved. Sampling and recruitment A non-probabilistic convenience sampling approach was used with pre-formed cohorts. Each cohort comprised approximately 45 students per section, assigned to sections randomly by the Academic Registrar at enrolment, without instructor involvement in the assignment. Recruitment of the intervention cohort was census-based, including all eligible students who consented to participate. Students were informed at the start of the course about the study objectives, the nature of the educational intervention and data confidentiality. No academic or financial incentives were offered, and it was made clear that participation would not affect academic performance or the student–institution relationship. Variables and data sources Variables analysed included age, academic section, semester, scores on three partial examinations (P1, P2, P3), score on the final ordinary examination (FE), and final course pass/fail status. Additionally, in the intervention cohort, clinical performance was assessed using an OSCE station for medical prescription and patient counselling. Data were obtained from official academic records of the department. The variable sex was excluded from the analysis due to unavailability of data. Assessment instruments Clinical performance in the intervention cohort was assessed using a structured 14-item OSCE rubric (maximum score 28 points; passing ≥ 60%), adapted from two previously validated instruments: the medical prescription rubric for medical students developed by Kantiwong & Lertsakulbunlue (2024) [ 19 ] and the patient counselling rubric based on Indian Health Service guidelines developed by Garling & Wong (2023) [ 20 ]. Items were reviewed by local pharmacology experts to ensure content validity and piloted with third-year students. Since the control cohort did not undergo an OSCE assessment, this instrument was used exclusively as a descriptive measure of clinical performance within the intervention cohort and not as a comparative variable between cohorts. Partial examinations and the final examination consisted of written multiple-choice tests, developed and validated by the teaching team, aligned with course learning objectives and the current curriculum. A minimum passing score of 60% was established for both partial examinations and the final ordinary examination, per institutional academic policy. Final course pass was operationally defined as achieving a score of ≥ 60% on the final ordinary examination. Validity and reliability Content validity of the instruments was established through expert review by pharmacology specialists. Reliability was evaluated through a pilot test, with Cronbach's alpha coefficients above 0.70, considered acceptable for educational studies. For the OSCE assessment, standardised rubrics were used and inter-rater reliability was estimated using the intraclass correlation coefficient (ICC). Educational procedure Both cohorts received a total of six weekly contact hours distributed as follows. Theory sessions consisted of one two-hour weekly lecture (Section A on Mondays, Section B on Tuesdays) covering course conceptual content. At the end of each theory session, four clinical cases related to the covered content were distributed to students for preparation as a pre-practical activity. Weekly clinical simulation sessions of four total hours were exclusive to the intervention cohort. During these sessions, students were allocated to small groups of approximately ten students, assigned randomly according to attendance. Each group had 90 minutes to discuss and agree on the resolution of the assigned case, including selection of the therapeutic regimen, pharmacological indications and general disease management considerations. This was followed by a clinical simulation via role-playing, in which an instructor assumed the role of patient and three group members acted as treating physicians, with emphasis on clinical reasoning, rational prescribing and patient communication. The final hour of each session was devoted to a 15-item multiple-choice formative test, corrected and debriefed during the same session, with a passing criterion of ≥ 60% (≥ 9/15). Weekly test scores were not included in the between-cohort comparative analysis, as they constitute a variable exclusive to the intervention. The control cohort received the six weekly hours under the traditional modality, without simulation sessions or weekly formative assessment, in accordance with the teaching model in effect during the March–July 2025 semester. Statistical analysis Data were recorded in an anonymised digital database. Descriptive and comparative statistical analyses were performed. Quantitative variables were expressed as mean and standard deviation or median and interquartile range according to their distribution, assessed using the Shapiro–Wilk test. Categorical variables were expressed as absolute frequencies and percentages. Comparison of partial and final examination scores between cohorts used the independent-samples Student's t-test or the Mann–Whitney U test as appropriate for normality, supplemented with Cohen's d effect sizes. The final pass rate was compared between cohorts using the chi-square test, reporting odds ratio (OR) and relative risk (RR) with 95% confidence intervals. Within-cohort performance trajectories across the semester (P1, P2, P3, FE) were analysed using the Friedman test with Wilcoxon signed-rank post-hoc comparisons and Bonferroni correction. A p-value < 0.05 was considered statistically significant. Analyses were performed using R and Microsoft Excel. Given the imbalanced distribution of students across the four academic groups (Semester × Section) and their differential allocation between cohorts, a combined academic group variable was constructed and included as a covariate in all multivariate models. The primary multivariate analysis was a multiple linear regression model (FE ~ Cohort + Group + P1 + P2 + P3). A logistic regression model (Pass/Fail ~ Cohort + Group + P1 + P2 + P3) was also fitted to estimate the adjusted odds of passing. An ANCOVA adjusted for academic group was additionally conducted. Bias control To reduce biases inherent to quasi-experimental designs, the following measures were adopted. Student assignment to sections was performed randomly by the Academic Registrar at enrolment, without instructor involvement, reducing selection bias and favouring baseline comparability between groups. No changes were introduced in the curriculum, learning objectives or course content between the compared semesters, ensuring curricular equivalence. Summative assessment instruments (partial and final examinations) were identical across cohorts, eliminating instrument-related difficulty differences. Standardised rubrics were used for the OSCE assessment and inter-rater reliability was estimated using ICC. Students with missing final examination data were excluded from the analysis. Ethical considerations The study was conducted in accordance with the principles of the Declaration of Helsinki and local regulations on human subject’s research. The protocol was reviewed and approved by the Comité de Ética Institucional de la Universidad del Pacífico (Asunción, Paraguay). For the prospective intervention cohort, participation was voluntary and written informed consent was obtained from all participants prior to enrollment, with the right to withdraw without academic consequences guaranteed. For the retrospective control cohort, the Comité de Ética Institucional granted a waiver of individual informed consent, as data were fully de-identified prior to analysis and the study was classified as minimal risk involving only anonymized academic records used exclusively for research purposes. All data were handled confidentially and used solely for academic and research purposes. Results Sample characteristics Of 366 students enrolled in the Pharmacology course across the two evaluated semesters, 291 met the inclusion criteria and had complete data for all four summative assessments (P1, P2, P3 and final ordinary examination). Fifty-six students were excluded due to absence of a final examination record, and an additional 19 were excluded due to missing data in at least one partial examination. The analytic sample comprised 152 students in the control cohort (March–July 2025) and 139 in the intervention cohort (August–December 2025). Distribution by academic group showed some imbalance between cohorts: Semester 1–Section A (Control = 43, Intervention = 32), Semester 1–Section B (Control = 18, Intervention = 36), Semester 2–Section A (Control = 36, Intervention = 30) and Semester 2–Section B (Control = 55, Intervention = 41). This imbalance was accounted for in multivariate analyses through the inclusion of a combined academic group variable as a covariate. Descriptive statistics and bivariate comparisons Table 1 summarises examination scores and final ordinary examination pass rate by cohort. Both groups showed similar baseline scores on the first partial examination (P1: Control = 66.1 ± 12.0 vs. Intervention = 65.3 ± 16.6), suggesting equivalence in prior knowledge at the start of the semester. Subsequent examinations showed a downward trend in both cohorts, with slightly higher values in the control cohort for P2 and P3, and slightly higher values in the intervention cohort for the final examination. The Shapiro–Wilk test indicated departure from normality in at least one cohort for P1 (intervention: W = 0.974; p = 0.009), P2 (control: W = 0.978; p = 0.016), P3 (intervention: W = 0.979; p = 0.031) and FE (control: W = 0.982; p = 0.043), justifying the use of non-parametric tests for bivariate comparisons. The Mann–Whitney U test revealed no statistically significant differences between cohorts in any examination (Table 1 ). Cohen's d effect sizes were negligible for P1 (d = 0.05), P2 (d = 0.13) and FE (d = 0.10), and small for P3 (d = 0.21). The final examination pass rate was 50.0% in the control cohort and 57.6% in the intervention cohort; this difference did not reach statistical significance (χ² = 1.376; df = 1; p = 0.241), with OR = 0.74 (95% CI: 0.46–1.17) and RR = 0.85 (95% CI: 0.66–1.09). Table 1 Descriptive statistics and bivariate comparisons between cohorts. Variable Control (n = 152) Intervention (n = 139) Statistic p P1 (mean ± SD) 66.1 ± 12.0 65.3 ± 16.6 W = 10,847 0.691 P2 (mean ± SD) 62.7 ± 11.2 61.0 ± 14.7 W = 11,119 0.372 P3 (mean ± SD) 62.9 ± 12.2 60.1 ± 13.9 W = 11,524 0.087 FE (mean ± SD) 59.4 ± 15.0 60.8 ± 13.0 W = 10,008 0.437 Cohen's d (P1/P2/P3/FE) — — 0.05 / 0.13 / 0.21 / 0.10 — FE pass rate, n (%) 76 (50.0%) 80 (57.6%) χ² = 1.376 0.241 OR (95% CI) 0.74 (0.46–1.17) — — — RR (95% CI) 0.85 (0.66–1.09) — — — p-values correspond to the Mann–Whitney U test for continuous variables and Pearson chi-square for proportions. SD = standard deviation. FE = final ordinary examination. OR = odds ratio. RR = relative risk. CI = confidence interval. Within-cohort performance trajectory The Friedman test revealed significant differences in the performance trajectory across the semester in both cohorts (Control: χ² = 43.1; df = 3; p < 0.0001; Intervention: χ² = 17.9; df = 3; p = 0.0005). However, the qualitative pattern differed substantially between groups. In the control cohort, a progressive and sustained decline in scores was observed from the first partial examination to the final ordinary examination, with significant differences in most pairwise comparisons after Bonferroni correction (Table 2 ). In particular, comparisons P2→FE (p < 0.0001) and P3→FE (p = 0.016) were significant, indicating continuous deterioration towards the end of the semester. In contrast, the intervention cohort exhibited a different pattern: although a significant decline was observed from P1 to subsequent assessments (P1→P2: p = 0.004; P1→P3: p < 0.001; P1→FE: p < 0.0001), comparisons P2→FE and P3→FE did not reach statistical significance. This finding suggests that the simulation-based intervention with weekly formative assessment contributed to stabilising academic performance in the second half of the semester, attenuating the progressive decline observed in the control cohort. Table 2 Wilcoxon signed-rank post-hoc comparisons with Bonferroni correction — within-cohort performance trajectory. Comparison Control (p-adj) Sig. Intervention (p-adj) Sig. P1 – P2 < 0.001 *** 0.004 ** P1 – P3 < 0.0001 **** < 0.001 *** P1 – FE < 0.0001 **** < 0.0001 **** P2 – P3 ns ns 0.041 * P2 – FE < 0.0001 **** ns ns P3 – FE 0.016 * ns ns p-adj = Bonferroni-adjusted p-value. Significance levels: *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns = not significant. Friedman global test: Control χ² = 43.1 (p < 0.0001); Intervention χ² = 17.9 (p = 0.0005). Multivariate analysis Given the observed imbalance in the distribution of students across the four academic groups and their differential allocation between cohorts, a combined academic group variable (Semester × Section) was constructed and included as a covariate in all multivariate models. The ANCOVA adjusted for academic group did not show a significant effect of cohort on final examination score (F = 0.836; p = 0.361), although academic group was a significant confounder (F = 9.397; p < 0.001). Estimated marginal means were 60.6 points (95% CI: 58.3–62.8) for the control cohort and 61.1 points (95% CI: 58.9–63.4) for the intervention cohort, with an adjusted difference of 0.57 points (p = 0.725). Multiple linear regression The multiple linear regression model (FE ~ Cohort + Group + P1 + P2 + P3) showed that, after controlling for academic group and partial examination performance, belonging to the intervention cohort was associated with a 3.12-point increase in the final ordinary examination score (β = 3.12; SE = 1.13; p = 0.006). The model explained 55.6% of the variance in the final score (adjusted R² = 0.556; F = 52.82; p < 0.001). All three partial examinations were significant independent predictors: P1 (β = 0.37; p < 0.001), P2 (β = 0.31; p < 0.001) and P3 (β = 0.22; p < 0.001). Full coefficients are presented in Table 3 . Table 3 Multiple linear regression — dependent variable: final ordinary examination score (FE). Predictor β SE t p 95% CI lower 95% CI upper Intercept 6.69 3.57 1.87 0.062 −0.34 13.72 Cohort (Intervention) 3.12 1.13 2.76 0.006** 0.90 5.34 Group S1-B — — — ns — — Group S2-A — — — ns — — Group S2-B — — — ns — — P1 0.37 0.05 7.18 < 0.001*** 0.27 0.47 P2 0.31 0.06 5.43 < 0.001*** 0.20 0.43 P3 0.22 0.06 3.78 < 0.001*** 0.11 0.34 β = unstandardised coefficient. SE = standard error. CI = confidence interval. Reference category for Cohort: Control; for Group: Semester 1–Section A. Adjusted R² = 0.556; F(7, 283) = 52.82; p < 0.001. *p < 0.05; **p < 0.01; ***p < 0.001; ns = not significant. Logistic regression The logistic regression model (Pass/Fail ~ Cohort + Group + P1 + P2 + P3) showed that, after controlling for academic group and partial examination performance, belonging to the intervention cohort was associated with 2.29-fold greater odds of passing the final ordinary examination (OR = 2.29; 95% CI: 1.21–4.47; p = 0.013). P1 (OR = 1.09; 95% CI: 1.05–1.12; p < 0.001) and P2 (OR = 1.10; 95% CI: 1.06–1.14; p < 0.001) were also significant independent predictors of passing, whereas P3 did not reach significance in this model (OR = 1.01; 95% CI: 0.98–1.04; p = 0.704). Detailed results are presented in Table 4 . Table 4 Logistic regression — dependent variable: pass/fail on final ordinary examination. Predictor OR 95% CI z p Cohort (Intervention) 2.29 1.21–4.47 2.49 0.013* Group S1-B — — — ns Group S2-A — — — ns Group S2-B — — — ns P1 1.09 1.05–1.12 — < 0.001*** P2 1.10 1.06–1.14 — < 0.001*** P3 1.01 0.98–1.04 — 0.704 ns OR = odds ratio. CI = confidence interval. Reference category for Cohort: Control; for Group: Semester 1–Section A. *p < 0.05; **p < 0.01; ***p < 0.001; ns = not significant. Summary of findings In summary, bivariate analyses did not detect statistically significant differences between cohorts in summative examination scores or pass rate. However, within-cohort trajectory analysis revealed that the intervention cohort showed a stabilisation of performance in the second half of the semester, in contrast to the progressive decline observed in the control group. More relevantly, multivariate models — adjusted for academic group and prior performance — demonstrated that belonging to the intervention cohort was independently associated with a significant 3.12-point increase in the final examination (p = 0.006) and with 2.29-fold greater odds of passing (OR = 2.29; p = 0.013). These findings suggest that the effect of the intervention becomes clearer when baseline and structural differences between groups are controlled for. Discussion This study evaluated the effectiveness of a clinical simulation-based intervention combined with weekly formative assessment in Pharmacology, using a quasi-experimental design with a historical control cohort. Findings are organised around three axes: the absence of significant differences in bivariate analyses, the emergence of a significant effect in adjusted multivariate models, and a differential pattern in the performance trajectory across the semester. Bivariate comparisons (Mann–Whitney U, chi-square) did not detect statistically significant differences between cohorts in any partial examination, the final examination or the pass rate, with negligible effect sizes. This result is partially consistent with the literature: although Arcoraci et al. [ 16 ] reported significant improvements with simulation in a randomised trial, systematic reviews in pharmacological and pharmacy education [ 17 , 18 ] have noted that the impact of simulation on conventional summative assessments is heterogeneous and depends on the alignment between instructional modality and assessment type. However, when controlling for academic group and prior performance through multivariate models, the intervention effect became significant. Multiple linear regression showed a 3.12-point increase in the final examination for the intervention cohort (β = 3.12; p = 0.006; adjusted R² = 0.556), and logistic regression revealed 2.29-fold greater odds of passing (OR = 2.29; 95% CI: 1.21–4.47; p = 0.013). This discrepancy between crude and adjusted analyses suggests that variability across academic groups and the weight of accumulated performance mask the independent contribution of the intervention, a phenomenon previously described in the medical education literature using quasi-experimental designs [ 12 , 13 ]. The within-cohort trajectory analysis using the Friedman test constitutes perhaps the most relevant finding. Both cohorts showed a significant decline in performance across the semester, but with qualitatively distinct patterns: in the control cohort, the decrease was progressive and sustained from P1 to the final examination, whereas in the intervention cohort scores stabilised in the second half of the semester (P2→FE and P3→FE comparisons non-significant). This pattern is consistent with mechanisms proposed by the testing effect and spaced practice theory [ 3 , 4 , 7 ]: weekly formative assessment, by promoting active retrieval, would have attenuated the cumulative decline. Furthermore, Pastötter and Bäuml [ 11 ] demonstrated that retrieval practice improves the acquisition of subsequently presented new content, suggesting that weekly tests may have facilitated more efficient learning of later topics. The role-playing simulation sessions, by requiring the integrated application of pharmacological knowledge in realistic clinical contexts, promote deeper and more transferable encoding, potentiating the effect of retrieval practice through structured feedback and deliberate practice [ 13 , 14 ]. This study has limitations inherent to quasi-experimental designs with a historical control cohort, which cannot entirely exclude the influence of unmeasured between-cohort factors. The single-centre design limits generalisability. The intervention combines simulation and formative assessment, preventing isolation of each component's contribution; future factorial designs could decompose these contributions. Outcomes beyond the final examination were not assessed, and the absence of a formal a priori power calculation is a methodological limitation, although the sample size (n = 291) exceeds that of most published studies in this field. Despite these limitations, results suggest that combining weekly clinical simulation with formative assessment is a feasible intervention within the existing curricular structure, whose primary mechanism of action may not be elevating absolute scores but rather protecting against progressive performance deterioration across a demanding semester. Future multicentre studies with longitudinal follow-up and assessment of clinical competencies in real scenarios would allow determination of whether these benefits translate into improvements in prescribing quality and patient safety. Conclusions The incorporation of weekly clinical simulation sessions combined with formative testing in the Pharmacology course did not produce statistically significant differences in partial examination scores or pass rate in bivariate analyses. However, multivariate models adjusted for academic group and prior performance revealed that the intervention was associated with a significant increase in the final examination score (β = 3.12; p = 0.006) and with 2.29-fold greater odds of passing (OR = 2.29; 95% CI: 1.21–4.47; p = 0.013). Additionally, the intervention cohort showed a stabilisation of performance in the second half of the semester, in contrast to the progressive and sustained decline observed in the control cohort, suggesting a protective effect of the intervention against cumulative performance deterioration. These findings support the feasibility and potential benefit of integrating clinical simulation and formative retrieval practice as a complementary pedagogical strategy in pharmacology education, without requiring substantial modifications to the curricular structure. Multicentre studies with factorial designs and longitudinal follow-up are recommended to confirm these results, disentangle the individual contributions of each component of the intervention, and assess its impact on clinical competencies and patient safety. Abbreviations ANCOVA: Analysis of covariance | CI: Confidence interval | FE: Final ordinary examination | ICC: Intraclass correlation coefficient | OR: Odds ratio | OSCE: Objective Structured Clinical Examination | P1/P2/P3: First, second and third partial examinations | RR: Relative risk | SD: Standard deviation | SE: Standard error Declarations Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki. The protocol was reviewed and approved by the Institutional Ethics Committee of Universidad del Pacífico. All participants provided written informed consent prior to participation. For the control cohort, de-identified retrospective academic data were used with institutional approval. Consent for publication Not applicable. No individual-level identifying data are presented. Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding No external funding was received for this study. The study was conducted within the regular academic activities of the Cátedra de Farmacología, Universidad del Pacífico. Authors' contributions GI: Study conception and design, data collection, statistical analysis, manuscript drafting and revision. AP, SG, RB, AF: Study design, data collection, educational intervention implementation, manuscript review and approval. All authors read and approved the final manuscript. Acknowledgements The authors thank the students of the Pharmacology course at Universidad del Pacífico for their participation, and the Academic Registrar for facilitating access to academic records. References McHugh D, Yanik AJ, Mancini MR. An innovative pharmacology curriculum for medical students: promoting higher order cognition, learner-centered coaching, and constructive feedback through a social pedagogy framework. BMC Med Educ. 2021;21(1):90. doi:10.1186/s12909-021-02516-y. Andrews LB, Barta L. Simulation as a Tool to Illustrate Clinical Pharmacology Concepts to Healthcare Program Learners. Curr Pharmacol Rep. 2020;6:182–191. doi:10.1007/s40495-020-00221-w. Roediger HL 3rd, Karpicke JD. Test-enhanced learning: taking memory tests improves long-term retention. Psychol Sci. 2006;17(3):249–255. doi:10.1111/j.1467-9280.2006.01693.x. Karpicke JD, Roediger HL 3rd. The critical importance of retrieval for learning. Science. 2008;319(5865):966–968. doi:10.1126/science.1152408. Butler AC, Roediger HL 3rd. Testing improves long-term retention in a simulated classroom setting. Eur J Cogn Psychol. 2007;19(4–5):514–527. doi:10.1080/09541440701326097. Larsen DP, Butler AC, Roediger HL 3rd. Repeated testing improves long-term retention relative to repeated study: a randomized controlled trial. Med Educ. 2009;43(12):1174–1181. doi:10.1111/j.1365-2923.2009.03518.x. Dunlosky J, Rawson KA, Marsh EJ, Nathan MJ, Willingham DT. Improving students' learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychol Sci Public Interest. 2013;14(1):4–58. doi:10.1177/1529100612453266. Szpunar KK, Khan NY, Schacter DL. Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proc Natl Acad Sci U S A. 2013;110(16):6313–6317. doi:10.1073/pnas.1221764110. Palmen LN, Vorstenbosch MA, Tanck E, Kooloos JG. What is more effective: a daily or a weekly formative test? Perspect Med Educ. 2015;4(2):73–78. doi:10.1007/s40037-015-0178-8. Martinengo L, et al. Spaced Digital Education for Health Professionals: Systematic Review and Meta-Analysis. J Med Internet Res. 2024;26:e57760. doi:10.2196/57760. Pastötter B, Bäuml KH. Retrieval practice enhances new learning: the forward effect of testing. Front Psychol. 2014;5:286. doi:10.3389/fpsyg.2014.00286. Cook DA, Hatala R, Brydges R, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA. 2011;306(9):978–988. McGaghie WC, Issenberg SB, Petrusa ER, Scalese RJ. A critical review of simulation-based medical education research: 2003–2009. Med Educ. 2010;44(1):50–63. doi:10.1111/j.1365-2923.2009.03547.x. Issenberg SB, McGaghie WC, Petrusa ER, Gordon DL, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10–28. doi:10.1080/01421590500046924. Cook DA, Brydges R, Zendejas B, Hamstra SJ, Hatala R. Mastery learning for health professionals using technology-enhanced simulation: a systematic review and meta-analysis. Acad Med. 2013;88(8):1178–1186. doi:10.1097/ACM.0b013e31829a365d. Arcoraci V, Squadrito F, Altavilla D, et al. Medical simulation in pharmacology learning and retention: a comparison study with traditional teaching in undergraduate medical students. Pharmacol Res Perspect. 2019;7(1):e00449. doi:10.1002/prp2.449. Phanudulkitti C, Puengrung S, Meepong R, et al. A systematic review on the use of virtual patient and computer-based simulation for experiential pharmacy education. Explor Res Clin Soc Pharm. 2023;11:100316. doi:10.1016/j.rcsop.2023.100316. Foucault-Fruchard L, Michelet-Barbotin V, Leichnam A, et al. The impact of using simulation-based learning to further develop communication skills of pharmacy students and pharmacists: a systematic review. BMC Med Educ. 2024;24(1):1435. doi:10.1186/s12909-024-06338-6. Lertsakulbunlue S, Kantiwong A. Development of peer assessment rubrics in simulation-based learning for advanced cardiac life support skills among medical students. Adv Simul (Lond). 2024 Jun 24;9(1):25. doi: 10.1186/s41077-024-00301-7. PMID: 38902752; PMCID: PMC11188265. Garling KA, Wong B. An initial reliability analysis of a patient counseling rubric to objectively measure student pharmacist performance. Heliyon. 2023 May 5;9(5):e15768. doi: 10.1016/j.heliyon.2023.e15768. PMID: 37206018; PMCID: PMC10189406. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 May, 2026 Editor assigned by journal 04 May, 2026 Editor invited by journal 13 Apr, 2026 Submission checks completed at journal 11 Apr, 2026 First submitted to journal 11 Apr, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9357747","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639887727,"identity":"ed22a80c-893f-4ebe-bfda-cf57223919d9","order_by":0,"name":"Guillermo Insfran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACZiCuAGJ+9uYDQEpChjgtZ4BYsudYAkgLD3E2gbQY3PAxALEJa5F35z384eAeuzyDGzyfX92oseBhYD98dAM+LYaH+dIkDjxLLpa83bvNOucY0GE8aWk38Gpp5jFj/nCAObHvztltxjlsQC0SPGaEtBh/OHCgPrHhRs4z45x/RGiRZ+YxkDhw4HDihBs5zI9z24jQYsDMYwbUcjxxZs8xM+bcPgkeNkJ+ke8/A3JYdWI/e/Pjzznf6uT42Q8fw2/LAQSbTQJM4lMOtqUBwWb+QEj1KBgFo2AUjEwAABqyTNFp9PBHAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad del Pacífico","correspondingAuthor":true,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Insfran","suffix":""},{"id":639887735,"identity":"204278d1-bca3-41ca-96b6-65e9ae0fd7bb","order_by":1,"name":"Angilberto Paredes","email":"","orcid":"","institution":"Universidad del Pacífico","correspondingAuthor":false,"prefix":"","firstName":"Angilberto","middleName":"","lastName":"Paredes","suffix":""},{"id":639887742,"identity":"76573d2c-08a4-457f-bd78-d05d038c22d8","order_by":2,"name":"Sara Ramirez","email":"","orcid":"","institution":"Universidad del Pacífico","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Ramirez","suffix":""},{"id":639887746,"identity":"9239ac71-9293-48a3-96e6-ac9fd6c3e349","order_by":3,"name":"Rocio Bogado","email":"","orcid":"","institution":"Universidad del Pacífico","correspondingAuthor":false,"prefix":"","firstName":"Rocio","middleName":"","lastName":"Bogado","suffix":""},{"id":639887751,"identity":"6299aa07-3a4d-4068-ab8f-af07428975b5","order_by":4,"name":"Ana Fiandro","email":"","orcid":"","institution":"Universidad del Pacífico","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Fiandro","suffix":""}],"badges":[],"createdAt":"2026-04-08 13:39:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9357747/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9357747/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109296367,"identity":"6b4c813c-a32a-4db1-8e81-094e408ce221","added_by":"auto","created_at":"2026-05-15 08:46:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":252851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9357747/v1/a356b224-a77c-4e5e-8b34-84609356767f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness of simulation-based learning in undergraduate Pharmacology: a quasi-experimental cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003ePharmacology is widely recognised as one of the most challenging basic science disciplines for health professions students. Learners frequently report cognitive overload, high anxiety related to the volume and clinical relevance of content, and difficulties integrating pharmacokinetics and pharmacodynamics with clinical decision-making, factors that may contribute to poor academic performance and subsequent prescribing risks [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Educators have therefore explored active, contextualised approaches to bridge conceptual knowledge and clinical application; simulation-based pedagogies \u0026mdash; encompassing virtual patients, computer simulations, high-fidelity manikin scenarios and role-playing \u0026mdash; are increasingly recommended as tools that create authentic contexts, enable deliberate practice, and promote the application of pharmacological principles to patient care [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCognitive science provides a robust rationale for incorporating frequent, low-stakes testing into instruction. The testing effect \u0026mdash; the finding that retrieval practice improves long-term retention more than additional study \u0026mdash; is well established in both laboratory and classroom settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Classroom-based research has demonstrated that retrieval practice following lectures improves delayed retention and knowledge transfer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Major synthesis papers and reviews of learning techniques conclude that retrieval practice and spacing are among the most evidence-based strategies educators can implement to promote durable learning [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTranslational work in health professions training confirms that frequent, low-stakes formative quizzes and spaced practice produce measurable improvements in subsequent summative performance and engagement. Interventions incorporating frequent low-stakes quizzes \u0026mdash; daily, weekly or spaced digital modules \u0026mdash; correlate with better summative performance and greater study diligence; some studies show little difference between daily and weekly cadences, but a consistent benefit from engagement and spacing [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Laboratory and applied studies have also documented the forward testing effect, whereby interpolated retrieval practice improves the learning of subsequently presented new material, suggesting that routine formative testing may consolidate prior material and boost acquisition of future topics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimulation-based education has a broad and growing evidence base in health professions training. Systematic reviews and meta-analyses show that technology-enhanced simulation improves knowledge, skills and behaviours compared with no intervention, and can produce moderate translational effects on clinical outcomes when designed with deliberate practice, feedback and mastery-oriented goals [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Best-practice features \u0026mdash; explicit objectives, repetitive practice, individualised feedback, increasing difficulty and validated assessment \u0026mdash; amplify the educational yield of simulation and make it a plausible vehicle for integrating conceptual pharmacology with realistic prescribing or counselling tasks [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn pharmacology and pharmacy education, a growing number of studies and systematic reviews indicate that computer simulations, virtual patients and mixed-mode simulation improve student knowledge, clinical reasoning and confidence in medication-management tasks. A randomised comparative study with undergraduate students showed improved learning and retention with simulation approaches compared with traditional pharmacology teaching [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, gaps remain: few studies have combined weekly simulation sessions with regular low-stakes formative retrieval practice and then compared outcomes against historical controls who received only conventional summative assessments.\u003c/p\u003e \u003cp\u003eAlthough evidence separately supports the efficacy of simulation and frequent retrieval practice for improving learning and retention, few semester-long evaluations have integrated weekly simulation sessions with regular formative testing and compared outcomes with historically trained cohorts. The quasi-experimental design adopted here allows assessment of the realistic, feasible effect of an instructional package \u0026mdash; clinically oriented simulation plus weekly 15-item formative tests \u0026mdash; on relevant educational outcomes (partial and final examination scores, performance trajectory and pass rate), while maintaining the usual evaluative structure of the course. Methodologically, this strategy combines principles validated by cognitive psychology (retrieval practice and spacing) with best practices of simulation education (deliberate practice, structured feedback and debriefing), increasing the plausibility of gains in both immediate learning and medium-term retention. Comparison with a retrospective cohort avoids disruption of the course, is ethically appropriate and generates evidence directly applicable to curricular implementation if meaningful benefits are demonstrated.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eA quasi-experimental study was conducted with a prospective intervention group and a retrospective historical control group, aimed at evaluating the effectiveness of clinical simulation-based learning applied to the Pharmacology course for medical students. The study was conducted in Paraguay, within the Pharmacology Department of Universidad del Pac\u0026iacute;fico.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation and study cohorts\u003c/h3\u003e\n\u003cp\u003eThe study population comprised students enrolled in the Pharmacology course, distributed across two consecutive academic cohorts. The intervention cohort included students who attended the course during the August\u0026ndash;December 2025 semester and participated in a structured clinical simulation educational strategy. The control cohort comprised students who attended the same course during the March\u0026ndash;July 2025 semester under the traditional modality without clinical simulation, whose academic records were analysed retrospectively and de-identified.\u003c/p\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eStudents were included if they were officially enrolled in the course during the corresponding semester, had a recorded attendance rate of \u0026ge;\u0026thinsp;75%, were aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, and had granted informed consent or accepted the use of de-identified academic data for research purposes. Students were excluded if they withdrew from the course before completing half the semester, failed for administrative reasons, were repeating the course, or whose final ordinary examination data could not be retrieved.\u003c/p\u003e\n\u003ch3\u003eSampling and recruitment\u003c/h3\u003e\n\u003cp\u003eA non-probabilistic convenience sampling approach was used with pre-formed cohorts. Each cohort comprised approximately 45 students per section, assigned to sections randomly by the Academic Registrar at enrolment, without instructor involvement in the assignment. Recruitment of the intervention cohort was census-based, including all eligible students who consented to participate. Students were informed at the start of the course about the study objectives, the nature of the educational intervention and data confidentiality. No academic or financial incentives were offered, and it was made clear that participation would not affect academic performance or the student\u0026ndash;institution relationship.\u003c/p\u003e\n\u003ch3\u003eVariables and data sources\u003c/h3\u003e\n\u003cp\u003eVariables analysed included age, academic section, semester, scores on three partial examinations (P1, P2, P3), score on the final ordinary examination (FE), and final course pass/fail status. Additionally, in the intervention cohort, clinical performance was assessed using an OSCE station for medical prescription and patient counselling. Data were obtained from official academic records of the department. The variable sex was excluded from the analysis due to unavailability of data.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessment instruments\u003c/h2\u003e \u003cp\u003eClinical performance in the intervention cohort was assessed using a structured 14-item OSCE rubric (maximum score 28 points; passing\u0026thinsp;\u0026ge;\u0026thinsp;60%), adapted from two previously validated instruments: the medical prescription rubric for medical students developed by Kantiwong \u0026amp; Lertsakulbunlue (2024) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and the patient counselling rubric based on Indian Health Service guidelines developed by Garling \u0026amp; Wong (2023) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Items were reviewed by local pharmacology experts to ensure content validity and piloted with third-year students. Since the control cohort did not undergo an OSCE assessment, this instrument was used exclusively as a descriptive measure of clinical performance within the intervention cohort and not as a comparative variable between cohorts.\u003c/p\u003e \u003cp\u003ePartial examinations and the final examination consisted of written multiple-choice tests, developed and validated by the teaching team, aligned with course learning objectives and the current curriculum. A minimum passing score of 60% was established for both partial examinations and the final ordinary examination, per institutional academic policy. Final course pass was operationally defined as achieving a score of \u0026ge;\u0026thinsp;60% on the final ordinary examination.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidity and reliability\u003c/h3\u003e\n\u003cp\u003eContent validity of the instruments was established through expert review by pharmacology specialists. Reliability was evaluated through a pilot test, with Cronbach's alpha coefficients above 0.70, considered acceptable for educational studies. For the OSCE assessment, standardised rubrics were used and inter-rater reliability was estimated using the intraclass correlation coefficient (ICC).\u003c/p\u003e\n\u003ch3\u003eEducational procedure\u003c/h3\u003e\n\u003cp\u003eBoth cohorts received a total of six weekly contact hours distributed as follows. Theory sessions consisted of one two-hour weekly lecture (Section A on Mondays, Section B on Tuesdays) covering course conceptual content. At the end of each theory session, four clinical cases related to the covered content were distributed to students for preparation as a pre-practical activity.\u003c/p\u003e \u003cp\u003eWeekly clinical simulation sessions of four total hours were exclusive to the intervention cohort. During these sessions, students were allocated to small groups of approximately ten students, assigned randomly according to attendance. Each group had 90 minutes to discuss and agree on the resolution of the assigned case, including selection of the therapeutic regimen, pharmacological indications and general disease management considerations. This was followed by a clinical simulation via role-playing, in which an instructor assumed the role of patient and three group members acted as treating physicians, with emphasis on clinical reasoning, rational prescribing and patient communication. The final hour of each session was devoted to a 15-item multiple-choice formative test, corrected and debriefed during the same session, with a passing criterion of \u0026ge;\u0026thinsp;60% (\u0026ge;\u0026thinsp;9/15). Weekly test scores were not included in the between-cohort comparative analysis, as they constitute a variable exclusive to the intervention.\u003c/p\u003e \u003cp\u003eThe control cohort received the six weekly hours under the traditional modality, without simulation sessions or weekly formative assessment, in accordance with the teaching model in effect during the March\u0026ndash;July 2025 semester.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were recorded in an anonymised digital database. Descriptive and comparative statistical analyses were performed. Quantitative variables were expressed as mean and standard deviation or median and interquartile range according to their distribution, assessed using the Shapiro\u0026ndash;Wilk test. Categorical variables were expressed as absolute frequencies and percentages.\u003c/p\u003e \u003cp\u003eComparison of partial and final examination scores between cohorts used the independent-samples Student's t-test or the Mann\u0026ndash;Whitney U test as appropriate for normality, supplemented with Cohen's d effect sizes. The final pass rate was compared between cohorts using the chi-square test, reporting odds ratio (OR) and relative risk (RR) with 95% confidence intervals. Within-cohort performance trajectories across the semester (P1, P2, P3, FE) were analysed using the Friedman test with Wilcoxon signed-rank post-hoc comparisons and Bonferroni correction. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Analyses were performed using R and Microsoft Excel.\u003c/p\u003e \u003cp\u003eGiven the imbalanced distribution of students across the four academic groups (Semester \u0026times; Section) and their differential allocation between cohorts, a combined academic group variable was constructed and included as a covariate in all multivariate models. The primary multivariate analysis was a multiple linear regression model (FE\u0026thinsp;~\u0026thinsp;Cohort\u0026thinsp;+\u0026thinsp;Group\u0026thinsp;+\u0026thinsp;P1\u0026thinsp;+\u0026thinsp;P2\u0026thinsp;+\u0026thinsp;P3). A logistic regression model (Pass/Fail\u0026thinsp;~\u0026thinsp;Cohort\u0026thinsp;+\u0026thinsp;Group\u0026thinsp;+\u0026thinsp;P1\u0026thinsp;+\u0026thinsp;P2\u0026thinsp;+\u0026thinsp;P3) was also fitted to estimate the adjusted odds of passing. An ANCOVA adjusted for academic group was additionally conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBias control\u003c/h2\u003e \u003cp\u003eTo reduce biases inherent to quasi-experimental designs, the following measures were adopted. Student assignment to sections was performed randomly by the Academic Registrar at enrolment, without instructor involvement, reducing selection bias and favouring baseline comparability between groups. No changes were introduced in the curriculum, learning objectives or course content between the compared semesters, ensuring curricular equivalence. Summative assessment instruments (partial and final examinations) were identical across cohorts, eliminating instrument-related difficulty differences. Standardised rubrics were used for the OSCE assessment and inter-rater reliability was estimated using ICC. Students with missing final examination data were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the principles of the Declaration of Helsinki and local regulations on human subject\u0026rsquo;s research. The protocol was reviewed and approved by the Comit\u0026eacute; de \u0026Eacute;tica Institucional de la Universidad del Pac\u0026iacute;fico (Asunci\u0026oacute;n, Paraguay). For the prospective intervention cohort, participation was voluntary and written informed consent was obtained from all participants prior to enrollment, with the right to withdraw without academic consequences guaranteed. For the retrospective control cohort, the Comit\u0026eacute; de \u0026Eacute;tica Institucional granted a waiver of individual informed consent, as data were fully de-identified prior to analysis and the study was classified as minimal risk involving only anonymized academic records used exclusively for research purposes. All data were handled confidentially and used solely for academic and research purposes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eOf 366 students enrolled in the Pharmacology course across the two evaluated semesters, 291 met the inclusion criteria and had complete data for all four summative assessments (P1, P2, P3 and final ordinary examination). Fifty-six students were excluded due to absence of a final examination record, and an additional 19 were excluded due to missing data in at least one partial examination. The analytic sample comprised 152 students in the control cohort (March\u0026ndash;July 2025) and 139 in the intervention cohort (August\u0026ndash;December 2025).\u003c/p\u003e \u003cp\u003eDistribution by academic group showed some imbalance between cohorts: Semester 1\u0026ndash;Section A (Control\u0026thinsp;=\u0026thinsp;43, Intervention\u0026thinsp;=\u0026thinsp;32), Semester 1\u0026ndash;Section B (Control\u0026thinsp;=\u0026thinsp;18, Intervention\u0026thinsp;=\u0026thinsp;36), Semester 2\u0026ndash;Section A (Control\u0026thinsp;=\u0026thinsp;36, Intervention\u0026thinsp;=\u0026thinsp;30) and Semester 2\u0026ndash;Section B (Control\u0026thinsp;=\u0026thinsp;55, Intervention\u0026thinsp;=\u0026thinsp;41). This imbalance was accounted for in multivariate analyses through the inclusion of a combined academic group variable as a covariate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics and bivariate comparisons\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises examination scores and final ordinary examination pass rate by cohort. Both groups showed similar baseline scores on the first partial examination (P1: Control\u0026thinsp;=\u0026thinsp;66.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0 vs. Intervention\u0026thinsp;=\u0026thinsp;65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6), suggesting equivalence in prior knowledge at the start of the semester. Subsequent examinations showed a downward trend in both cohorts, with slightly higher values in the control cohort for P2 and P3, and slightly higher values in the intervention cohort for the final examination.\u003c/p\u003e \u003cp\u003eThe Shapiro\u0026ndash;Wilk test indicated departure from normality in at least one cohort for P1 (intervention: W\u0026thinsp;=\u0026thinsp;0.974; p\u0026thinsp;=\u0026thinsp;0.009), P2 (control: W\u0026thinsp;=\u0026thinsp;0.978; p\u0026thinsp;=\u0026thinsp;0.016), P3 (intervention: W\u0026thinsp;=\u0026thinsp;0.979; p\u0026thinsp;=\u0026thinsp;0.031) and FE (control: W\u0026thinsp;=\u0026thinsp;0.982; p\u0026thinsp;=\u0026thinsp;0.043), justifying the use of non-parametric tests for bivariate comparisons.\u003c/p\u003e \u003cp\u003eThe Mann\u0026ndash;Whitney U test revealed no statistically significant differences between cohorts in any examination (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cohen's d effect sizes were negligible for P1 (d\u0026thinsp;=\u0026thinsp;0.05), P2 (d\u0026thinsp;=\u0026thinsp;0.13) and FE (d\u0026thinsp;=\u0026thinsp;0.10), and small for P3 (d\u0026thinsp;=\u0026thinsp;0.21). The final examination pass rate was 50.0% in the control cohort and 57.6% in the intervention cohort; this difference did not reach statistical significance (χ\u0026sup2; = 1.376; df\u0026thinsp;=\u0026thinsp;1; p\u0026thinsp;=\u0026thinsp;0.241), with OR\u0026thinsp;=\u0026thinsp;0.74 (95% CI: 0.46\u0026ndash;1.17) and RR\u0026thinsp;=\u0026thinsp;0.85 (95% CI: 0.66\u0026ndash;1.09).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and bivariate comparisons between cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \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\u003eControl (n\u0026thinsp;=\u0026thinsp;152)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention (n\u0026thinsp;=\u0026thinsp;139)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eP1 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;10,847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;11,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;11,524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFE (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;10,008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohen's d (P1/P2/P3/FE)\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\u003e0.05 / 0.13 / 0.21 / 0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFE pass rate, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2; = 1.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.46\u0026ndash;1.17)\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=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.66\u0026ndash;1.09)\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=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ep-values correspond to the Mann\u0026ndash;Whitney U test for continuous variables and Pearson chi-square for proportions. SD\u0026thinsp;=\u0026thinsp;standard deviation. FE\u0026thinsp;=\u0026thinsp;final ordinary examination. OR\u0026thinsp;=\u0026thinsp;odds ratio. RR\u0026thinsp;=\u0026thinsp;relative risk. CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWithin-cohort performance trajectory\u003c/h2\u003e \u003cp\u003eThe Friedman test revealed significant differences in the performance trajectory across the semester in both cohorts (Control: χ\u0026sup2; = 43.1; df\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Intervention: χ\u0026sup2; = 17.9; df\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;=\u0026thinsp;0.0005). However, the qualitative pattern differed substantially between groups.\u003c/p\u003e \u003cp\u003eIn the control cohort, a progressive and sustained decline in scores was observed from the first partial examination to the final ordinary examination, with significant differences in most pairwise comparisons after Bonferroni correction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In particular, comparisons P2\u0026rarr;FE (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and P3\u0026rarr;FE (p\u0026thinsp;=\u0026thinsp;0.016) were significant, indicating continuous deterioration towards the end of the semester.\u003c/p\u003e \u003cp\u003eIn contrast, the intervention cohort exhibited a different pattern: although a significant decline was observed from P1 to subsequent assessments (P1\u0026rarr;P2: p\u0026thinsp;=\u0026thinsp;0.004; P1\u0026rarr;P3: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P1\u0026rarr;FE: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), comparisons P2\u0026rarr;FE and P3\u0026rarr;FE did not reach statistical significance. This finding suggests that the simulation-based intervention with weekly formative assessment contributed to stabilising academic performance in the second half of the semester, attenuating the progressive decline observed in the control cohort.\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\u003eWilcoxon signed-rank post-hoc comparisons with Bonferroni correction \u0026mdash; within-cohort performance trajectory.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl (p-adj)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntervention (p-adj)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1 \u0026ndash; P2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1 \u0026ndash; P3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1 \u0026ndash; FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2 \u0026ndash; P3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2 \u0026ndash; FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3 \u0026ndash; FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ep-adj\u0026thinsp;=\u0026thinsp;Bonferroni-adjusted p-value. Significance levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; ns\u0026thinsp;=\u0026thinsp;not significant. Friedman global test: Control χ\u0026sup2; = 43.1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); Intervention χ\u0026sup2; = 17.9 (p\u0026thinsp;=\u0026thinsp;0.0005).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate analysis\u003c/h2\u003e \u003cp\u003eGiven the observed imbalance in the distribution of students across the four academic groups and their differential allocation between cohorts, a combined academic group variable (Semester \u0026times; Section) was constructed and included as a covariate in all multivariate models.\u003c/p\u003e \u003cp\u003eThe ANCOVA adjusted for academic group did not show a significant effect of cohort on final examination score (F\u0026thinsp;=\u0026thinsp;0.836; p\u0026thinsp;=\u0026thinsp;0.361), although academic group was a significant confounder (F\u0026thinsp;=\u0026thinsp;9.397; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Estimated marginal means were 60.6 points (95% CI: 58.3\u0026ndash;62.8) for the control cohort and 61.1 points (95% CI: 58.9\u0026ndash;63.4) for the intervention cohort, with an adjusted difference of 0.57 points (p\u0026thinsp;=\u0026thinsp;0.725).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMultiple linear regression\u003c/h2\u003e \u003cp\u003eThe multiple linear regression model (FE\u0026thinsp;~\u0026thinsp;Cohort\u0026thinsp;+\u0026thinsp;Group\u0026thinsp;+\u0026thinsp;P1\u0026thinsp;+\u0026thinsp;P2\u0026thinsp;+\u0026thinsp;P3) showed that, after controlling for academic group and partial examination performance, belonging to the intervention cohort was associated with a 3.12-point increase in the final ordinary examination score (β\u0026thinsp;=\u0026thinsp;3.12; SE\u0026thinsp;=\u0026thinsp;1.13; p\u0026thinsp;=\u0026thinsp;0.006). The model explained 55.6% of the variance in the final score (adjusted R\u0026sup2; = 0.556; F\u0026thinsp;=\u0026thinsp;52.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All three partial examinations were significant independent predictors: P1 (β\u0026thinsp;=\u0026thinsp;0.37; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), P2 (β\u0026thinsp;=\u0026thinsp;0.31; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and P3 (β\u0026thinsp;=\u0026thinsp;0.22; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Full coefficients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression \u0026mdash; dependent variable: final ordinary examination score (FE).\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\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\u003et\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\u003e95% CI lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort (Intervention)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup S1-B\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=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup S2-A\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=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup S2-B\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=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eβ\u0026thinsp;=\u0026thinsp;unstandardised coefficient. SE\u0026thinsp;=\u0026thinsp;standard error. CI\u0026thinsp;=\u0026thinsp;confidence interval. Reference category for Cohort: Control; for Group: Semester 1\u0026ndash;Section A. Adjusted R\u0026sup2; = 0.556; F(7, 283)\u0026thinsp;=\u0026thinsp;52.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ns\u0026thinsp;=\u0026thinsp;not significant.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression\u003c/h2\u003e \u003cp\u003eThe logistic regression model (Pass/Fail\u0026thinsp;~\u0026thinsp;Cohort\u0026thinsp;+\u0026thinsp;Group\u0026thinsp;+\u0026thinsp;P1\u0026thinsp;+\u0026thinsp;P2\u0026thinsp;+\u0026thinsp;P3) showed that, after controlling for academic group and partial examination performance, belonging to the intervention cohort was associated with 2.29-fold greater odds of passing the final ordinary examination (OR\u0026thinsp;=\u0026thinsp;2.29; 95% CI: 1.21\u0026ndash;4.47; p\u0026thinsp;=\u0026thinsp;0.013). P1 (OR\u0026thinsp;=\u0026thinsp;1.09; 95% CI: 1.05\u0026ndash;1.12; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and P2 (OR\u0026thinsp;=\u0026thinsp;1.10; 95% CI: 1.06\u0026ndash;1.14; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significant independent predictors of passing, whereas P3 did not reach significance in this model (OR\u0026thinsp;=\u0026thinsp;1.01; 95% CI: 0.98\u0026ndash;1.04; p\u0026thinsp;=\u0026thinsp;0.704). Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression \u0026mdash; dependent variable: pass/fail on final ordinary examination.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eCohort (Intervention)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026ndash;4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup S1-B\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=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup S2-A\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=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup S2-B\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=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026ndash;1.12\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026ndash;1.14\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.04\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 \u003cp\u003e0.704 ns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOR\u0026thinsp;=\u0026thinsp;odds ratio. CI\u0026thinsp;=\u0026thinsp;confidence interval. Reference category for Cohort: Control; for Group: Semester 1\u0026ndash;Section A. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ns\u0026thinsp;=\u0026thinsp;not significant.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSummary of findings\u003c/h2\u003e \u003cp\u003eIn summary, bivariate analyses did not detect statistically significant differences between cohorts in summative examination scores or pass rate. However, within-cohort trajectory analysis revealed that the intervention cohort showed a stabilisation of performance in the second half of the semester, in contrast to the progressive decline observed in the control group. More relevantly, multivariate models \u0026mdash; adjusted for academic group and prior performance \u0026mdash; demonstrated that belonging to the intervention cohort was independently associated with a significant 3.12-point increase in the final examination (p\u0026thinsp;=\u0026thinsp;0.006) and with 2.29-fold greater odds of passing (OR\u0026thinsp;=\u0026thinsp;2.29; p\u0026thinsp;=\u0026thinsp;0.013). These findings suggest that the effect of the intervention becomes clearer when baseline and structural differences between groups are controlled for.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the effectiveness of a clinical simulation-based intervention combined with weekly formative assessment in Pharmacology, using a quasi-experimental design with a historical control cohort. Findings are organised around three axes: the absence of significant differences in bivariate analyses, the emergence of a significant effect in adjusted multivariate models, and a differential pattern in the performance trajectory across the semester.\u003c/p\u003e \u003cp\u003eBivariate comparisons (Mann\u0026ndash;Whitney U, chi-square) did not detect statistically significant differences between cohorts in any partial examination, the final examination or the pass rate, with negligible effect sizes. This result is partially consistent with the literature: although Arcoraci et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported significant improvements with simulation in a randomised trial, systematic reviews in pharmacological and pharmacy education [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have noted that the impact of simulation on conventional summative assessments is heterogeneous and depends on the alignment between instructional modality and assessment type.\u003c/p\u003e \u003cp\u003eHowever, when controlling for academic group and prior performance through multivariate models, the intervention effect became significant. Multiple linear regression showed a 3.12-point increase in the final examination for the intervention cohort (β\u0026thinsp;=\u0026thinsp;3.12; p\u0026thinsp;=\u0026thinsp;0.006; adjusted R\u0026sup2; = 0.556), and logistic regression revealed 2.29-fold greater odds of passing (OR\u0026thinsp;=\u0026thinsp;2.29; 95% CI: 1.21\u0026ndash;4.47; p\u0026thinsp;=\u0026thinsp;0.013). This discrepancy between crude and adjusted analyses suggests that variability across academic groups and the weight of accumulated performance mask the independent contribution of the intervention, a phenomenon previously described in the medical education literature using quasi-experimental designs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe within-cohort trajectory analysis using the Friedman test constitutes perhaps the most relevant finding. Both cohorts showed a significant decline in performance across the semester, but with qualitatively distinct patterns: in the control cohort, the decrease was progressive and sustained from P1 to the final examination, whereas in the intervention cohort scores stabilised in the second half of the semester (P2\u0026rarr;FE and P3\u0026rarr;FE comparisons non-significant). This pattern is consistent with mechanisms proposed by the testing effect and spaced practice theory [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]: weekly formative assessment, by promoting active retrieval, would have attenuated the cumulative decline. Furthermore, Past\u0026ouml;tter and B\u0026auml;uml [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] demonstrated that retrieval practice improves the acquisition of subsequently presented new content, suggesting that weekly tests may have facilitated more efficient learning of later topics. The role-playing simulation sessions, by requiring the integrated application of pharmacological knowledge in realistic clinical contexts, promote deeper and more transferable encoding, potentiating the effect of retrieval practice through structured feedback and deliberate practice [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has limitations inherent to quasi-experimental designs with a historical control cohort, which cannot entirely exclude the influence of unmeasured between-cohort factors. The single-centre design limits generalisability. The intervention combines simulation and formative assessment, preventing isolation of each component's contribution; future factorial designs could decompose these contributions. Outcomes beyond the final examination were not assessed, and the absence of a formal a priori power calculation is a methodological limitation, although the sample size (n\u0026thinsp;=\u0026thinsp;291) exceeds that of most published studies in this field.\u003c/p\u003e \u003cp\u003eDespite these limitations, results suggest that combining weekly clinical simulation with formative assessment is a feasible intervention within the existing curricular structure, whose primary mechanism of action may not be elevating absolute scores but rather protecting against progressive performance deterioration across a demanding semester. Future multicentre studies with longitudinal follow-up and assessment of clinical competencies in real scenarios would allow determination of whether these benefits translate into improvements in prescribing quality and patient safety.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe incorporation of weekly clinical simulation sessions combined with formative testing in the Pharmacology course did not produce statistically significant differences in partial examination scores or pass rate in bivariate analyses. However, multivariate models adjusted for academic group and prior performance revealed that the intervention was associated with a significant increase in the final examination score (β\u0026thinsp;=\u0026thinsp;3.12; p\u0026thinsp;=\u0026thinsp;0.006) and with 2.29-fold greater odds of passing (OR\u0026thinsp;=\u0026thinsp;2.29; 95% CI: 1.21\u0026ndash;4.47; p\u0026thinsp;=\u0026thinsp;0.013). Additionally, the intervention cohort showed a stabilisation of performance in the second half of the semester, in contrast to the progressive and sustained decline observed in the control cohort, suggesting a protective effect of the intervention against cumulative performance deterioration.\u003c/p\u003e \u003cp\u003eThese findings support the feasibility and potential benefit of integrating clinical simulation and formative retrieval practice as a complementary pedagogical strategy in pharmacology education, without requiring substantial modifications to the curricular structure. Multicentre studies with factorial designs and longitudinal follow-up are recommended to confirm these results, disentangle the individual contributions of each component of the intervention, and assess its impact on clinical competencies and patient safety.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANCOVA: Analysis of covariance | CI: Confidence interval | FE: Final ordinary examination | ICC: Intraclass correlation coefficient | OR: Odds ratio | OSCE: Objective Structured Clinical Examination | P1/P2/P3: First, second and third partial examinations | RR: Relative risk | SD: Standard deviation | SE: Standard error\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. The protocol was reviewed and approved by the Institutional Ethics Committee of Universidad del Pacífico. All participants provided written informed consent prior to participation. For the control cohort, de-identified retrospective academic data were used with institutional approval.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable. No individual-level identifying data are presented.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo external funding was received for this study. The study was conducted within the regular academic activities of the Cátedra de Farmacología, Universidad del Pacífico.\u003c/p\u003e\n\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\n\u003cp\u003eGI: Study conception and design, data collection, statistical analysis, manuscript drafting and revision. AP, SG, RB, AF: Study design, data collection, educational intervention implementation, manuscript review and approval. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank the students of the Pharmacology course at Universidad del Pacífico for their participation, and the Academic Registrar for facilitating access to academic records.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcHugh D, Yanik AJ, Mancini MR. An innovative pharmacology curriculum for medical students: promoting higher order cognition, learner-centered coaching, and constructive feedback through a social pedagogy framework. BMC Med Educ. 2021;21(1):90. doi:10.1186/s12909-021-02516-y.\u003c/li\u003e\n\u003cli\u003eAndrews LB, Barta L. Simulation as a Tool to Illustrate Clinical Pharmacology Concepts to Healthcare Program Learners. Curr Pharmacol Rep. 2020;6:182\u0026ndash;191. doi:10.1007/s40495-020-00221-w.\u003c/li\u003e\n\u003cli\u003eRoediger HL 3rd, Karpicke JD. Test-enhanced learning: taking memory tests improves long-term retention. Psychol Sci. 2006;17(3):249\u0026ndash;255. doi:10.1111/j.1467-9280.2006.01693.x.\u003c/li\u003e\n\u003cli\u003eKarpicke JD, Roediger HL 3rd. The critical importance of retrieval for learning. Science. 2008;319(5865):966\u0026ndash;968. doi:10.1126/science.1152408.\u003c/li\u003e\n\u003cli\u003eButler AC, Roediger HL 3rd. Testing improves long-term retention in a simulated classroom setting. Eur J Cogn Psychol. 2007;19(4\u0026ndash;5):514\u0026ndash;527. doi:10.1080/09541440701326097.\u003c/li\u003e\n\u003cli\u003eLarsen DP, Butler AC, Roediger HL 3rd. Repeated testing improves long-term retention relative to repeated study: a randomized controlled trial. Med Educ. 2009;43(12):1174\u0026ndash;1181. doi:10.1111/j.1365-2923.2009.03518.x.\u003c/li\u003e\n\u003cli\u003eDunlosky J, Rawson KA, Marsh EJ, Nathan MJ, Willingham DT. Improving students\u0026apos; learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychol Sci Public Interest. 2013;14(1):4\u0026ndash;58. doi:10.1177/1529100612453266.\u003c/li\u003e\n\u003cli\u003eSzpunar KK, Khan NY, Schacter DL. Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proc Natl Acad Sci U S A. 2013;110(16):6313\u0026ndash;6317. doi:10.1073/pnas.1221764110.\u003c/li\u003e\n\u003cli\u003ePalmen LN, Vorstenbosch MA, Tanck E, Kooloos JG. What is more effective: a daily or a weekly formative test? Perspect Med Educ. 2015;4(2):73\u0026ndash;78. doi:10.1007/s40037-015-0178-8.\u003c/li\u003e\n\u003cli\u003eMartinengo L, et al. Spaced Digital Education for Health Professionals: Systematic Review and Meta-Analysis. J Med Internet Res. 2024;26:e57760. doi:10.2196/57760.\u003c/li\u003e\n\u003cli\u003ePast\u0026ouml;tter B, B\u0026auml;uml KH. Retrieval practice enhances new learning: the forward effect of testing. Front Psychol. 2014;5:286. doi:10.3389/fpsyg.2014.00286.\u003c/li\u003e\n\u003cli\u003eCook DA, Hatala R, Brydges R, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA. 2011;306(9):978\u0026ndash;988.\u003c/li\u003e\n\u003cli\u003eMcGaghie WC, Issenberg SB, Petrusa ER, Scalese RJ. A critical review of simulation-based medical education research: 2003\u0026ndash;2009. Med Educ. 2010;44(1):50\u0026ndash;63. doi:10.1111/j.1365-2923.2009.03547.x.\u003c/li\u003e\n\u003cli\u003eIssenberg SB, McGaghie WC, Petrusa ER, Gordon DL, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10\u0026ndash;28. doi:10.1080/01421590500046924.\u003c/li\u003e\n\u003cli\u003eCook DA, Brydges R, Zendejas B, Hamstra SJ, Hatala R. Mastery learning for health professionals using technology-enhanced simulation: a systematic review and meta-analysis. Acad Med. 2013;88(8):1178\u0026ndash;1186. doi:10.1097/ACM.0b013e31829a365d.\u003c/li\u003e\n\u003cli\u003eArcoraci V, Squadrito F, Altavilla D, et al. Medical simulation in pharmacology learning and retention: a comparison study with traditional teaching in undergraduate medical students. Pharmacol Res Perspect. 2019;7(1):e00449. doi:10.1002/prp2.449.\u003c/li\u003e\n\u003cli\u003ePhanudulkitti C, Puengrung S, Meepong R, et al. A systematic review on the use of virtual patient and computer-based simulation for experiential pharmacy education. Explor Res Clin Soc Pharm. 2023;11:100316. doi:10.1016/j.rcsop.2023.100316.\u003c/li\u003e\n\u003cli\u003eFoucault-Fruchard L, Michelet-Barbotin V, Leichnam A, et al. The impact of using simulation-based learning to further develop communication skills of pharmacy students and pharmacists: a systematic review. BMC Med Educ. 2024;24(1):1435. doi:10.1186/s12909-024-06338-6.\u003c/li\u003e\n\u003cli\u003eLertsakulbunlue S, Kantiwong A. Development of peer assessment rubrics in simulation-based learning for advanced cardiac life support skills among medical students. Adv Simul (Lond). 2024 Jun 24;9(1):25. doi: 10.1186/s41077-024-00301-7. PMID: 38902752; PMCID: PMC11188265.\u003c/li\u003e\n\u003cli\u003eGarling KA, Wong B. An initial reliability analysis of a patient counseling rubric to objectively measure student pharmacist performance. Heliyon. 2023 May 5;9(5):e15768. doi: 10.1016/j.heliyon.2023.e15768. PMID: 37206018; PMCID: PMC10189406.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"clinical simulation, formative assessment, pharmacology education, quasi-experimental, retrieval practice, medical education","lastPublishedDoi":"10.21203/rs.3.rs-9357747/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9357747/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003ePharmacology is one of the most demanding basic science courses in health professions education, with high rates of poor performance and cognitive overload. Simulation-based learning and formative retrieval practice have each been shown to improve knowledge retention independently, but few studies have combined both strategies within a single semester-long intervention and compared outcomes against a historical control cohort.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eA quasi-experimental study compared a prospective intervention cohort (August–December 2025; n = 139) with a retrospective historical control cohort (March–July 2025; n = 152) in the Pharmacology course at Universidad del Pacífico, Paraguay. The intervention consisted of weekly 4-hour sessions combining clinical simulation via role-playing and a 15-item formative test. Both cohorts sat the same three partial examinations (P1, P2, P3) and a final examination (FE). Bivariate comparisons used the Mann–Whitney U test and chi-square; within-cohort trajectories were assessed using the Friedman test with Wilcoxon post-hoc comparisons. Multivariate analyses included multiple linear regression and logistic regression adjusted for academic group and prior performance.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eBivariate comparisons showed no statistically significant differences between cohorts in any examination score or final pass rate (FE pass rate: Control 50.0% vs. Intervention 57.6%; χ² = 1.376, p = 0.241). However, within-cohort trajectory analysis revealed divergent patterns: the control cohort showed a progressive and sustained decline throughout the semester, whereas the intervention cohort stabilised in the second half (P2→FE and P3→FE comparisons non-significant). After adjusting for academic group and prior performance, the intervention cohort scored 3.12 points higher on the FE (β = 3.12; 95% CI: 0.90–5.34; p = 0.006) and had 2.29-fold greater odds of passing (OR = 2.29; 95% CI: 1.21–4.47; p = 0.013).\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eWeekly clinical simulation combined with formative testing was associated with a protective effect against progressive performance decline and with significant improvements in final examination outcomes after adjustment for baseline differences. These findings support the feasibility and educational value of integrating simulation and retrieval practice within standard pharmacology curricula. Multicentre studies with factorial designs are needed to disentangle the contributions of each component.\u003c/p\u003e","manuscriptTitle":"Effectiveness of simulation-based learning in undergraduate Pharmacology: a quasi-experimental cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 06:40:35","doi":"10.21203/rs.3.rs-9357747/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-06T09:56:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T10:26:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T16:40:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-11T13:26:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-04-11T13:22:47+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":"e07cbb15-e424-4bb0-817e-9180f92030a3","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"30","date":"2026-05-06T09:56:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T10:26:49+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:40:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 06:40:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9357747","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9357747","identity":"rs-9357747","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}