Raising theoretical knowledge and practical skills in medical statistics among Syrian doctors: a peer-led virtual intervention

Raising theoretical knowledge and practical skills in medical statistics among Syrian doctors: a peer-led virtual intervention | 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 Raising theoretical knowledge and practical skills in medical statistics among Syrian doctors: a peer-led virtual intervention Maarouf Gorra Al Nafouri, Kheder Kheder, Ibrahim Shammas, Amal Youssef, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7905589/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 Biostatistics skills are essential for medical personnel to practice evidence-based medicine, conduct high-quality research, and provide optimal patient care. In Syria, years of conflict have severely impacted the healthcare system, medical education, and research training, leading to poor biostatistics training for junior staff and students. To bridge this gap, we investigated the effectiveness of a peer-led online biostatistics workshop in fostering sustained biostatistics knowledge in early-career medical personnel. A 22-hour workshop was conducted covering core topics like study design, data types, statistical tests, and practical applications using statistical software. A case-control assessed long-term outcomes one-year post-completion. Workshop attendees served as cases ( n = 24), with three matched controls ( n = 70) per case, selected from the same cohort based on academic level and specialty. All participants completed an online questionnaire evaluating the objective knowledge, confidence in theoretical concepts, and confidence in practical skills. Cases showed higher scores than controls in objective knowledge (median 6.0 vs 4.7, P < 0.05), confidence in theoretical knowledge (44.0 vs 29.3, P 0.05), and confidence in practical skills (9.5 vs 5.7, P < 0.05). Workshop attendance was the only significant independent predictor of objective knowledge (P < 0.01) and confidence in theoretical knowledge (P < 0.05), while prior data analysis experience also predicted confidence in practical skills (P < 0.001). Peer-led online biostatistics workshops can be an effective and sustainable approach to enhance biostatistics proficiency among medical personnel in resource-limited and conflict-affected settings. Integrating theoretical with practical application supports long-term knowledge retention. Moreover, combining objective and subjective assessments provide in-depth insights into the skills gained and help identify misunderstood terms. biostatistics Syria medical education evidence-based medicine knowledge practical skills statistical literacy Figures Figure 1 Figure 2 Figure 3 Introduction A solid foundation in biostatistics is an indispensable prerequisite for updating clinical knowledge, practicing evidence-based medicine (EBM), and providing a good quality of care for patients.(Swift et al., 2009 ; Windish et al., 2007 ) Biostatistics is also essential to conduct high quality research as it facilitates accurate description and analysis of data.(Zapf et al., 2020 ) Biostatistics has become increasingly important to further aspects of the research process, including study design and data collection too.(Zapf et al., 2020 ) Furthermore, it serves as a cornerstone of public health research, particularly epidemiology and health services research.(Lee et al., 2019 ) Therefore, it is incumbent upon medical personnel to acquire a functional understanding of this fundamental field despite the challenging nature in its acquisition.(Brimacombe, 2014 ) As such, biostatistics should be regarded as an essential component of medical education.(Brimacombe, 2014 ) The healthcare system in Syria has been greatly impacted by the conflict, leading to fragmentation and a significant shortage of staff.(Boseley & editor, 2017; Physicians for Human Rights, 2016)The resulting high workload heavily affected the well-being of those working within the system, medical education, and research synthesis.(Alhaffar et al., 2019 ; Hanafi et al., 2022 ) The conflict also diminished the number of senior faculty members, leaving the remaining faculty members to struggle in delivering sufficient research skills training.(Hanafi et al., 2022 ; Saadi et al., 2018 ; Turk et al., 2018 ) Consequently, this shortage restricted the research training junior staff and undergraduate students received, resulting in inadequate guidance and mentorship.(Alhaffar et al., 2019 ; Fouad et al., 2017 ; Strengthening Human Resources for Health , n.d.)This situation emphasizes the necessity for supplementary extracurricular assistance to bridge the gap in research training.(Saadi et al., 2018 ) Several studies have assessed the effectiveness of various teaching methods in fostering medical personnel knowledge of EBM and research.(Hosny & Ghaly, 2014 ; Mai et al., n.d.; Nelson & Bedinghaus, 2013 ; Widyahening et al., 2019 ) For Instance, one study showed online biostatistics education to be as effective as on campus education in wealthy countries.(McGready & Brookmeyer, 2013 ). However, there is a shortage of such documented online trainings in war-torn or limited resource countries, where they might have better implications due to the unsafe or costly movement across the country.(Alahdab et al., 2017 ) Additionally, the lack of qualified trainers and educators in the field of biostatistics in limited resources countries poses a significant challenge to high-quality statistics training.(Rahman et al., 2020 ) Meanwhile, peer-led training was reported as an efficacious method of teaching biostatistics and evidence-based medicine when associated with practical assignments and problem-solving sessions.(Mai et al., n.d.) During the Syrian conflict, a comparable approach of peer-led training was effective in enhancing academic writing and evidence-based medicine skills.(Alahdab et al., 2017 ; Hanafi, Kheder, Sabouni, Gorra Al Nafouri, et al., 2024 ) This prompts consideration of the applicability of peer-led approaches for teaching biostatistics in countries with limited resources. Accordingly, this study aims to investigate the applicability and long-term efficacy of a peer-led training in fostering biostatistics skills among early-career Syrian medical personnel. Methods Study design and participants: We evaluated the effectiveness of an online peer-led basic medical statistics workshop using a case-control design. The workshop was publicly announced to early-career medical personnel via social media platforms. Additionally, students’ representatives and chief residents shared the invitation to their fellow students and residents, respectively through their official platforms. Students in their third undergraduate year or younger were excluded from the targeted cohort. Attendees of this workshop, which lasted from 7 April until 14 May 2020, served as cases for each participant in the intervention group, we recruited three matched controls (3:1 ratio) from the same target cohort who had not attended the presented workshop at evaluation. Controls were matched to cases based on their academic level and specialty. The academic levels used for matching included undergraduate students, recent graduates, and junior and senior residents (or master’s students). Specialties were matched in the same methods as our previous article.(Hanafi et al., 2022) The design of the workshop: The workshop consisted of 14 sessions, each lasting approximately 100 minutes, totalling 23 hours. It covered basic topics in biostatistics mainly including study designs, types of data, association and diagnostic measures, statistical tests, and power and sample size. Additionally, it included practical applications of these topics mainly using Statistical Package for the Social Sciences version 23.0 (SPSS Inc., Chicago, IL) and Microsoft Office Excel and Access 365 version 2011 (year 2020). These practical applications aimed to deliver skills like data entry and cleaning, tests of normality, the different statistical tests, as well as the basics of data representation. The sessions of the workshop were held online using Zoom and were also recorded and published on YouTube to be accessible exclusively among attendees. Workshop assessment and measures: We evaluated the impact of the workshop one year after concluding the training. The assessment was conducted using an online questionnaire, and participation was voluntary with assurance of confidentiality. Both cases and controls completed the same questionnaire and gave their informed consent to participate in our study beforehand. The questionnaire consisted of four sections; the first included questions regarding participants' demographic characteristics, previous extracurricular trainings on medical statistics, previous experiences in data analysis, and the number of research projects they were involved in. We excluded case reports and literature reviews from these research projects, as they typically not require any statistical knowledge. The second section of the survey objectively probed participants' theoretical knowledge using 15 questions derived from previously published articles, with similar targeted cohorts.(Ahmadi-Abhari et al., 2008; Msaouel et al., 2014) These questions covered most of the theoretical topics discussed during the workshop (i.e., standard deviation, standard error of the mean, confidence interval, P-value, sensitivity, specificity, likelihood ratio, risk ratio, number needed to treat, correlation coefficient, study designs, surrogates outcomes, gambler’s fallacy, and conjunction fallacy). One vignette about post-test probability was excluded, as it was not scored in the original study.(Ahmadi-Abhari et al., 2008) The answer to each of these questions was classified as correct (1 point) or false (0 points). The questions of this section were then summed into a total objective knowledge score ranging from 0 to 15. The third section of the survey assessed participants’ subjective confidence in both their theoretical knowledge (i.e., self-assessed understanding of statistical terms) and practical skills (i.e., perceived ability to apply statistical tests). The concepts assessed in this section directly aligned with the workshop content. The theoretical part included a total of 14 questions about the types of data, normal data distribution, standard deviation, standard error of the mean, P value, confidence interval, power, correlation coefficients, specificity, positive predictive value, negative likelihood ratio, odds ratio, number needed to treat, and forest plot. A nine-point-Likert scale ranging from “illiteracy” (0 points) to “excellence” (8 points) was employed here in line with earlier studies.(Chima et al., 2015; Hanafi, Kheder, Sabouni, Rahmeh, et al., 2024) The responses were summed to yield a total confidence in theoretical knowledge score, which ranged from 0 to 112. For each item, participants were classified as confident if they reported a score greater than three. The assessment of practical knowledge, on the other hand, included eight questions about calculating sample size, testing normality, choosing a suitable statistical test, and applying Chi-square, Student’s t, one-way ANOVA, correlation, and non-parametric tests. The responses followed a three-point scale: “cannot interpret or apply” (0 points), “can interpret but cannot apply” (1 point), and “can interpret and apply” (2 points). This terminology was employed to reflect the dual skills emphasized in the workshop: applying the appropriate statistical test and interpreting their results. While previous studies have adopted similar approaches, they often lacked this dual focus in their assessments, focusing instead only on whether participants were able to conduct specific statistical analyses.(Chima et al., 2015) The responses to this part of the survey yielded to a total score of confidence in practical skills ranging from 0 to 16. Participants were considered confident on individual items if they gave a score higher than one. The decision to conduct objective assessment before the subjective evaluation was made to minimize the risk of knowledge overestimation observed in prior studies,(Msaouel et al., 2014) and to sidestep the overconfidence frequently reported among medical students and clinicians.(Lakhlifi et al., 2023) At the end of this section, survey respondents were asked whether they had attended at least two sessions of the workshop, and, if so, to specify their method of attendance (i.e., live or recorded sessions). To ensure the questionnaire’s clarity and structure, we piloted it with a sample of 25 medical personnel from the same target cohort (i.e., workshop attendees). This sample included two workshop attendees who did not meet the workshop attendance criteria and were therefore excluded from the formal analysis. All respondents were able to follow the instruction and complete the survey without issues related to language, comprehension, consistency, or structure. However, nearly all respondent indicated that the objective questions were challenging and required a considerable amount of time. Data analysis: The data were exported from the Google form to SPSS (version 23.0, SPSS Inc., Chicago, IL, United States) using Microsoft Office Excel version 2011 (year 2020). Data analysis was performed using SPSS while figures were produced using Microsoft Office Excel. We represented the categorical variables in frequencies and percentages and tested their association using Chi-square or Fisher’s exact test. The continuous variables were represented in medians and interquartile ranges, as they were non-parametric. Wilcoxon matched-pairs signed-rank test was used to compare the cases and controls regarding these variables. The total scores of every three controls matched with one case were averaged and compared with the corresponding score of the case. We also used a Stepwise multiple regression analysis to test the contributions of the training and other characteristics to the variability in the three total scores. The non-parametric Spearman's Rank correlation was employed to investigate the relationship between the three total scores. Additionally, we conducted an item-wise analysis using the percentage of participants who either answered the objective questions correctly or reported confidence above the specified threshold. Results 94 participants were included in the study, comprising 24 cases and 70 controls. No significant differences were found between the groups in gender, academic level, nor previous data analysis experiences. However, more cases (70.8%) participated in extracurricular statistical training compared to controls (41.4%; P=0.01). Additionally, cases reported greater numbers of research projects than controls, with 87.5% of cases with at least one research project, versus 50.0% of controls ( P <0.01, Table 1). Compared to controls, cases showed significantly higher total objective knowledge (median 6.0 vs. 4.7; P<0.05), confidence in theoretical knowledge (44.0 vs. 29.3; P <0.05), and total confidence in practical skills (9.5 vs. 5.7; P <0.05; Figure 1). It is important to note that the confidence scores for both theoretical knowledge and practical skills exhibited a strong significant intercorrelation (R2=0.45, P <0.001). Both also correlated with the objective knowledge score (R2=0.252; P <0.001 and R2=0.163; P <0.001, respectively; Figure 2). In order to examine the contribution of course attendance (i.e., cases vs. controls), extracurricular statistical training, data analysis experience, and the number of research projects to the variance in the studied scores, a stepwise multiple linear regression analysis was employed. Our findings indicated that course attendance was the only factor predicting both the total objective knowledge score and the confidence in theoretical knowledge score (R2=0.14; P <0.01; and R2=0.11; P <0.05, respectively). On the other hand, course attendance and data analysis experience carried independent information explaining 27% of the variance in the total confidence in practical skills score ( P <0.001; Table 2). In terms of objective knowledge, cases outperformed controls in questions related to level of evidence (% of correct answers: 100% vs. 65.7%; P <0.01), standard deviation (50.0% vs. 21.4%; P <0.01), and sensitivity (75% vs. 50%; P <0.05). Additionally, cases more frequently reported higher confidence in their theoretical knowledge with differences ranging between 5.3% and 32.6%. The difference was strongest for questions about categorical and continuous data (58.3% vs. 25.7%; P <0.01), power (41.7% vs. 10%; P<0.01), and confidence interval (54.2% vs. 25.7%; P <0.05). Lastly, the most evident difference between cases and controls in their confidence about practical skills was in performing Chi-square test (50% vs. 20%; P <0.01), normality tests (37.5% vs. 11.4%; P <0.01), and non-parametric tests (20.8% vs. 5.7%; P <0.05; Figure 3). Discussion Our study showed sustained positive impact of a peer-led online biostatistics workshop on medical personnel’s knowledge and confidence compared to matched controls. Notably, this impact was superior to other extracurricular training, data analysis experience, and the number of research projects in which participants had taken part. The objective scores in our intervention group were equivalent or slightly better than average students in Greece and Iran, while the controls in our cohort performed notably worse.(Ahmadi-Abhari et al., 2008 ; Msaouel et al., 2014 ) This suggests that Syrian medical personnel generally stand well below their peers in Greece and Iran, and that our workshop was ameliorated this gap for at least one year. The lasting benefit can be linked to combining theoretical concepts with practical applications during the sessions, similar to what was found in a previous peer-led academic writing training targeting Syrian medical personnel.(Hanafi, Kheder, Sabouni, Gorra Al Nafouri, et al., 2024 )It is worth noting that several other studies have evaluated workshops incorporating biostatistics sessions.(Abbas et al., 2022; Bantounou & Kumar, 2023 ; Mai et al., n.d.; Ukrani et al., 2021 )or assessed biostatistical competence using cross-sectional designs.(Ahmadi-Abhari et al., 2008 ; Lakhlifi et al., 2023 ; Laopaiboon et al., 1997 ; Msaouel et al., 2014 ; Windish et al., 2007 ) However, direct comparisons between our cohort’s baseline knowledge and those reported in previous studies may be misleading, given the variation in assessment tools—each differing in difficulty level and the range of statistical concepts covered. This underscores the unmet need for a standardized biostatistics assessment tool that reliably captures essential statistical competencies required by medical doctors. Previous studies have demonstrated that biostatistics training, number of publications, research involvement, and higher academic qualifications are positively associated with both objective and self-perceived statistical knowledge.(F. M. Schmidt et al., 2021 ; R. L. Schmidt et al., 2017 ; Windish et al., 2007 ) However, this relationship does not fully align with the Syrian context. In our earlier work,(Hanafi, Kheder, Sabouni, Rahmeh, et al., 2024 ) we found that prior research experience was significantly associated with lower self-assessed statistical proficiency. One possible explanation is the overconfidence often observed among medical personnel, even when actual biostatistical competence is limited.(Ahmadi-Abhari et al., 2008 ; Lakhlifi et al., 2023 ; Laopaiboon et al., 1997 ; Msaouel et al., 2014 ; Windish et al., 2007 ) As students engage more deeply in research, they may become increasingly aware of their gaps in practical statistical skills, leading to more conservative self-assessments. This may also explain why participants in the case group—who voluntarily enrolled in the workshop—had more prior research experience than their matched controls. These underlying differences could potentially confound our findings. To mitigate this, we conducted a regression analysis, which identified workshop attendance as the only independent predictor of total objective knowledge score. Our study demonstrated positive correlations among the knowledge and confidence scores, with the strongest correlation between confidence in theoretical knowledge and confidence in practical skills, and the weakest correlation between objective knowledge and confidence in practical skills. These findings indicate that strong confidence in practical skills does not necessarily align with equally strong theoretical knowledge, mirroring previous observations of widespread statistical errors in the medical literature.(García-Berthou & Alcaraz, 2004 ; Gardner & Bond, 1990 ; Gore et al., 1977 ; Kanter & Taylor, 1994 ; MacArthur & Jackson, 1984 ; McKinney et al., 1989 ; Porter, 1999 ) Similarly, the moderate correlation between objective knowledge and confidence in theoretical knowledge helps explain the heterogeneous findings between objective and subjective assessments in the literature.(Msaouel et al., 2014 ; Windish et al., 2007 ) For instance, Windish et al. found that residents with higher confidence were more likely to interpret p-value correctly,(Windish et al., 2007 ) while Msaouel et al. concluded that an advanced degree did not always translate to better performance.(Msaouel et al., 2014 ) However, a recent study found, in line with our results, that although physicians’ confidence correlated with their performance, confidence tended to rise more quickly- what they called an ‘’illusion of knowledge’’ phenomenon.(Lakhlifi et al., 2023 ) These findings underscore the importance of relying on objective statistical assessments to accurately evaluate the impact of educational interventions Previous studies have indicated that the correspondence between objective knowledge and self-confidence may vary depending on the specific topic within medical statistics.(Msaouel et al., 2014 ; Windish et al., 2007 ) Interestingly, we found that both cases and controls showed tendencies to either overestimate or underestimate their confidence in specific areas. For example, 58.3% of cases and 37.1% of controls reported high confidence in interpreting the p-value, yet only 16.7% of cases and 11.4% of controls correctly answered the corresponding objective question. This may be due to misunderstanding of statistical terms, confusion with other terms or the decline of statistical knowledge over time, which might not apply to self-assessed confidence, as noted in previous studies.(Berwick et al., 1981 ; Lakhlifi et al., 2023 ; Msaouel et al., 2014 ; Windish et al., 2007 ) Conversely, both cases and controls tended to underestimate their confidence in understanding the “number needed to treat/harm”, despite demonstrating a high ability to calculate it. This discrepancy may be attributed to the relative simplicity of the calculation itself, coupled with limited exposure to this measure in medical literature.(Elliott et al., 2021 ) This topic-specific mismatch between confidence and objective knowledge may reflect variability in participants’ ability to accurately judge their own performance—an aspect known as metacognitive sensitivity. Recent findings by Lakhlifi et al. suggest that physicians’ metacognitive sensitivity in biostatistics tends to decline as their performance improves.(Lakhlifi et al., 2023 ) In contrast, our results indicate that even cases or participants who answered questions correctly were sometimes aware of limitations in their deeper conceptual understanding. Collectively, these findings highlight the importance of integrating both objective and subjective assessments of theoretical and practical skills to identify misunderstood concepts, resolve confusion, and address superficial learning through targeted instructional strategies. Limitations The selection of a case-control study design may not be the optimal method for evaluating intervention effectiveness. However, opting to assess participants' knowledge and confidence levels one-year post-completion of our biostatistics workshop was a strategic move aimed at addressing certain limitations prevalent in medical literature.(Abbas et al., 2022; Ukrani et al., 2021 )Specifically, we aimed to sidestep the common decline in biostatistics knowledge over time, often accompanied by an overestimation of confidence.(Berwick et al., 1981 ; Msaouel et al., 2014 ; Windish et al., 2007 ) Additionally, due to the limited range of statistical concepts that are commonly assessed in medical research,(Windish et al., 2007 ) and to avoid potential response bias resulting from participants memorizing the questions or focusing on specific concepts during the workshop, we chose to conduct only a post-assessment test. Finally, we cannot fully exclude the possibility of a confounding effect due to participant self-selection—namely, the higher number of prior publications and trainings among workshop attendees—which may have contributed to the positive outcomes observed. Although regression analysis indicated that these factors had a less significant impact compared to workshop attendance, some degree of residual confounding may persist. Conclusion We introduced a peer-led online workshop as an efficient approach to teaching early-career medical personnel the fundamentals of biostatistics, particularly in resource-constrained setting. This initiative offers a practical solution to the scarcity of expertise and could serve as a cost-effective means of fostering sustainable statistical proficiency. By integrating both theoretical and practical training, this educational intervention enabled trainees to immediately apply newly acquired skills, and thereby possibly maximized long-term knowledge retention. Furthermore, incorporating both objective and subjective assessments provided a more comprehensive understanding of participants’ learning, enabling the identification of misunderstood concepts and informing the development of more effective educational strategies. Abbreviations Evidence-based medicine (EBM); Statistical Package for the Social Sciences (SPSS); Office for Human Research Protections (OHRP); Institutional Review Board (IRB). Declarations Ethical approval and informed consent to participate: Participants’ consent was obtained at the beginning of the questionnaire. The study involved voluntary participation in an educational evaluation and did not include any intervention or collection of identifiable personal data. Therefore, according to the U.S. Code of Federal Regulations (45 CFR 46.104) and the Office for Human Research Protections (Protections (OHRP), 2017), the study was exempt from Institutional Review Board (IRB) review. Accordingly, the authors, who are affiliated with the Faculty of Medicine, Damascus University, confirm that the study was performed in compliance with the Declaration of Helsinki. Consent for publication: N/A Availability of the Data: The complete dataset supporting this manuscript is available and can be provided upon request to the corresponding author. Competing Interests: None of the authors has any conflicts of interests to be reported. Funding: No funding was received to conduct or publish this work. Authors’ contributions: I.H. conceived and designed the study. I.H. and K.K. organized and administered the workshop and collected the data. I.H. and M.G.N. analysed the data and designed the figures. M.G.N., K.K., I.S., and A.Y. wrote the manuscript. 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Misuse of correlation and regression in three medical journals. Journal of the Royal Society of Medicine , 92 (3), 123–128. https://doi.org/10.1177/014107689909200306 Protections (OHRP), O. for H. R. (2017, March 7). 2018 Requirements (2018 Common Rule) [Text]. https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/revised-common-rule-regulatory-text/index.html Rahman, M. M., Ghoshal, U. C., Ragunath, K., Jenkins, G., Rahman, M., Edwards, C., Hasan, M., & Taylor-Robinson, S. D. (2020). Biomedical research in developing countries: Opportunities, methods, and challenges. Indian Journal of Gastroenterology , 39 (3), 292–302. https://doi.org/10.1007/s12664-020-01056-5 Saadi, T. A., Abbas, F., Turk, T., Alkhatib, M., Hanafi, I., & Alahdab, F. (2018). Medical research in war-torn Syria: Medical students’ perspective. The Lancet , 391 (10139), 2497–2498. https://doi.org/10.1016/S0140-6736(18)31207-8 Schmidt, F. M., Zottmann, J. M., Sailer, M., Fischer, M. R., & Berndt, M. (2021). Statistical literacy and scientific reasoning & argumentation in physicians. GMS Journal for Medical Education , 38 (4), Doc77. https://doi.org/10.3205/zma001473 Schmidt, R. L., Chute, D. J., Colbert-Getz, J. M., Firpo-Betancourt, A., James, D. S., Karp, J. K., Miller, D. C., Milner, D. A., Smock, K. J., Sutton, A. T., Walker, B. S., White, K. L., Wilson, A. R., Wojcik, E. M., Yared, M. A., & Factor, R. E. (2017). Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty. Archives of Pathology & Laboratory Medicine , 141 (2), 279–287. https://doi.org/10.5858/arpa.2016-0200-OA Strengthening Human Resources for Health . (n.d.). Swift, L., Miles, S., Price, G. M., Shepstone, L., & Leinster, S. J. (2009). Do doctors need statistics? Doctors’ use of and attitudes to probability and statistics. Statistics in Medicine , 28 (15), 1969–1981. https://doi.org/10.1002/sim.3608 Turk, T., Al Saadi, T., Alkhatib, M., Hanafi, I., Alahdab, F., Firwana, B., Koudsi, M., & Al-Moujahed, A. (2018). Attitudes, barriers, and practices toward research and publication among medical students at the University of Damascus, Syria. Avicenna Journal of Medicine , 8 (01), 24–33. https://doi.org/10.4103/ajm.AJM_116_17 Ukrani, R. D., Shaikh, A. N., Martins, R. S., Fatima, S. S., Naseem, H. A., & Baig, M. A. (2021). Low-cost peer-taught virtual research workshops for medical students in Pakistan: A creative, scalable, and sustainable solution for student research. BMC Medical Education , 21 (1), 557. https://doi.org/10.1186/s12909-021-02996-y Widyahening, I. S., Findyartini, A., Ranakusuma, R. W., Dewiasty, E., & Harimurti, K. (2019). Evaluation of the role of near-peer teaching in critical appraisal skills learning: A randomized crossover trial. International Journal of Medical Education , 10 , 9–15. https://doi.org/10.5116/ijme.5c39.b55b Windish, D. M., Huot, S. J., & Green, M. L. (2007). Medicine residents’ understanding of the biostatistics and results in the medical literature. JAMA , 298 (9), 1010–1022. https://doi.org/10.1001/jama.298.9.1010 Zapf, A., Rauch, G., & Kieser, M. (2020). Why do you need a biostatistician? BMC Medical Research Methodology , 20 (1), 23. https://doi.org/10.1186/s12874-020-0916-4 Tables Table 1: Demographic characteristics of cases and controls. Factor Cases (n=24) [n (%)] Controls (n=70) [n (%)] P value* Gender Male 12 (50) 49 (70) 0.76 Academic level Undergraduates 14 (58.3) 38 (54.3) 0.73 Postgraduates 10 (41.7) 32 (45.7) Extracurricular statistical training 17 (70.8) 29 (41.4) 0.01 ‖ Data analysis experience 11 (45.8) 21 (30.0) 0.16 Number of research projects None 3 (12.5) 35 (50) One or two 10 (41.7) 19 (27.1) Three or more 11 (45.8) 16 (22.9) 0.01 ‖ *All comparisons were assessed using Chi-square test; † For residents and specialists only; ‖ statistically significant at the level of alpha=0.05 Table 2: A stepwise multiple linear regression analysis of the confounding factors that might contribute to the variance in the studied scores. Dependent variable Variable† B SE B b t R 2 ∆R 2 Total objective knowledge score 0.14 ** Course attendance 1.66 ** 0.61 0.37 2.74 Total confidence in theoretical knowledge 0.11 * Course attendance 15.22 * 6.47 0.33 2.35 Total confidence in practical skills Step 1 0.17 ** Data analysis experience 3.42 ** 1.12 0.41 3.06 Step 2 0.27 *** 0.10 * Data analysis experience 2.84 * 1.09 0.34 2.61 Course attendance 2.54 * 1.04 0.32 2.45 *P < 0.05, ** P < 0.01, *** P < 0.001, The prediction model included course attendance, extracurricular statistical training; Data analysis experience; Number of research projects participations. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 12 Dec, 2025 Editor assigned by journal 08 Dec, 2025 Editor invited by journal 13 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 13 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":199006,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of objective knowledge, confidence in theoretical knowledge, and confidence in practical skills between cases and controls.\u003c/p\u003e\n\u003cp\u003eFigure 1 legend: Each point on the control side represents an average of the values of the three controls matched to each case. The X sign refers to the median of each group. The * sign refers to statistically significant difference of the Wilcoxon signed-rank test at the level of alpha=0.05.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7905589/v1/f185a74b0c76f8bb51795e9b.png"},{"id":98749119,"identity":"e87a9fd5-9ef8-4447-ac7a-5b927a168742","added_by":"auto","created_at":"2025-12-22 08:59:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91717,"visible":true,"origin":"","legend":"\u003cp\u003eCross-correlations between the scores of objective knowledge, confidence in theoretical knowledge, and confidence in practical skills\u003c/p\u003e\n\u003cp\u003eFigure 2 Legend: The R-square value for each correlation was calculated using Spearman's Rank Correlation test. The ** and the *** signs refer to statistically significant correlations at the level of alpha=0.01 and 0.001, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7905589/v1/4e0d9e355710b55b2b593c9c.png"},{"id":98777724,"identity":"a61d4317-3450-4881-b4c5-e80bf2f6bd55","added_by":"auto","created_at":"2025-12-22 12:28:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":234707,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons between cases and controls in each item of the studied scores.\u003c/p\u003e\n\u003cp\u003eFigure 3 legend: Spider charts illustrating the percentages of case and controls who answered correctly for the objective knowledge questions (a) or had higher confidence on the items of the other two subjective scales (b and c). The * and ** signs refer to statistically significant differences of the Chi-square test at the level of alpha=0.05 and 0.01, respectively. The † and †† signs refer to statistically significant differences of the Fischer’s Exact test at the level of alpha=0.05 and 0.01, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7905589/v1/1198f4567eb4b2d5279ea159.png"},{"id":98783278,"identity":"8a16ce41-fffd-41cf-8461-923d97ca4b07","added_by":"auto","created_at":"2025-12-22 12:41:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1120259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7905589/v1/49b75d9e-dbf0-4e5f-8153-5aa6887fb37e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Raising theoretical knowledge and practical skills in medical statistics among Syrian doctors: a peer-led virtual intervention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA solid foundation in biostatistics is an indispensable prerequisite for updating clinical knowledge, practicing evidence-based medicine (EBM), and providing a good quality of care for patients.(Swift et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) Biostatistics is also essential to conduct high quality research as it facilitates accurate description and analysis of data.(Zapf et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Biostatistics has become increasingly important to further aspects of the research process, including study design and data collection too.(Zapf et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Furthermore, it serves as a cornerstone of public health research, particularly epidemiology and health services research.(Lee et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Therefore, it is incumbent upon medical personnel to acquire a functional understanding of this fundamental field despite the challenging nature in its acquisition.(Brimacombe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) As such, biostatistics should be regarded as an essential component of medical education.(Brimacombe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe healthcare system in Syria has been greatly impacted by the conflict, leading to fragmentation and a significant shortage of staff.(Boseley \u0026amp; editor, 2017; Physicians for Human Rights, 2016)The resulting high workload heavily affected the well-being of those working within the system, medical education, and research synthesis.(Alhaffar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hanafi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) The conflict also diminished the number of senior faculty members, leaving the remaining faculty members to struggle in delivering sufficient research skills training.(Hanafi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saadi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Turk et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) Consequently, this shortage restricted the research training junior staff and undergraduate students received, resulting in inadequate guidance and mentorship.(Alhaffar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fouad et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u003cem\u003eStrengthening Human Resources for Health\u003c/em\u003e, n.d.)This situation emphasizes the necessity for supplementary extracurricular assistance to bridge the gap in research training.(Saadi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSeveral studies have assessed the effectiveness of various teaching methods in fostering medical personnel knowledge of EBM and research.(Hosny \u0026amp; Ghaly, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mai et al., n.d.; Nelson \u0026amp; Bedinghaus, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Widyahening et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) For Instance, one study showed online biostatistics education to be as effective as on campus education in wealthy countries.(McGready \u0026amp; Brookmeyer, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, there is a shortage of such documented online trainings in war-torn or limited resource countries, where they might have better implications due to the unsafe or costly movement across the country.(Alahdab et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Additionally, the lack of qualified trainers and educators in the field of biostatistics in limited resources countries poses a significant challenge to high-quality statistics training.(Rahman et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Meanwhile, peer-led training was reported as an efficacious method of teaching biostatistics and evidence-based medicine when associated with practical assignments and problem-solving sessions.(Mai et al., n.d.) During the Syrian conflict, a comparable approach of peer-led training was effective in enhancing academic writing and evidence-based medicine skills.(Alahdab et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hanafi, Kheder, Sabouni, Gorra Al Nafouri, et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) This prompts consideration of the applicability of peer-led approaches for teaching biostatistics in countries with limited resources. Accordingly, this study aims to investigate the applicability and long-term efficacy of a peer-led training in fostering biostatistics skills among early-career Syrian medical personnel.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design and participants:\u003c/h2\u003e\n\u003cp\u003eWe evaluated the effectiveness of an online peer-led basic medical statistics workshop using a case-control design. The workshop was publicly announced to early-career medical personnel via social media platforms. Additionally, students\u0026rsquo; representatives and chief residents shared the invitation to their fellow students and residents, respectively through their official platforms. Students in their third undergraduate year or younger were excluded from the targeted cohort. Attendees of this workshop, which lasted from 7 April until 14 May 2020, served as cases\u0026nbsp;for each participant in the intervention group, we recruited three matched controls (3:1 ratio) from the same target cohort who had not attended the presented workshop at evaluation.\u0026nbsp; Controls were matched to cases based on their academic level and specialty. The academic levels used for matching included undergraduate students, recent graduates, and junior and senior residents (or master\u0026rsquo;s students). Specialties were matched in the same methods as our previous article.(Hanafi et al., 2022)\u003c/p\u003e\n\u003ch2\u003eThe design of the workshop:\u003c/h2\u003e\n\u003cp\u003eThe workshop consisted of 14 sessions, each lasting approximately 100 minutes, totalling 23 hours. It covered basic topics in biostatistics mainly including study designs, types of data, association and diagnostic measures, statistical tests, and power and sample size. Additionally, it included practical applications of these topics mainly using Statistical Package for the Social Sciences version 23.0 (SPSS Inc., Chicago, IL) and Microsoft Office Excel and Access 365 version 2011 (year 2020). These practical applications aimed to deliver skills like data entry and cleaning, tests of normality, the different statistical tests, as well as the basics of data representation. The sessions of the workshop were held online using Zoom and were also recorded and published on YouTube to be accessible exclusively among attendees.\u003c/p\u003e\n\u003ch2\u003eWorkshop assessment and measures:\u003c/h2\u003e\n\u003cp\u003eWe evaluated the impact of the workshop one year after concluding the training. The assessment was conducted using an online questionnaire, and participation was voluntary with assurance of confidentiality. Both cases and controls completed the same questionnaire and gave their informed consent to participate in our study beforehand.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe questionnaire consisted of four sections; the first included questions regarding participants\u0026apos; demographic characteristics, previous extracurricular trainings on medical statistics, previous experiences in data analysis, and the number of research projects they were involved in. We excluded case reports and literature reviews from these research projects, as they typically not require any statistical knowledge. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second section of the survey objectively probed participants\u0026apos; theoretical knowledge using 15 questions derived from previously published articles, with similar targeted cohorts.(Ahmadi-Abhari et al., 2008; Msaouel et al., 2014)\u0026nbsp;These questions covered most of the theoretical topics discussed during the workshop (i.e., standard deviation, standard error of the mean, confidence interval, P-value, sensitivity, specificity, likelihood ratio, risk ratio, number needed to treat, correlation coefficient, study designs, surrogates outcomes, gambler\u0026rsquo;s fallacy, and conjunction fallacy). \u0026nbsp;One vignette about post-test probability was excluded, as it was not scored in the original study.(Ahmadi-Abhari et al., 2008) \u0026nbsp;The answer to each of these questions was classified as correct (1 point) or false (0 points). The questions of this section were then summed into a total objective knowledge score ranging from 0 to 15.\u003c/p\u003e\n\u003cp\u003eThe third section of the survey assessed participants\u0026rsquo; subjective confidence in both their theoretical knowledge (i.e., self-assessed understanding of statistical terms) and practical skills (i.e., perceived ability to apply statistical tests). \u0026nbsp;The concepts assessed in this section directly aligned with the workshop content. The theoretical part included a total of 14 questions about the types of data, normal data distribution, standard deviation, standard error of the mean, P value, confidence interval, power, correlation coefficients, specificity, positive predictive value, negative likelihood ratio, odds ratio, number needed to treat, and forest plot. A nine-point-Likert scale ranging from \u0026ldquo;illiteracy\u0026rdquo; (0 points) to \u0026ldquo;excellence\u0026rdquo; (8 points) was employed here in line with earlier studies.(Chima et al., 2015; Hanafi, Kheder, Sabouni, Rahmeh, et al., 2024) The responses were summed to yield a total confidence in theoretical knowledge score, which ranged from 0 to 112. For each item, participants were classified as confident if they reported a score greater than three.\u003c/p\u003e\n\u003cp\u003eThe assessment of practical knowledge, on the other hand, included eight questions about calculating sample size, testing normality, choosing a suitable statistical test, and applying Chi-square, Student\u0026rsquo;s t, one-way ANOVA, correlation, and non-parametric tests. The responses followed a three-point scale: \u0026ldquo;cannot interpret or apply\u0026rdquo; (0 points), \u0026ldquo;can interpret but cannot apply\u0026rdquo; (1 point), and \u0026ldquo;can interpret and apply\u0026rdquo; (2 points). This terminology was employed to reflect the dual skills emphasized in the workshop: applying the appropriate statistical test and interpreting their results. While previous studies have adopted similar approaches, they often lacked this dual focus in their assessments, focusing instead only on whether participants were able to conduct specific statistical analyses.(Chima et al., 2015) The responses to this part of the survey yielded to a total score of confidence in practical skills ranging from 0 to 16. Participants were considered confident on individual items if they gave a score higher than one. The decision to conduct objective assessment before the subjective evaluation was made to minimize the risk of knowledge overestimation observed in prior studies,(Msaouel et al., 2014)\u0026nbsp;and to sidestep the overconfidence frequently reported among medical students and clinicians.(Lakhlifi et al., 2023)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the end of this section, survey respondents were asked whether they had attended at least two sessions of the workshop, and, if so, to specify their method of attendance (i.e., live or recorded sessions).\u003c/p\u003e\n\u003cp\u003eTo ensure the questionnaire\u0026rsquo;s clarity and structure, we piloted it with a sample of 25 medical personnel from the same target cohort (i.e., workshop attendees). This sample included two workshop attendees who did not meet the workshop attendance criteria and were therefore excluded from the formal analysis. All respondents were able to follow the instruction and complete the survey without issues related to language, comprehension, consistency, or structure. However, nearly all respondent indicated that the objective questions were challenging and required a considerable amount of time.\u003c/p\u003e\n\u003ch2\u003eData analysis:\u003c/h2\u003e\n\u003cp\u003eThe data were exported from the Google form to SPSS (version 23.0, SPSS Inc., Chicago, IL, United States) using Microsoft Office Excel version 2011 (year 2020). Data analysis was performed using SPSS while figures were produced using Microsoft Office Excel. We represented the categorical variables in frequencies and percentages and tested their association using Chi-square or Fisher\u0026rsquo;s exact test. The continuous variables were represented in medians and interquartile ranges, as they were non-parametric. Wilcoxon matched-pairs signed-rank test was used to compare the cases and controls regarding these variables. The total scores of every three controls matched with one case were averaged and compared with the corresponding score of the case. We also used a Stepwise multiple regression analysis to test the contributions of the training and other characteristics to the variability in the three total scores. The non-parametric Spearman\u0026apos;s Rank correlation was employed to investigate the relationship between the three total scores. Additionally, we conducted an item-wise analysis using the percentage of participants who either answered the objective questions correctly or reported confidence above the specified threshold.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e94 participants were included in the study, comprising 24 cases and 70 controls. No significant differences were found between the groups in gender, academic level, nor previous data analysis experiences. However, more cases (70.8%) participated in extracurricular statistical training compared to controls (41.4%; P=0.01). Additionally, cases reported greater numbers of research projects than controls, with 87.5% of cases with at least one research project, versus 50.0% of controls (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to controls, cases showed significantly higher total objective knowledge (median 6.0 vs. 4.7; P\u0026lt;0.05), confidence in theoretical knowledge (44.0 vs. 29.3; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), and total confidence in practical skills (9.5 vs. 5.7; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; Figure 1). It is important to note that the confidence scores for both theoretical knowledge and practical skills exhibited a strong significant intercorrelation (R2=0.45, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Both also correlated with the objective knowledge score (R2=0.252; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 and R2=0.163; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, respectively; Figure 2).\u003c/p\u003e\n\u003cp\u003eIn order to examine the contribution of course attendance (i.e., cases vs. controls), extracurricular statistical training, data analysis experience, and the number of research projects to the variance in the studied scores, a stepwise multiple linear regression analysis was employed. Our findings indicated that course attendance was the only factor predicting both the total objective knowledge score and the confidence in theoretical knowledge score (R2=0.14; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01; and R2=0.11; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, respectively). On the other hand, course attendance and data analysis experience carried independent information explaining 27% of the variance in the total confidence in practical skills score (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; Table 2). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of objective knowledge, cases outperformed controls in questions related to level of evidence (% of correct answers: 100% vs. 65.7%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), standard deviation (50.0% vs. 21.4%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), and sensitivity (75% vs. 50%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Additionally, cases more frequently reported higher confidence in their theoretical knowledge with differences ranging between 5.3% and 32.6%. The difference was strongest for questions about categorical and continuous data (58.3% vs. 25.7%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), power (41.7% vs. 10%; P\u0026lt;0.01), and confidence interval (54.2% vs. 25.7%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Lastly, the most evident difference between cases and controls in their confidence about practical skills was in performing Chi-square test (50% vs. 20%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), normality tests (37.5% vs. 11.4%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), and non-parametric tests (20.8% vs. 5.7%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; Figure 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study showed sustained positive impact of a peer-led online biostatistics workshop on medical personnel\u0026rsquo;s knowledge and confidence compared to matched controls. Notably, this impact was superior to other extracurricular training, data analysis experience, and the number of research projects in which participants had taken part.\u003c/p\u003e \u003cp\u003eThe objective scores in our intervention group were equivalent or slightly better than average students in Greece and Iran, while the controls in our cohort performed notably worse.(Ahmadi-Abhari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) This suggests that Syrian medical personnel generally stand well below their peers in Greece and Iran, and that our workshop was ameliorated this gap for at least one year. The lasting benefit can be linked to combining theoretical concepts with practical applications during the sessions, similar to what was found in a previous peer-led academic writing training targeting Syrian medical personnel.(Hanafi, Kheder, Sabouni, Gorra Al Nafouri, et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)It is worth noting that several other studies have evaluated workshops incorporating biostatistics sessions.(Abbas et al., 2022; Bantounou \u0026amp; Kumar, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mai et al., n.d.; Ukrani et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)or assessed biostatistical competence using cross-sectional designs.(Ahmadi-Abhari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lakhlifi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Laopaiboon et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) However, direct comparisons between our cohort\u0026rsquo;s baseline knowledge and those reported in previous studies may be misleading, given the variation in assessment tools\u0026mdash;each differing in difficulty level and the range of statistical concepts covered. This underscores the unmet need for a standardized biostatistics assessment tool that reliably captures essential statistical competencies required by medical doctors.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that biostatistics training, number of publications, research involvement, and higher academic qualifications are positively associated with both objective and self-perceived statistical knowledge.(F. M. Schmidt et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; R. L. Schmidt et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) However, this relationship does not fully align with the Syrian context. In our earlier work,(Hanafi, Kheder, Sabouni, Rahmeh, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) we found that prior research experience was significantly associated with lower self-assessed statistical proficiency. One possible explanation is the overconfidence often observed among medical personnel, even when actual biostatistical competence is limited.(Ahmadi-Abhari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lakhlifi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Laopaiboon et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) As students engage more deeply in research, they may become increasingly aware of their gaps in practical statistical skills, leading to more conservative self-assessments. This may also explain why participants in the case group\u0026mdash;who voluntarily enrolled in the workshop\u0026mdash;had more prior research experience than their matched controls. These underlying differences could potentially confound our findings. To mitigate this, we conducted a regression analysis, which identified workshop attendance as the only independent predictor of total objective knowledge score.\u003c/p\u003e \u003cp\u003eOur study demonstrated positive correlations among the knowledge and confidence scores, with the strongest correlation between confidence in theoretical knowledge and confidence in practical skills, and the weakest correlation between objective knowledge and confidence in practical skills. These findings indicate that strong confidence in practical skills does not necessarily align with equally strong theoretical knowledge, mirroring previous observations of widespread statistical errors in the medical literature.(Garc\u0026iacute;a-Berthou \u0026amp; Alcaraz, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gardner \u0026amp; Bond, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Gore et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Kanter \u0026amp; Taylor, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; MacArthur \u0026amp; Jackson, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; McKinney et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Porter, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) Similarly, the moderate correlation between objective knowledge and confidence in theoretical knowledge helps explain the heterogeneous findings between objective and subjective assessments in the literature.(Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) For instance, Windish et al. found that residents with higher confidence were more likely to interpret p-value correctly,(Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) while Msaouel et al. concluded that an advanced degree did not always translate to better performance.(Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) However, a recent study found, in line with our results, that although physicians\u0026rsquo; confidence correlated with their performance, confidence tended to rise more quickly- what they called an \u0026lsquo;\u0026rsquo;illusion of knowledge\u0026rsquo;\u0026rsquo; phenomenon.(Lakhlifi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) These findings underscore the importance of relying on objective statistical assessments to accurately evaluate the impact of educational interventions\u003c/p\u003e \u003cp\u003ePrevious studies have indicated that the correspondence between objective knowledge and self-confidence may vary depending on the specific topic within medical statistics.(Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) Interestingly, we found that both cases and controls showed tendencies to either overestimate or underestimate their confidence in specific areas. For example, 58.3% of cases and 37.1% of controls reported high confidence in interpreting the p-value, yet only 16.7% of cases and 11.4% of controls correctly answered the corresponding objective question. This may be due to misunderstanding of statistical terms, confusion with other terms or the decline of statistical knowledge over time, which might not apply to self-assessed confidence, as noted in previous studies.(Berwick et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Lakhlifi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) Conversely, both cases and controls tended to underestimate their confidence in understanding the \u0026ldquo;number needed to treat/harm\u0026rdquo;, despite demonstrating a high ability to calculate it. This discrepancy may be attributed to the relative simplicity of the calculation itself, coupled with limited exposure to this measure in medical literature.(Elliott et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) This topic-specific mismatch between confidence and objective knowledge may reflect variability in participants\u0026rsquo; ability to accurately judge their own performance\u0026mdash;an aspect known as metacognitive sensitivity. Recent findings by Lakhlifi et al. suggest that physicians\u0026rsquo; metacognitive sensitivity in biostatistics tends to decline as their performance improves.(Lakhlifi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) In contrast, our results indicate that even cases or participants who answered questions correctly were sometimes aware of limitations in their deeper conceptual understanding. Collectively, these findings highlight the importance of integrating both objective and subjective assessments of theoretical and practical skills to identify misunderstood concepts, resolve confusion, and address superficial learning through targeted instructional strategies.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThe selection of a case-control study design may not be the optimal method for evaluating intervention effectiveness. However, opting to assess participants' knowledge and confidence levels one-year post-completion of our biostatistics workshop was a strategic move aimed at addressing certain limitations prevalent in medical literature.(Abbas et al., 2022; Ukrani et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)Specifically, we aimed to sidestep the common decline in biostatistics knowledge over time, often accompanied by an overestimation of confidence.(Berwick et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Msaouel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) Additionally, due to the limited range of statistical concepts that are commonly assessed in medical research,(Windish et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and to avoid potential response bias resulting from participants memorizing the questions or focusing on specific concepts during the workshop, we chose to conduct only a post-assessment test. Finally, we cannot fully exclude the possibility of a confounding effect due to participant self-selection\u0026mdash;namely, the higher number of prior publications and trainings among workshop attendees\u0026mdash;which may have contributed to the positive outcomes observed. Although regression analysis indicated that these factors had a less significant impact compared to workshop attendance, some degree of residual confounding may persist.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e We introduced a peer-led online workshop as an efficient approach to teaching early-career medical personnel the fundamentals of biostatistics, particularly in resource-constrained setting. This initiative offers a practical solution to the scarcity of expertise and could serve as a cost-effective means of fostering sustainable statistical proficiency. By integrating both theoretical and practical training, this educational intervention enabled trainees to immediately apply newly acquired skills, and thereby possibly maximized long-term knowledge retention. Furthermore, incorporating both objective and subjective assessments provided a more comprehensive understanding of participants\u0026rsquo; learning, enabling the identification of misunderstood concepts and informing the development of more effective educational strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEvidence-based medicine (EBM); Statistical Package for the Social Sciences (SPSS); Office for Human Research Protections (OHRP); Institutional Review Board (IRB).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical approval and informed consent to participate:\u003c/h2\u003e\n\u003cp\u003eParticipants\u0026rsquo; consent was obtained at the beginning of the questionnaire. The study involved voluntary participation in an educational evaluation and did not include any intervention or collection of identifiable personal data. Therefore, according to the U.S. Code of Federal Regulations (45 CFR 46.104) and the Office for Human Research Protections (Protections (OHRP), 2017), the study was exempt from Institutional Review Board (IRB) review. Accordingly, the authors, who are affiliated with the Faculty of Medicine, Damascus University, confirm that the study was performed in compliance with the \u003cem\u003eDeclaration of Helsinki.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eConsent for publication:\u003c/h2\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003ch2\u003eAvailability of the Data:\u003c/h2\u003e\n\u003cp\u003eThe complete dataset supporting this manuscript is available and can be provided upon request to the corresponding author.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\n\u003cp\u003eNone of the authors has any conflicts of interests to be reported.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNo funding was received to conduct or publish this work.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions:\u003c/h2\u003e\n\u003cp\u003eI.H. conceived and designed the study. I.H. and K.K. organized and administered the workshop and collected the data. I.H. and M.G.N. analysed the data and designed the figures. M.G.N., K.K., I.S., and A.Y. wrote the manuscript. I.H. critically revised the manuscript.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eAcknowledgment:\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors have no acknowledgements to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbas, M., Hareem Rauf, Javeria Bilal Qamar, Syeda Ramlah Tul Sania, Russell Seth Martins, \u0026amp; Zahra Hoodbhoy. (2022). Evaluation of data analytics workshop using RStudio amongst medical students in Pakistan. \u003cem\u003eJournal of the Pakistan Medical Association\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(1), 222\u0026ndash;224. https://doi.org/10.47391/JPMA.6450\u003c/li\u003e\n \u003cli\u003eAhmadi-Abhari, S., Soltani, A., \u0026amp; Hosseinpanah, F. (2008). 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Evaluation of the role of near-peer teaching in critical appraisal skills learning: A randomized crossover trial. \u003cem\u003eInternational Journal of Medical Education\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 9\u0026ndash;15. https://doi.org/10.5116/ijme.5c39.b55b\u003c/li\u003e\n \u003cli\u003eWindish, D. M., Huot, S. J., \u0026amp; Green, M. L. (2007). Medicine residents\u0026rsquo; understanding of the biostatistics and results in the medical literature. \u003cem\u003eJAMA\u003c/em\u003e, \u003cem\u003e298\u003c/em\u003e(9), 1010\u0026ndash;1022. https://doi.org/10.1001/jama.298.9.1010\u003c/li\u003e\n \u003cli\u003eZapf, A., Rauch, G., \u0026amp; Kieser, M. (2020). Why do you need a biostatistician? \u003cem\u003eBMC Medical Research Methodology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 23. https://doi.org/10.1186/s12874-020-0916-4\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Demographic characteristics of cases and controls.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003cp\u003e(n=24)\u003c/p\u003e\n \u003cp\u003e[n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003cp\u003e(n=70)\u003c/p\u003e\n \u003cp\u003e[n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e12 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e49 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003eAcademic level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eUndergraduates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e14 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e38 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003ePostgraduates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e10 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e32 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003eExtracurricular statistical training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e17 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e29 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e0.01\u003csup\u003e‖\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003eData analysis experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e11 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e21 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003eNumber of research projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e3 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e35 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eOne or two\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e10 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e19 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.5985%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eThree or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8686%;\"\u003e\n \u003cp\u003e11 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7737%;\"\u003e\n \u003cp\u003e16 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1387%;\"\u003e\n \u003cp\u003e0.01\u003csup\u003e‖\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e*All comparisons were assessed using Chi-square test; \u0026dagger; For residents and specialists only; ‖ statistically significant at the level of alpha=0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: A stepwise multiple linear regression analysis of the confounding factors that might contribute to the variance in the studied scores.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003eDependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003eVariable\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003eSE B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e∆R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003eTotal objective knowledge score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e0.14 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003eCourse attendance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e1.66 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003eTotal confidence in theoretical knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e0.11 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003eCourse attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e15.22 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003eTotal confidence in practical skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003eStep 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e0.17 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003eData analysis experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e3.42 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003eStep 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e0.27 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e0.10 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003eData analysis experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e2.84 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9533%;\"\u003e\n \u003cp\u003eCourse attendance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96885%;\"\u003e\n \u003cp\u003e2.54 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.94393%;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25545%;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41121%;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.34579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e*P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, The prediction model included course attendance, extracurricular statistical training; Data analysis experience; Number of research projects participations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"biostatistics, Syria, medical education, evidence-based medicine, knowledge, practical skills, statistical literacy","lastPublishedDoi":"10.21203/rs.3.rs-7905589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7905589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiostatistics skills are essential for medical personnel to practice evidence-based medicine, conduct high-quality research, and provide optimal patient care. In Syria, years of conflict have severely impacted the healthcare system, medical education, and research training, leading to poor biostatistics training for junior staff and students. To bridge this gap, we investigated the effectiveness of a peer-led online biostatistics workshop in fostering sustained biostatistics knowledge in early-career medical personnel.\u003c/p\u003e \u003cp\u003eA 22-hour workshop was conducted covering core topics like study design, data types, statistical tests, and practical applications using statistical software. A case-control assessed long-term outcomes one-year post-completion. Workshop attendees served as cases (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24), with three matched controls (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70) per case, selected from the same cohort based on academic level and specialty. All participants completed an online questionnaire evaluating the objective knowledge, confidence in theoretical concepts, and confidence in practical skills.\u003c/p\u003e \u003cp\u003eCases showed higher scores than controls in objective knowledge (median 6.0 vs 4.7, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confidence in theoretical knowledge (44.0 vs 29.3, P 0.05), and confidence in practical skills (9.5 vs 5.7, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Workshop attendance was the only significant independent predictor of objective knowledge (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and confidence in theoretical knowledge (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while prior data analysis experience also predicted confidence in practical skills (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003ePeer-led online biostatistics workshops can be an effective and sustainable approach to enhance biostatistics proficiency among medical personnel in resource-limited and conflict-affected settings. Integrating theoretical with practical application supports long-term knowledge retention. Moreover, combining objective and subjective assessments provide in-depth insights into the skills gained and help identify misunderstood terms.\u003c/p\u003e","manuscriptTitle":"Raising theoretical knowledge and practical skills in medical statistics among Syrian doctors: a peer-led virtual intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:59:48","doi":"10.21203/rs.3.rs-7905589/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-12-12T07:22:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T14:57:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-13T18:50:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T10:19:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-11-13T10:16:10+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":"6f7c41fd-163d-42d8-84bd-f3a2114760bd","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T08:59:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:59:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7905589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7905589","identity":"rs-7905589","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}