Effectiveness of a structured ECG training program among medical students and early-career doctors in Nigeria: A pre-post quasi-experimental study

Effectiveness of a structured ECG training program among medical students and early-career doctors in Nigeria: A pre-post quasi-experimental study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effectiveness of a structured ECG training program among medical students and early-career doctors in Nigeria: A pre-post quasi-experimental study Harry Okwilagwe, Amir Yahaya, Olumide Noah, Okwukweamaka Okoli, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9204737/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: Electrocardiogram (ECG) interpretation is a fundamental clinical skill required for the timely diagnosis and management of cardiovascular conditions. However, multiple studies have shown that medical students and early-career doctors often demonstrate inadequate ECG knowledge and low confidence in interpretation. The aim of our study was to assess the effectiveness of a structured ECG workshop on improving the knowledge and self-reported confidence of ECG interpretation among medical students and early-career doctors in Nigeria. Methods: A one-group pretest–posttest quasi-experimental design was conducted to evaluate the effectiveness of a structured online electrocardiography (ECG) training workshop. The study population comprised medical students and early-career doctors who attended at least 75% of the training sessions. Data were collected using validated, self-administered online questionnaires that assessed participants’ ECG knowledge and self-reported confidence before and immediately after the intervention. Pre- and post-intervention scores were compared using appropriate non-parametric statistical tests, and associations between outcome measures and selected sociodemographic variables were further explored. Results: Of 424 enrolled participants, 188 completed both assessments (attrition rate 55.7%). The mean knowledge score increased from 42.6% to 67.9% (25.3% improvement; p < .001), while mean confidence scores increased from 39.2% to 72.6% (33.5% improvement; p < .001), both with large effect sizes. Participants without prior ECG training and medical students demonstrated significantly greater improvements. Overall satisfaction with the training was high (mean score 4.3 ± 0.8). Conclusion: This study demonstrates that structured online ECG training significantly improved ECG interpretation knowledge and confidence among medical students and early-career doctors in Nigeria. By addressing gaps in undergraduate and early postgraduate medical training through scalable, interactive ECG workshops can equip trainees to make informed clinical decisions on ECG interpretation and strengthen healthcare delivery in resource-limited settings. Electrocardiography Medical education Clinical competence Medical students Early-career doctors Figures Figure 1 Background The electrocardiogram (ECG) is a vital non-invasive diagnostic tool in clinical medicine, essential for evaluating cardiac rhythm disturbances, myocardial ischemia, and structural abnormalities. The increasing prevalence of cardiovascular disease (CVDs) and the need for accurate diagnosis and treatment has highlighted the significance of understanding ECG knowledge and application within the medical community. ECG is essential in the diagnosis of CVDs such as coronary artery disease (CAD), arrhythmias, inflammatory heart diseases and certain electrolyte imbalances. Proper ECG interpretation is vital in the management of cardiac diseases and emergencies. 1 ECG misinterpretation can result in erroneous clinical decisions with harmful outcomes. 2 Despite the widespread clinical use of ECG, several studies have shown that performance in ECG interpretation is poor among both undergraduate and postgraduate students of medicine, cardiologists included. 3 Furthermore, studies showed that the majority of medical students felt a low level of self-confidence regarding their competence in ECG interpretation. 4 In low-resource settings like Nigeria, additional barriers include a lack of up-to-date knowledge about ECGs and infrequent use of this skill in practice. 5 Hence, this necessitates an effective way to improve the knowledge of ECG interpretation among medical students and doctors. In response to this issue, a variety of learning and teaching methods have been developed to enhance ECG competence, including courses and workshops. Such modalities recorded significant improvements in the knowledge and confidence of ECG interpretations of the participants, especially when compared with classroom lectures. 6 Blended learning, which combines face-to-face lectures with e-learning, has proven effective in increasing confidence in ECG analysis and interpretation, as well as enhancing diagnostic accuracy. 2 E-learning provides a convenient and asynchronous learning experience and facilitates immediate online feedback. Self-directed learning (SDL) is beneficial in that it allows for repetitive practice and focused revision of learning material, thereby increasing the time spent on learning. 2 Focused teaching program (FTP) was found to be more effective than self-directed learning. 4 Other interventions include ECG workshops with the use of HEARTS approach, 7 “reverse classroom’’ method - an interaction-based learning 8 - which was found to be more effective than conventional lecture-based teaching and courses like Foundations of Emergency Medicine (FoEM) ECG 1 course, among many others. 6 Therefore, a combination of conventional and electronic methods is more effective than either one alone for improving ECG learning and competence among medical and postgraduate trainees. 9 Studies has shown a positive impact of medium- and long-term educational workshops on ECG knowledge and interpretation; however, more studies are needed to further understand the nature of this impact. 10 The primary aim of our study is to assess the effectiveness of a structured ECG workshop on improving the knowledge and self-reported confidence of ECG interpretation among medical students and early-career doctors in Nigeria. The findings from this study are intended to inform the development and standardization of effective, scalable ECG training workshops across Nigeria medical institutions, ultimately contributing to improved clinical competency and patient care. Methods Study setting and design The training workshop was conducted online to accommodate Nigerian medical students and early-career doctors across different states in Nigeria. The study was a one-group pretest-posttest survey using a quasi-experimental design to evaluate the impact of a structured ECG training workshop on ECG interpretation knowledge and self-reported confidence among medical students and early career doctors in Nigeria. Intervention Overview of IMGN The Internal Medicine Interest Group of Nigeria (IMGN) is a non-profit independent organization dedicated to promoting interest and excellence in Internal Medicine among medical students and early career medical doctors in Nigeria. Program description The IMGN ECG training workshop was a structured online webinar program open to all medical students and early-career doctors in Nigeria and Africa at large. The training sessions delivered in English covered the following key areas: Core ECG Foundations, Rhythm Disorders & Interpretation, Ischemia & Infarction Patterns, Electrolytes and Drugs, with the overall aim being to boost understanding and confidence in ECG interpretation and to ensure standard ECG application in clinical practices in Nigeria. The training was delivered over three weekends via the virtual conferencing platform “Zoom”. All participants registered for the study beforehand. All communications for the program were conducted via a dedicated WhatsApp group. Study population This study was carried out among undergraduate medical students, house officers and early-career medical doctors across Nigeria who registered for the IMGN ECG training workshop and gave informed consent to be included in the study. However, only participants who attended at least 75% of the training sessions were included in the study. Sample size was based on the number of eligible participants that enrolled, provided informed consent and attended at least 75% of the training sessions. No sample size calculation was required due to convenience-based recruitment. Data collection and management Data collection involved pre- and post-test structured questionnaires which were pretested and self-administered online using a web-based survey administration software, “KoboToolbox”. The questionnaire was developed based on the course outline of the training webinar, with consultation and approval from the cardiologists involved in the course. The link to the questionnaire was distributed to all participants via the dedicated WhatsApp group at two points: At baseline (pretest) which assessed demographics, knowledge, and confidence and immediately after the training workshop (post test) which assessed knowledge, confidence and satisfaction with the training. Each participant received a unique anonymized code to track their responses across both phases. The study instrument is included as supplementary material (Appendix 1). Statistical analysis Mean ± standard deviation (SD) was calculated for continuous variables. Frequencies and percentages were calculated for categorical variables. The normality of distribution for both the knowledge and confidence scores were assessed using the Shapiro-Wilk test. As they both violated the assumption of normality (Shapiro-Wilk p < .001), non-parametric statistical tests were employed for subsequent analyses. Comparisons were done using Wilcoxon signed rank test to investigate differences between pre-test and post-test scores. Intragroup comparison was done using Kruskal-Wallis and Mann-Whitney U tests. A Spearman correlation was used to investigate whether there were any correlations between the pre-test score and post-test score. A p-value < 0.05 was considered statistically significant. All statistical analyses were done using R version 4.5.2 (R Foundation for Statistical Computing, Vienna, Austria) and the IBM SPSS Statistics software Version 27 (Armonk, NY: IBM Corp). Ethical considerations The aim and objectives of the study were clearly communicated to the participants, and each participant was assured of the confidentiality of all information provided, voluntary participation, and the right to withdraw from the study at any point in time. Informed consent was obtained before participation. Ethical clearance for the study was obtained on the 12th of February, 2026 from Ambrose Alli University, Ekpoma Health Research Ethics Committee with assigned number 081/26. Results Sociodemographic Characteristics of Participants The study initially enrolled 424 participants who provided informed consent. Of these, 188 participants (44.3%) attended at least 75% of the training sessions and completed both the pretest and post-test assessments. This corresponds to an attrition rate of 55.7%. The mean age was 25 years (± 5 SD). There were slightly more males (52.7%) than females (47.3%). Most participants were medical students (81.4%), while the remainder comprised house officers (12.8%), medical officers (4.3%), and resident doctors (1.6%). Among medical students, the distribution across academic levels was as follows: 600 level (34.6%), 500 level (29.4%), 400 level (27.5%), and preclinical students (8.5%). Most participants (82.4%) reported no prior formal ECG training. Detailed sociodemographic data are presented in Table 1 . Table 1 Sociodemographic characteristics of participants (N = 188) Variable Frequency (N = 188) Percentage (%) Age (years) mean ± SD 25 ± 5 Gender Male 99 52.7% Female 89 47.3% Training Status Resident Doctor 3 1.6% Medical Officer 8 4.3% House Officer 24 12.8% Medical Student 153 81.4% Academic Level (Medical Students only) 600 level 53 34.6% 500 level 45 29.4% 400 level 42 27.5% Preclinical 13 8.5% Prior ECG Training No 155 82.4% Yes 33 17.6% Comparison between pre- and post-test results Knowledge Change The mean knowledge score increased from 42.6% in the pretest to 67.9% in the posttest, with a percentage improvement of 25.3%. The 95% confidence interval for the knowledge change was 3.05 to 4.03. The distribution of knowledge change scores was not normal (Shapiro-Wilk p < .001). Therefore, the Wilcoxon signed-rank test was used, revealing a statistically significant increase in knowledge scores ( Z = -10.376, p < .001) as shown in Table 3 . Confidence Change Participants’ self-reported confidence in ECG interpretation also improved significantly. The percentage confidence score increased from 39.2% to 72.6%, yielding a percentage increase of 33.5%, with a 95% CI of 15.33 to 18.15. The change in confidence scores was also not normally distributed (Shapiro-Wilk p < .001). As shown in Table 3 , the Wilcoxon signed-rank test confirmed a significant improvement ( Z = -11.420, p < .001). Table 2 presents the pre- and posttest knowledge and confidence score distributions. Table 2 Distribution of pre- and post-test scores Variable Percentage (%) Mean SD 95% CI Knowledge Pretest knowledge score 42.6% 5.97 3.364 5.48–6.45 Posttest knowledge score 67.9% 9.51 3.062 9.06–9.95 Knowledge improvement 25.3% 3.54 3.413 3.05–4.03 Confidence Pretest confidence score 39.2% 19.58 9.376 18.23–20.93 Posttest confidence score 72.6% 36.32 8.457 35.10-37.54 Confidence improvement 33.5% 16.74 9.796 15.33–18.15 Table 3 Comparison of pre- and post-test scores using Wilcoxon signed-rank test Variable Pretest Median (IQR) Posttest Median (IQR) Z value Effect size (r) p-value Knowledge score 6.00 (3–8) 10.00 (8–12) -10.376 0.76 < .001 Confidence score 18.00 (11–26) 38.00 (30–41) -11.420 0.83 < .001 Participants Satisfaction with the Training Program Overall, participants reported high satisfaction with the training. The mean satisfaction score across all items was 4.3 ± 0.8 on a 5-point Likert scale. Specifically, 90.4% agreed or strongly agreed that the course content was well-organized, 91.5% found the speakers to be knowledgeable and engaging, 77.1% felt the pacing and duration of the sessions were appropriate, 83.0% agreed the course met their expectations, and 85.1% indicated that they would recommend the course to others. Detailed data on the satisfaction survey are presented in Table 4 . Table 4 Participant satisfaction survey responses (N = 188) Variable Frequency (n) Percentage (%) Mean Satisfaction score ± SD 4.3 ± 0.8 The course content was well organized Disagree 18 9.6% Agree/Strongly agree 170 90.4% The speakers were knowledgeable and engaging Disagree 16 8.5% Agree 172 91.5% The pacing and duration of the sessions were appropriate Disagree 43 22.9% Agree 145 77.1% The course met my expectations Disagree 32 17.0% Agree 156 83.0% I would recommend this course to others Disagree 28 14.9% Agree 160 85.1% *Note: The likert responses were dichotomized, with scores 1–3 categorized as “Disagree” and scores 4–5 categorized as “Agree”. Association between Knowledge Improvement and Sociodemographic variables No significant difference in knowledge gain was found between male and female participants ( U = 3886.0, p = .161). Similarly, among medical students, improvement in knowledge did not differ significantly across academic levels ( H = 0.441, p = .932). A Kruskal-Wallis test was conducted to compare improvement in knowledge across different training statuses (resident doctor, medical officer, house officer, medical student). The result was statistically significant ( H = 10.643, p = .014), suggesting that the magnitude of knowledge improvement varied by professional category. Medical students showed the highest mean rank in knowledge improvement. Participants without prior ECG training (n = 155) had a higher median knowledge improvement (4 [IQR: 2–6]) compared to those with prior training (n = 33, 2 [IQR: 1–4]). The Mann-Whitney U test showed this difference was statistically significant ( U = 1962.5, p = .035), indicating that those without prior training benefited more from the intervention. More detailed findings are presented in Table 5 . Table 5 Association between knowledge improvement and Sociodemographic variables Variable Median knowledge improvement (IQR) Mean rank Test statistic p-value Gender Male 3(1–5) 89.25 Z = -1.401 .161 Female 4(2-6.25) 100.34 Training Status Resident Doctor 1(-) 50.00 H = 10.643 .014 Medical Officer 2.5(1.25–5.25) 88.44 House Officer 2(0–3) 65.65 Medical Student 4(1–6) 100.22 Academic Level (Medical Students only) 600 level 4(1–6) 74.70 H = .441 .932 500 level 4(2–6) 78.61 400 level 3.5(1.75-6) 79.43 Preclinical 3(1.5–5.5) 72.96 Prior ECG Training No 4(2–6) 98.34 Z = -2.106 .035 Yes 2(1–4) 76.47 Association between Confidence Improvement and Sociodemographic variables The change in confidence following the ECG training was also analyzed in relation to key demographic variables. No significant difference in confidence improvement was observed between male and female participants. The Mann-Whitney U test confirmed no statistically significant difference ( Z = -0.786, p = .432). Confidence improvement differed significantly across professional categories (Kruskal-Wallis H = 11.120, p = .011). House officers showed the highest median increase (17 [IQR: 14.25–20.75]), followed by medical students (17 [IQR: 10–24]), medical officers (8 [IQR: 4–19]), and resident doctors (4). The low sample size of resident doctors (n = 3) limits interpretation for this subgroup. Among medical students, confidence gain varied significantly by academic level ( H = 7.972, p = .047). Students in 400 level reported the largest median improvement (20 [IQR: 14–26)], followed by 500 level (17 [IQR: 10.5–25.5]), preclinical (15 [IQR: 8.5–22.5]), and 600 level (15 [IQR: 9-21.5]). Participants without prior ECG training experienced a significantly greater increase in confidence compared to those with prior training. The median confidence gain was 18 (IQR: 12–25) for the no-prior-training group versus 10 (IQR: 8.75-15) for the prior-training group. The Mann-Whitney U test confirmed this difference was statistically significant ( Z = -2.683, p = .007). More detailed findings are presented in Table 6 . Table 6 Association between Confidence improvement and Sociodemographic variables Variable Median knowledge improvement (IQR) Mean rank Test statistic p-value Gender Male 17(12–24) 91.55 Z = − .786 .432 Female 17(9.76-25) 97.79 Training Status Resident Doctor 4(-) 15.17 H = 11.120 .011 Medical Officer 8(4–19) 56.19 House Officer 17(14.25–20.75) 102.15 Medical Student 17(10–24) 96.86 Academic Level (Medical Students only) 600 level 15(9-21.5) 66.08 H = 7.972 .047 500 level 17(10.5–25.5) 79.59 400 level 20(14–26) 90.77 Preclinical 15(8.5–22.5) 68.04 Prior ECG Training No 18(12–25) 99.41 Z = -2.683 .007 Yes 10(8.75-15) 71.44 Correlation Between Knowledge and Confidence Scores Spearman’s rank correlation revealed moderate positive correlations between pretest knowledge and pretest confidence (ρ = 0.655, p < .001), and between posttest knowledge and posttest confidence (ρ = 0.511, p < .001). Pretest knowledge was also positively correlated with posttest knowledge (ρ = 0.427, p < .001), indicating that baseline knowledge was associated with post-intervention performance. Findings are presented in Fig. 1 . Discussion ECG interpretation is a core clinical competency expected of medical students and early-career doctors, particularly in resource-limited settings such as Nigeria, where access to specialist cardiology services is often limited. Therefore, evaluating the effectiveness of structured ECG training among Nigerian medical trainees is essential for strengthening clinical competence, improving early detection and management of cardiovascular conditions, and ultimately enhancing patient outcomes within the healthcare system. Our study found that more than four-fifths (82.4%) of participants reported having no prior formal ECG training, indicating a substantial gap in ECG education within Nigerian undergraduate medical curricula and early postgraduate training programs. This finding is consistent with previous studies that have reported deficiencies in structured ECG training among medical trainees, underscoring the persistent need for targeted educational interventions within medical education programs. 4,9,11 This gap may reflect limited curricular time allocation for ECG interpretation and insufficient opportunities for supervised, hands-on ECG practice. 12 Our study demonstrated suboptimal baseline levels of knowledge and confidence in ECG interpretation, with only 42.7% of participants demonstrating good knowledge and 39.2% reporting a good level of confidence at the pre-intervention assessment. These findings are similar to previous reports by Kashou et al. 13 and Kopec et al. 14 , who observed good ECG knowledge in 56.4% and 66% of participants, respectively, indicating that baseline ECG competence remains suboptimal across different training contexts. This gap may be attributable to inadequate formal ECG instruction in undergraduate curricula, inconsistent opportunities for hands-on interpretation during clinical rotations, and overreliance on self-directed learning, which together may be insufficient to foster competence and confidence. 9 Our study observed a substantial improvement in ECG knowledge, with a 25.3% increase from pretest to posttest scores and a large effect size (r = 0.76), consistent with existing evidence that focused ECG training programs significantly enhance learners’ interpretative abilities. 9.15 The observed improvement is consistent with results from blended and workshop-based ECG interventions, highlighting the importance of structured training programs that prioritize pattern recognition, repeated practice, and application to clinically relevant scenarios. 2 The participants’ self-reported confidence in ECG interpretation also increased substantially, with a 33.5% improvement and a very large effect size (r = 0.83). Confidence is a critical component of clinical competence, particularly in ECG interpretation, where hesitation or misinterpretation can delay diagnosis and management. 16,17 The marked improvement in confidence is consistent with previous studies showing that educational interventions combining didactic instruction with interactive, hands-on elements significantly enhance learners’ self-efficacy. 4,15 Our study found that medical students demonstrated the greatest improvement in ECG knowledge compared to house officers and resident doctors, which may reflect larger baseline knowledge gaps among students and consequently more room for improvement. This interpretation is supported by the significantly higher gains observed among participants without prior ECG training. Similar trends have been reported in previous studies, where novice learners derived the most benefit from structured ECG instruction. 2,8 These findings highlight the importance of introducing formal ECG training early in medical education to address foundational knowledge gaps before clinical practice. Conversely, the smaller gains observed among resident doctors should be interpreted cautiously, given the very small sample size in this subgroup. The magnitude of confidence improvement differed across professional categories and academic levels. House officers and medical students experienced the largest gains, likely due to greater baseline knowledge deficits and limited prior formal ECG instruction. 15,16 The findings underscore the importance of implementing structured ECG instruction during pivotal phases of training, such as clinical clerkships and early postgraduate practice, to maximize educational impact. These findings suggest that ECG workshops may be most beneficial when integrated during critical learning stages, such as clinical clerkships or the early postgraduate period, to maximize skill development and build confidence in ECG interpretation. 4,11 This study has several limitations that should be considered when interpreting the findings. First, the use of a one-group pretest–posttest quasi-experimental design without a control group limits internal validity and precludes definitive causal inference regarding the observed improvements. Second, the relatively high attrition rate may have introduced selection bias, as participants who completed at least 75% of the training and both assessments were likely more motivated or academically engaged than those who withdrew. Despite these limitations, the findings provide important preliminary evidence that structured ECG training can enhance ECG knowledge and perceived competence among medical trainees in Nigeria, underscoring its potential value in resource-limited healthcare settings. Conclusions This study highlighted that structured online ECG training significantly improved both ECG interpretation knowledge and self-reported confidence among medical students and early-career doctors in Nigeria. Participants showed substantial gains in post-intervention knowledge and confidence scores, highlighting the effectiveness of focused, interactive ECG education delivered via a virtual platform. The greatest improvements were observed among medical students and participants without prior ECG training, underscoring the value of early and structured exposure to ECG interpretation during medical training. Given the high prevalence of inadequate baseline ECG training and suboptimal competence in this cohort, these findings reveal a critical gap in undergraduate and early postgraduate medical education in Nigeria. Structured ECG workshops, particularly those incorporating interactive, clinically oriented teaching methods, represent a feasible and scalable approach to enhancing ECG competence in resource-limited settings. By addressing these educational gaps, it is possible to equip medical trainees with the knowledge and confidence necessary to make informed clinical decisions on ECG interpretation, ultimately contributing to a more effective and equitable healthcare system in Nigeria. Abbreviations CAD Coronary artery disease CI Confidence interval CVDs Cardiovascular disease(s) ECG Electrocardiogram FoEM Foundations of Emergency Medicine FTP Focused teaching program HEARTS Heart rate/rhythm, Electrical conduction, Axis, R-wave progression, Tall/small voltages, and ST/T changes IMGN Internal Medicine Interest Group of Nigeria IQR Interquartile range SD Standard deviation SDL Self-directed learning SPSS Statistical Package for the Social Sciences Declarations Ethics approval and consent to participate Ethical clearance for this study was obtained from the Ambrose Alli University, Ekpoma Health Research Ethics Committee on the 12th of February, 2026 (reference number: 081/26). The study was conducted in accordance with the principles of the declaration of Helsinki. All participants provided informed consent before participation. The aim and objectives of the study were clearly communicated, and participants were assured of confidentiality, voluntary participation, and the right to withdraw at any point. Consent for publication Not applicable. Availability of data and materials All data obtained and analyzed are available in the manuscript. The main dataset generated during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions AY, HO, and OSC conceptualized and designed the study. OSC, OV, and ODU planned and executed the intervention. AY, ON, OO, IE-H, HO, ABO, OSC, OV, and ODU contributed to data acquisition. AY conducted data analysis. AY, ON, and OO drafted the manuscript. ABO and OSC critically revised the manuscript for important intellectual content. All authors read and approved of the final manuscript. Acknowledgements The authors would like to thank all the medical students and doctors who participated in this study. We also acknowledge the Internal Medicine Interest Group of Nigeria (IMGN) for organizing the training workshop and providing the platform for this research. Additionally, we thank the facilitators and speakers who contributed to the success of the ECG training program including Dr. Bunmi Ajala (Consultant Cardiologist, University of Port-harcourt Teaching Hospital), Dr. Francisca Olufunmilayo Inofomah (Consultant Cardiologist, Olabisi Onabanjo University Teaching Hospital), Dr. Bernard Akpu (Consultant Cardiologist, University of Calabar Teaching Hospital) and Dr. Fahad Lawal (Senior Registrar, Usmanu Danfodiyo University Teaching Hospital). References Alikor CA, Onuwaje P. Competency in electrocardiogram interpretation and placement of ECG leads among final year medical students of a Nigerian university. Gaz Med. 2018;6(2):650–60. Viljoen CA, Millar RS, Manning K, Burch VC. Effectiveness of blended learning versus lectures alone on ECG analysis and interpretation by medical students. BMC Med Educ. 2020;20:488. doi: 10.1186/s12909-020-02403-y. Sibbald M, Davies EG, Dorian P, Yu EH. Electrocardiographic interpretation skills of cardiology residents: are they competent? Can J Cardiol. 2014;30(12):1721–4. McAloon C, Leach H, Gill S, Aluwalia A, Trevelyan J. Improving ECG competence in medical trainees in a UK district general hospital. Cardiol Res. 2014;5(2):51–7. doi: 10.14740/cr333e. Isiguzo GC, Iroezindu MO, Muoneme AS, Okeahialam BN. Knowledge and utilisation of electrocardiogram among resident doctors in family medicine in Nigeria. Niger J Clin Pract. 2017;20(9):1133–8. doi: 10.4103/njcp.njcp_374_16. Lindsey S, Moran TP, Stauch MA, Lynch AL, Grabow Moore K. Bridging the gap: evaluation of an electrocardiogram curriculum for advanced practice clinicians. West J Emerg Med. 2024;25(2):155–9. doi: 10.5811/westjem.18085. El-Baba M, McLaren J, Argintaru N. The HEARTS ECG workshop: a novel approach to resident and student ECG education. Int J Emerg Med. 2023;16(1):8. doi: 10.1186/s12245-023-00559-0. Chaumont C, Morgat C, Ollitrault P, Brejoux C, Extramiana F, Milliez P, et al. How to improve medical students' ECG interpretation skills? Multicenter survey and results of a comparative study evaluating a new educational approach. BMC Med Educ. 2024;24(1):979. doi: 10.1186/s12909-024-05929-7. Adebayo OM, Anele FC, Afolabi TK, Inofomoh FO, Ajibare AO, Aje A. Improving ECG learning and competence among medical and postgraduate trainees: scoping review of available evidence. Ann Ib Postgrad Med. 2024;22(2):95–105. PMID: 11848365. Balhi S, Baati R, Mrabet MK, Mekki L, Ben Mansour A, Mrabet A. Effectiveness of ECG educational workshops among undergraduate medical students. Tunis Med. 2020;98(11):783–8. PMID: 33479976. Lever NA, Larsen PD, Dawes M, Wong A, Harding SA. Are our medical graduates in New Zealand safe and accurate in ECG interpretation? N Z Med J. 2009;122(1293):9–15. AlShakhs AA, AlAmeer AA, AlEsmaeel ZC, AlMotawa O, AlHaddad SM, AlHaddad HM, et al. Electrocardiogram interpretation competency of primary health care physicians: a cross-sectional study. Healthcare (Basel). 2025;13(23):3040. doi: 10.3390/healthcare13233040. Kashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Cullen MW, Angstman KB, et al. ECG interpretation proficiency of healthcare professionals. Curr Probl Cardiol. 2023;48(3):101924. doi: 10.1016/j.cpcardiol.2023.101924. Kopeć G, Magoń W, Hołda M, Podolec P. Competency in ECG interpretation among medical students. Med Sci Monit. 2015;21:3386–94. doi: 10.12659/MSM.895129. Pollock A, O’Sullivan J, Traynor B, McAloon C. The impact of two hours of ECG teaching on knowledge and confidence. Authorea. 2023. https://www.authorea.com/users/734834/articles/712605-the-impact-of-two-hours-of-ecg-teaching-on-knowledge-and-confidence. Accessed 25 Dec 2025. Kim M, Yoo J. Factors influencing self-confidence and educational needs in electrocardiographic monitoring among emergency room and intensive care unit nurses. Healthcare (Basel). 2025;13(3):277. doi: 10.3390/healthcare13030277. Vishnevsky G, Cohen T, Elitzur Y, Gandelman G, Golovchiner G, Strasberg B, et al. Competency and confidence in ECG interpretation among medical students. Int J Med Educ. 2022;13:315–21. doi: 10.5116/ijme.6382.9e71. Additional Declarations No competing interests reported. Supplementary Files ECGSTUDYQUESTIONNAIRE.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Editor invited by journal 28 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9204737","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631376416,"identity":"56620be9-187d-4683-887c-dd39ad7e1e67","order_by":0,"name":"Harry Okwilagwe","email":"","orcid":"","institution":"Ambrose Alli University","correspondingAuthor":false,"prefix":"","firstName":"Harry","middleName":"","lastName":"Okwilagwe","suffix":""},{"id":631376417,"identity":"252027b9-1bb3-47da-b198-aac588052c32","order_by":1,"name":"Amir Yahaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYFACHgjFxsB84MAHEIOdeC1siQdngBjMxGoBMowPg9mEtBgcP3vwM0+NXTSf9BmDwza/tsnzMTMwfviYg0fLmbxkaZ5jybltfGkFh3P7bhu2MTMwS87chkfLgRwDaR425tw2HuYNh3N7bjMCtbAx8+LTcv6N8W+ef/VALQwGhy17btsT1nIjx0yat+0wUAuLwWGGH7cTCWqRvPHGzHJu33GgFraEg70Nt5PbmBmb8fqF73yO8Y0336pz5/cwH/7w489t2/ntzQc/fMSjReEAAwMTPGoY28BkA271QCAPlGb8Aef+wat4FIyCUTAKRigAAPAnU5/oNbnVAAAAAElFTkSuQmCC","orcid":"","institution":"Usmanu Danfodiyo University","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"","lastName":"Yahaya","suffix":""},{"id":631376418,"identity":"cdbcc962-bacc-47cd-b892-bef064cfdad3","order_by":2,"name":"Olumide Noah","email":"","orcid":"","institution":"University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"Olumide","middleName":"","lastName":"Noah","suffix":""},{"id":631376419,"identity":"70114965-bb6d-40f3-9542-f06c80134c8b","order_by":3,"name":"Okwukweamaka Okoli","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Okwukweamaka","middleName":"","lastName":"Okoli","suffix":""},{"id":631376420,"identity":"62a72177-9bc4-4790-a984-7dcbbcc60928","order_by":4,"name":"Iyoha Ehi-Iyoha","email":"","orcid":"","institution":"Ambrose Alli University","correspondingAuthor":false,"prefix":"","firstName":"Iyoha","middleName":"","lastName":"Ehi-Iyoha","suffix":""},{"id":631376421,"identity":"d895627a-8c0f-460d-8a09-fbe96bc4ce33","order_by":5,"name":"Alabi Badrudeen Olalekan","email":"","orcid":"","institution":"University of Ilorin","correspondingAuthor":false,"prefix":"","firstName":"Alabi","middleName":"Badrudeen","lastName":"Olalekan","suffix":""},{"id":631376422,"identity":"34c479aa-5a19-4b6e-af7b-462a1dfd530e","order_by":6,"name":"Okafor Stanley Chukwudi","email":"","orcid":"","institution":"University of Port Harcourt Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Okafor","middleName":"Stanley","lastName":"Chukwudi","suffix":""},{"id":631376423,"identity":"bfa4a558-b2ac-4cc0-8883-7be07cb5365d","order_by":7,"name":"Odia Victor","email":"","orcid":"","institution":"University of Port Harcourt Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Odia","middleName":"","lastName":"Victor","suffix":""},{"id":631376424,"identity":"773da960-6cb4-4879-b7c7-294ff3efa811","order_by":8,"name":"Okechukwu Duke Uchenna","email":"","orcid":"","institution":"University of Calabar","correspondingAuthor":false,"prefix":"","firstName":"Okechukwu","middleName":"Duke","lastName":"Uchenna","suffix":""}],"badges":[],"createdAt":"2026-03-23 22:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9204737/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9204737/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108976405,"identity":"29da4e90-4960-45be-ab34-b47a9591183c","added_by":"auto","created_at":"2026-05-11 11:15:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":319464,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between Knowledge and Confidence scores\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9204737/v1/2141cf2897020465441c15db.png"},{"id":109204668,"identity":"d8176dd9-3004-4833-936f-29c35c024c59","added_by":"auto","created_at":"2026-05-13 15:01:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":605477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9204737/v1/ccf51a6e-a2e7-4048-8c87-82cc898ecf30.pdf"},{"id":108204483,"identity":"f7f87c3f-ff4a-40b3-b977-03d9728e16f2","added_by":"auto","created_at":"2026-04-30 12:37:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1000611,"visible":true,"origin":"","legend":"","description":"","filename":"ECGSTUDYQUESTIONNAIRE.docx","url":"https://assets-eu.researchsquare.com/files/rs-9204737/v1/da7f7e0c8be119106bba55fc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness of a structured ECG training program among medical students and early-career doctors in Nigeria: A pre-post quasi-experimental study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe electrocardiogram (ECG) is a vital non-invasive diagnostic tool in clinical medicine, essential for evaluating cardiac rhythm disturbances, myocardial ischemia, and structural abnormalities. The increasing prevalence of cardiovascular disease (CVDs) and the need for accurate diagnosis and treatment has highlighted the significance of understanding ECG knowledge and application within the medical community. ECG is essential in the diagnosis of CVDs such as coronary artery disease (CAD), arrhythmias, inflammatory heart diseases and certain electrolyte imbalances. Proper ECG interpretation is vital in the management of cardiac diseases and emergencies.\u003csup\u003e1\u003c/sup\u003e ECG misinterpretation can result in erroneous clinical decisions with harmful outcomes.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite the widespread clinical use of ECG, several studies have shown that performance in ECG interpretation is poor among both undergraduate and postgraduate students of medicine, cardiologists included.\u003csup\u003e3\u003c/sup\u003e Furthermore, studies showed that the majority of medical students felt a low level of self-confidence regarding their competence in ECG interpretation.\u003csup\u003e4\u003c/sup\u003e In low-resource settings like Nigeria, additional barriers include a lack of up-to-date knowledge about ECGs and infrequent use of this skill in practice.\u003csup\u003e5\u003c/sup\u003e Hence, this necessitates an effective way to improve the knowledge of ECG interpretation among medical students and doctors.\u003c/p\u003e \u003cp\u003eIn response to this issue, a variety of learning and teaching methods have been developed to enhance ECG competence, including courses and workshops. Such modalities recorded significant improvements in the knowledge and confidence of ECG interpretations of the participants, especially when compared with classroom lectures.\u003csup\u003e6\u003c/sup\u003e Blended learning, which combines face-to-face lectures with e-learning, has proven effective in increasing confidence in ECG analysis and interpretation, as well as enhancing diagnostic accuracy.\u003csup\u003e2\u003c/sup\u003e E-learning provides a convenient and asynchronous learning experience and facilitates immediate online feedback. Self-directed learning (SDL) is beneficial in that it allows for repetitive practice and focused revision of learning material, thereby increasing the time spent on learning.\u003csup\u003e2\u003c/sup\u003e Focused teaching program (FTP) was found to be more effective than self-directed learning.\u003csup\u003e4\u003c/sup\u003e Other interventions include ECG workshops with the use of HEARTS approach,\u003csup\u003e7\u003c/sup\u003e \u0026ldquo;reverse classroom\u0026rsquo;\u0026rsquo; method - an interaction-based learning\u003csup\u003e8\u003c/sup\u003e - which was found to be more effective than conventional lecture-based teaching and courses like Foundations of Emergency Medicine (FoEM) ECG 1 course, among many others.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTherefore, a combination of conventional and electronic methods is more effective than either one alone for improving ECG learning and competence among medical and postgraduate trainees.\u003csup\u003e9\u003c/sup\u003e Studies has shown a positive impact of medium- and long-term educational workshops on ECG knowledge and interpretation; however, more studies are needed to further understand the nature of this impact.\u003csup\u003e10\u003c/sup\u003e The primary aim of our study is to assess the effectiveness of a structured ECG workshop on improving the knowledge and self-reported confidence of ECG interpretation among medical students and early-career doctors in Nigeria. The findings from this study are intended to inform the development and standardization of effective, scalable ECG training workshops across Nigeria medical institutions, ultimately contributing to improved clinical competency and patient care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and design\u003c/h2\u003e \u003cp\u003eThe training workshop was conducted online to accommodate Nigerian medical students and early-career doctors across different states in Nigeria. The study was a one-group pretest-posttest survey using a quasi-experimental design to evaluate the impact of a structured ECG training workshop on ECG interpretation knowledge and self-reported confidence among medical students and early career doctors in Nigeria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIntervention\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOverview of IMGN\u003c/h2\u003e \u003cp\u003e The Internal Medicine Interest Group of Nigeria (IMGN) is a non-profit independent organization dedicated to promoting interest and excellence in Internal Medicine among medical students and early career medical doctors in Nigeria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProgram description\u003c/h3\u003e\n\u003cp\u003eThe IMGN ECG training workshop was a structured online webinar program open to all medical students and early-career doctors in Nigeria and Africa at large. The training sessions delivered in English covered the following key areas: Core ECG Foundations, Rhythm Disorders \u0026amp; Interpretation, Ischemia \u0026amp; Infarction Patterns, Electrolytes and Drugs, with the overall aim being to boost understanding and confidence in ECG interpretation and to ensure standard ECG application in clinical practices in Nigeria. The training was delivered over three weekends via the virtual conferencing platform \u0026ldquo;Zoom\u0026rdquo;. All participants registered for the study beforehand. All communications for the program were conducted via a dedicated WhatsApp group.\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003e This study was carried out among undergraduate medical students, house officers and early-career medical doctors across Nigeria who registered for the IMGN ECG training workshop and gave informed consent to be included in the study. However, only participants who attended at least 75% of the training sessions were included in the study. Sample size was based on the number of eligible participants that enrolled, provided informed consent and attended at least 75% of the training sessions. No sample size calculation was required due to convenience-based recruitment.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection and management\u003c/h2\u003e \u003cp\u003eData collection involved pre- and post-test structured questionnaires which were pretested and self-administered online using a web-based survey administration software, \u0026ldquo;KoboToolbox\u0026rdquo;. The questionnaire was developed based on the course outline of the training webinar, with consultation and approval from the cardiologists involved in the course. The link to the questionnaire was distributed to all participants via the dedicated WhatsApp group at two points: At baseline (pretest) which assessed demographics, knowledge, and confidence and immediately after the training workshop (post test) which assessed knowledge, confidence and satisfaction with the training. Each participant received a unique anonymized code to track their responses across both phases. The study instrument is included as supplementary material (Appendix 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) was calculated for continuous variables. Frequencies and percentages were calculated for categorical variables. The normality of distribution for both the knowledge and confidence scores were assessed using the Shapiro-Wilk test. As they both violated the assumption of normality (Shapiro-Wilk p \u0026lt; .001), non-parametric statistical tests were employed for subsequent analyses. Comparisons were done using Wilcoxon signed rank test to investigate differences between pre-test and post-test scores. Intragroup comparison was done using Kruskal-Wallis and Mann-Whitney U tests. A Spearman correlation was used to investigate whether there were any correlations between the pre-test score and post-test score. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were done using R version 4.5.2 (R Foundation for Statistical Computing, Vienna, Austria) and the IBM SPSS Statistics software Version 27 (Armonk, NY: IBM Corp).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThe aim and objectives of the study were clearly communicated to the participants, and each participant was assured of the confidentiality of all information provided, voluntary participation, and the right to withdraw from the study at any point in time. Informed consent was obtained before participation. Ethical clearance for the study was obtained on the 12th of February, 2026 from Ambrose Alli University, Ekpoma Health Research Ethics Committee with assigned number 081/26.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic Characteristics of Participants\u003c/h2\u003e \u003cp\u003e The study initially enrolled 424 participants who provided informed consent. Of these, 188 participants (44.3%) attended at least 75% of the training sessions and completed both the pretest and post-test assessments. This corresponds to an attrition rate of 55.7%. The mean age was 25 years (\u0026plusmn;\u0026thinsp;5 SD). There were slightly more males (52.7%) than females (47.3%). Most participants were medical students (81.4%), while the remainder comprised house officers (12.8%), medical officers (4.3%), and resident doctors (1.6%). Among medical students, the distribution across academic levels was as follows: 600 level (34.6%), 500 level (29.4%), 400 level (27.5%), and preclinical students (8.5%). Most participants (82.4%) reported no prior formal ECG training. Detailed sociodemographic data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of participants (N\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (N\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTraining Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResident Doctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical Officer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHouse Officer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical Student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAcademic Level (Medical Students only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrior ECG Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison between pre- and post-test results\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eKnowledge Change\u003c/h2\u003e \u003cp\u003eThe mean knowledge score increased from 42.6% in the pretest to 67.9% in the posttest, with a percentage improvement of 25.3%. The 95% confidence interval for the knowledge change was 3.05 to 4.03. The distribution of knowledge change scores was not normal (Shapiro-Wilk p \u0026lt; .001). Therefore, the Wilcoxon signed-rank test was used, revealing a statistically significant increase in knowledge scores (\u003cem\u003eZ\u003c/em\u003e = -10.376, p \u0026lt; .001) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConfidence Change\u003c/h2\u003e \u003cp\u003eParticipants\u0026rsquo; self-reported confidence in ECG interpretation also improved significantly. The percentage confidence score increased from 39.2% to 72.6%, yielding a percentage increase of 33.5%, with a 95% CI of 15.33 to 18.15. The change in confidence scores was also not normally distributed (Shapiro-Wilk p \u0026lt; .001). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the Wilcoxon signed-rank test confirmed a significant improvement (\u003cem\u003eZ\u003c/em\u003e = -11.420, p \u0026lt; .001). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the pre- and posttest knowledge and confidence score distributions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of pre- and post-test scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest knowledge score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.48\u0026ndash;6.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest knowledge score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.06\u0026ndash;9.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.05\u0026ndash;4.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eConfidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest confidence score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.23\u0026ndash;20.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest confidence score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.10-37.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfidence improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.33\u0026ndash;18.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of pre- and post-test scores using Wilcoxon signed-rank test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosttest Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect size (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00 (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.00 (11\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.00 (30\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eParticipants Satisfaction with the Training Program\u003c/h2\u003e \u003cp\u003eOverall, participants reported high satisfaction with the training. The mean satisfaction score across all items was 4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 on a 5-point Likert scale. Specifically, 90.4% agreed or strongly agreed that the course content was well-organized, 91.5% found the speakers to be knowledgeable and engaging, 77.1% felt the pacing and duration of the sessions were appropriate, 83.0% agreed the course met their expectations, and 85.1% indicated that they would recommend the course to others. Detailed data on the satisfaction survey are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant satisfaction survey responses (N\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean Satisfaction score\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe course content was well organized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree/Strongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe speakers were knowledgeable and engaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe pacing and duration of the sessions were appropriate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe course met my expectations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI would recommend this course to others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Note: The likert responses were dichotomized, with scores 1\u0026ndash;3 categorized as \u0026ldquo;Disagree\u0026rdquo; and scores 4\u0026ndash;5 categorized as \u0026ldquo;Agree\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Knowledge Improvement and Sociodemographic variables\u003c/h2\u003e \u003cp\u003eNo significant difference in knowledge gain was found between male and female participants (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3886.0, p = .161). Similarly, among medical students, improvement in knowledge did not differ significantly across academic levels (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.441, p = .932).\u003c/p\u003e \u003cp\u003eA Kruskal-Wallis test was conducted to compare improvement in knowledge across different training statuses (resident doctor, medical officer, house officer, medical student). The result was statistically significant (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.643, p = .014), suggesting that the magnitude of knowledge improvement varied by professional category. Medical students showed the highest mean rank in knowledge improvement.\u003c/p\u003e \u003cp\u003eParticipants without prior ECG training (n\u0026thinsp;=\u0026thinsp;155) had a higher median knowledge improvement (4 [IQR: 2\u0026ndash;6]) compared to those with prior training (n\u0026thinsp;=\u0026thinsp;33, 2 [IQR: 1\u0026ndash;4]). The Mann-Whitney U test showed this difference was statistically significant (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1962.5, p = .035), indicating that those without prior training benefited more from the intervention. More detailed findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between knowledge improvement and Sociodemographic variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian knowledge improvement (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ = -1.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2-6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTraining Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResident Doctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;10.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical Officer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5(1.25\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHouse Officer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical Student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(1\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAcademic Level\u003c/p\u003e \u003cp\u003e(Medical Students only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(1\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eH = .441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5(1.75-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.5\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrior ECG Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ = -2.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Confidence Improvement and Sociodemographic variables\u003c/h2\u003e \u003cp\u003eThe change in confidence following the ECG training was also analyzed in relation to key demographic variables. No significant difference in confidence improvement was observed between male and female participants. The Mann-Whitney U test confirmed no statistically significant difference (\u003cem\u003eZ\u003c/em\u003e = -0.786, p = .432).\u003c/p\u003e \u003cp\u003eConfidence improvement differed significantly across professional categories (Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.120, p = .011). House officers showed the highest median increase (17 [IQR: 14.25\u0026ndash;20.75]), followed by medical students (17 [IQR: 10\u0026ndash;24]), medical officers (8 [IQR: 4\u0026ndash;19]), and resident doctors (4). The low sample size of resident doctors (n\u0026thinsp;=\u0026thinsp;3) limits interpretation for this subgroup.\u003c/p\u003e \u003cp\u003eAmong medical students, confidence gain varied significantly by academic level (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.972, p = .047). Students in 400 level reported the largest median improvement (20 [IQR: 14\u0026ndash;26)], followed by 500 level (17 [IQR: 10.5\u0026ndash;25.5]), preclinical (15 [IQR: 8.5\u0026ndash;22.5]), and 600 level (15 [IQR: 9-21.5]).\u003c/p\u003e \u003cp\u003eParticipants without prior ECG training experienced a significantly greater increase in confidence compared to those with prior training. The median confidence gain was 18 (IQR: 12\u0026ndash;25) for the no-prior-training group versus 10 (IQR: 8.75-15) for the prior-training group. The Mann-Whitney U test confirmed this difference was statistically significant (\u003cem\u003eZ\u003c/em\u003e = -2.683, p = .007). More detailed findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Confidence improvement and Sociodemographic variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian knowledge improvement (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(12\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(9.76-25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTraining Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResident Doctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;11.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical Officer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(4\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHouse Officer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(14.25\u0026ndash;20.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical Student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(10\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAcademic Level (Medical Students only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(9-21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;7.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(10.5\u0026ndash;25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(14\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(8.5\u0026ndash;22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrior ECG Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(12\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ = -2.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(8.75-15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Between Knowledge and Confidence Scores\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s rank correlation revealed moderate positive correlations between pretest knowledge and pretest confidence (ρ\u0026thinsp;=\u0026thinsp;0.655, p \u0026lt; .001), and between posttest knowledge and posttest confidence (ρ\u0026thinsp;=\u0026thinsp;0.511, p \u0026lt; .001). Pretest knowledge was also positively correlated with posttest knowledge (ρ\u0026thinsp;=\u0026thinsp;0.427, p \u0026lt; .001), indicating that baseline knowledge was associated with post-intervention performance. Findings are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eECG interpretation is a core clinical competency expected of medical students and early-career doctors, particularly in resource-limited settings such as Nigeria, where access to specialist cardiology services is often limited. Therefore, evaluating the effectiveness of structured ECG training among Nigerian medical trainees is essential for strengthening clinical competence, improving early detection and management of cardiovascular conditions, and ultimately enhancing patient outcomes within the healthcare system.\u003c/p\u003e \u003cp\u003eOur study found that more than four-fifths (82.4%) of participants reported having no prior formal ECG training, indicating a substantial gap in ECG education within Nigerian undergraduate medical curricula and early postgraduate training programs. This finding is consistent with previous studies that have reported deficiencies in structured ECG training among medical trainees, underscoring the persistent need for targeted educational interventions within medical education programs.\u003csup\u003e4,9,11\u003c/sup\u003e This gap may reflect limited curricular time allocation for ECG interpretation and insufficient opportunities for supervised, hands-on ECG practice.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study demonstrated suboptimal baseline levels of knowledge and confidence in ECG interpretation, with only 42.7% of participants demonstrating good knowledge and 39.2% reporting a good level of confidence at the pre-intervention assessment. These findings are similar to previous reports by Kashou et al.\u003csup\u003e13\u003c/sup\u003e and Kopec et al.\u003csup\u003e14\u003c/sup\u003e, who observed good ECG knowledge in 56.4% and 66% of participants, respectively, indicating that baseline ECG competence remains suboptimal across different training contexts. This gap may be attributable to inadequate formal ECG instruction in undergraduate curricula, inconsistent opportunities for hands-on interpretation during clinical rotations, and overreliance on self-directed learning, which together may be insufficient to foster competence and confidence.\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study observed a substantial improvement in ECG knowledge, with a 25.3% increase from pretest to posttest scores and a large effect size (r\u0026thinsp;=\u0026thinsp;0.76), consistent with existing evidence that focused ECG training programs significantly enhance learners\u0026rsquo; interpretative abilities.\u003csup\u003e9.15\u003c/sup\u003e The observed improvement is consistent with results from blended and workshop-based ECG interventions, highlighting the importance of structured training programs that prioritize pattern recognition, repeated practice, and application to clinically relevant scenarios.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe participants\u0026rsquo; self-reported confidence in ECG interpretation also increased substantially, with a 33.5% improvement and a very large effect size (r\u0026thinsp;=\u0026thinsp;0.83). Confidence is a critical component of clinical competence, particularly in ECG interpretation, where hesitation or misinterpretation can delay diagnosis and management.\u003csup\u003e16,17\u003c/sup\u003e The marked improvement in confidence is consistent with previous studies showing that educational interventions combining didactic instruction with interactive, hands-on elements significantly enhance learners\u0026rsquo; self-efficacy.\u003csup\u003e4,15\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study found that medical students demonstrated the greatest improvement in ECG knowledge compared to house officers and resident doctors, which may reflect larger baseline knowledge gaps among students and consequently more room for improvement. This interpretation is supported by the significantly higher gains observed among participants without prior ECG training. Similar trends have been reported in previous studies, where novice learners derived the most benefit from structured ECG instruction.\u003csup\u003e2,8\u003c/sup\u003e These findings highlight the importance of introducing formal ECG training early in medical education to address foundational knowledge gaps before clinical practice. Conversely, the smaller gains observed among resident doctors should be interpreted cautiously, given the very small sample size in this subgroup.\u003c/p\u003e \u003cp\u003eThe magnitude of confidence improvement differed across professional categories and academic levels. House officers and medical students experienced the largest gains, likely due to greater baseline knowledge deficits and limited prior formal ECG instruction.\u003csup\u003e15,16\u003c/sup\u003e The findings underscore the importance of implementing structured ECG instruction during pivotal phases of training, such as clinical clerkships and early postgraduate practice, to maximize educational impact. These findings suggest that ECG workshops may be most beneficial when integrated during critical learning stages, such as clinical clerkships or the early postgraduate period, to maximize skill development and build confidence in ECG interpretation.\u003csup\u003e4,11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, the use of a one-group pretest\u0026ndash;posttest quasi-experimental design without a control group limits internal validity and precludes definitive causal inference regarding the observed improvements. Second, the relatively high attrition rate may have introduced selection bias, as participants who completed at least 75% of the training and both assessments were likely more motivated or academically engaged than those who withdrew. Despite these limitations, the findings provide important preliminary evidence that structured ECG training can enhance ECG knowledge and perceived competence among medical trainees in Nigeria, underscoring its potential value in resource-limited healthcare settings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlighted that structured online ECG training significantly improved both ECG interpretation knowledge and self-reported confidence among medical students and early-career doctors in Nigeria. Participants showed substantial gains in post-intervention knowledge and confidence scores, highlighting the effectiveness of focused, interactive ECG education delivered via a virtual platform. The greatest improvements were observed among medical students and participants without prior ECG training, underscoring the value of early and structured exposure to ECG interpretation during medical training.\u003c/p\u003e \u003cp\u003eGiven the high prevalence of inadequate baseline ECG training and suboptimal competence in this cohort, these findings reveal a critical gap in undergraduate and early postgraduate medical education in Nigeria. Structured ECG workshops, particularly those incorporating interactive, clinically oriented teaching methods, represent a feasible and scalable approach to enhancing ECG competence in resource-limited settings.\u003c/p\u003e \u003cp\u003eBy addressing these educational gaps, it is possible to equip medical trainees with the knowledge and confidence necessary to make informed clinical decisions on ECG interpretation, ultimately contributing to a more effective and equitable healthcare system in Nigeria.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular disease(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectrocardiogram\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFoEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFoundations of Emergency Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFocused teaching program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHEARTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart rate/rhythm, Electrical conduction, Axis, R-wave progression, Tall/small voltages, and ST/T changes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMGN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternal Medicine Interest Group of Nigeria\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-directed learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e Ethical clearance for this study was obtained from the Ambrose Alli University, Ekpoma Health Research Ethics Committee on the 12th of February, 2026 (reference number: 081/26). The study was conducted in accordance with the principles of the declaration of Helsinki. All participants provided informed consent before participation. The aim and objectives of the study were clearly communicated, and participants were assured of confidentiality, voluntary participation, and the right to withdraw at any point.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e Not applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e All data obtained and analyzed are available in the manuscript. The main dataset generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e AY, HO, and OSC conceptualized and designed the study. OSC, OV, and ODU planned and executed the intervention. AY, ON, OO, IE-H, HO, ABO, OSC, OV, and ODU contributed to data acquisition. AY conducted data analysis. AY, ON, and OO drafted the manuscript. ABO and OSC critically revised the manuscript for important intellectual content. All authors read and approved of the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e The authors would like to thank all the medical students and doctors who participated in this study. We also acknowledge the Internal Medicine Interest Group of Nigeria (IMGN) for organizing the training workshop and providing the platform for this research. Additionally, we thank the facilitators and speakers who contributed to the success of the ECG training program including Dr. Bunmi Ajala (Consultant Cardiologist, University of Port-harcourt Teaching Hospital), Dr. Francisca Olufunmilayo Inofomah (Consultant Cardiologist, Olabisi Onabanjo University Teaching Hospital), Dr. Bernard Akpu (Consultant Cardiologist, University of Calabar Teaching Hospital) and Dr. Fahad Lawal (Senior Registrar, Usmanu Danfodiyo University Teaching Hospital).\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlikor CA, Onuwaje P. Competency in electrocardiogram interpretation and placement of ECG leads among final year medical students of a Nigerian university. Gaz Med. 2018;6(2):650\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eViljoen CA, Millar RS, Manning K, Burch VC. Effectiveness of blended learning versus lectures alone on ECG analysis and interpretation by medical students. BMC Med Educ. 2020;20:488. doi: 10.1186/s12909-020-02403-y.\u003c/li\u003e\n\u003cli\u003eSibbald M, Davies EG, Dorian P, Yu EH. Electrocardiographic interpretation skills of cardiology residents: are they competent? Can J Cardiol. 2014;30(12):1721\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eMcAloon C, Leach H, Gill S, Aluwalia A, Trevelyan J. Improving ECG competence in medical trainees in a UK district general hospital. Cardiol Res. 2014;5(2):51\u0026ndash;7. doi: 10.14740/cr333e.\u003c/li\u003e\n\u003cli\u003eIsiguzo GC, Iroezindu MO, Muoneme AS, Okeahialam BN. Knowledge and utilisation of electrocardiogram among resident doctors in family medicine in Nigeria. Niger J Clin Pract. 2017;20(9):1133\u0026ndash;8. doi: 10.4103/njcp.njcp_374_16.\u003c/li\u003e\n\u003cli\u003eLindsey S, Moran TP, Stauch MA, Lynch AL, Grabow Moore K. Bridging the gap: evaluation of an electrocardiogram curriculum for advanced practice clinicians. West J Emerg Med. 2024;25(2):155\u0026ndash;9. doi: 10.5811/westjem.18085.\u003c/li\u003e\n\u003cli\u003eEl-Baba M, McLaren J, Argintaru N. The HEARTS ECG workshop: a novel approach to resident and student ECG education. Int J Emerg Med. 2023;16(1):8. doi: 10.1186/s12245-023-00559-0.\u003c/li\u003e\n\u003cli\u003eChaumont C, Morgat C, Ollitrault P, Brejoux C, Extramiana F, Milliez P, et al. How to improve medical students\u0026apos; ECG interpretation skills? Multicenter survey and results of a comparative study evaluating a new educational approach. BMC Med Educ. 2024;24(1):979. doi: 10.1186/s12909-024-05929-7.\u003c/li\u003e\n\u003cli\u003eAdebayo OM, Anele FC, Afolabi TK, Inofomoh FO, Ajibare AO, Aje A. Improving ECG learning and competence among medical and postgraduate trainees: scoping review of available evidence. Ann Ib Postgrad Med. 2024;22(2):95\u0026ndash;105. PMID: 11848365.\u003c/li\u003e\n\u003cli\u003eBalhi S, Baati R, Mrabet MK, Mekki L, Ben Mansour A, Mrabet A. Effectiveness of ECG educational workshops among undergraduate medical students. Tunis Med. 2020;98(11):783\u0026ndash;8. PMID: 33479976.\u003c/li\u003e\n\u003cli\u003eLever NA, Larsen PD, Dawes M, Wong A, Harding SA. Are our medical graduates in New Zealand safe and accurate in ECG interpretation? N Z Med J. 2009;122(1293):9\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eAlShakhs AA, AlAmeer AA, AlEsmaeel ZC, AlMotawa O, AlHaddad SM, AlHaddad HM, et al. Electrocardiogram interpretation competency of primary health care physicians: a cross-sectional study. Healthcare (Basel). 2025;13(23):3040. doi: 10.3390/healthcare13233040.\u003c/li\u003e\n\u003cli\u003eKashou AH, Noseworthy PA, Beckman TJ, Anavekar NS, Cullen MW, Angstman KB, et al. ECG interpretation proficiency of healthcare professionals. Curr Probl Cardiol. 2023;48(3):101924. doi: 10.1016/j.cpcardiol.2023.101924.\u003c/li\u003e\n\u003cli\u003eKopeć G, Magoń W, Hołda M, Podolec P. Competency in ECG interpretation among medical students. Med Sci Monit. 2015;21:3386\u0026ndash;94. doi: 10.12659/MSM.895129.\u003c/li\u003e\n\u003cli\u003ePollock A, O\u0026rsquo;Sullivan J, Traynor B, McAloon C. The impact of two hours of ECG teaching on knowledge and confidence. Authorea. 2023. https://www.authorea.com/users/734834/articles/712605-the-impact-of-two-hours-of-ecg-teaching-on-knowledge-and-confidence. Accessed 25 Dec 2025.\u003c/li\u003e\n\u003cli\u003eKim M, Yoo J. Factors influencing self-confidence and educational needs in electrocardiographic monitoring among emergency room and intensive care unit nurses. Healthcare (Basel). 2025;13(3):277. doi: 10.3390/healthcare13030277.\u003c/li\u003e\n\u003cli\u003eVishnevsky G, Cohen T, Elitzur Y, Gandelman G, Golovchiner G, Strasberg B, et al. Competency and confidence in ECG interpretation among medical students. Int J Med Educ. 2022;13:315\u0026ndash;21. doi: 10.5116/ijme.6382.9e71.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electrocardiography, Medical education, Clinical competence, Medical students, Early-career doctors","lastPublishedDoi":"10.21203/rs.3.rs-9204737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9204737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eElectrocardiogram (ECG) interpretation is a fundamental clinical skill required for the timely diagnosis and management of cardiovascular conditions. However, multiple studies have shown that medical students and early-career doctors often demonstrate inadequate ECG knowledge and low confidence in interpretation. The aim of our study was to assess the effectiveness of a structured ECG workshop on improving the knowledge and self-reported confidence of ECG interpretation among medical students and early-career doctors in Nigeria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA one-group pretest\u0026ndash;posttest quasi-experimental design was conducted to evaluate the effectiveness of a structured online electrocardiography (ECG) training workshop. The study population comprised medical students and early-career doctors who attended at least 75% of the training sessions. Data were collected using validated, self-administered online questionnaires that assessed participants\u0026rsquo; ECG knowledge and self-reported confidence before and immediately after the intervention. Pre- and post-intervention scores were compared using appropriate non-parametric statistical tests, and associations between outcome measures and selected sociodemographic variables were further explored.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOf 424 enrolled participants, 188 completed both assessments (attrition rate 55.7%). The mean knowledge score increased from 42.6% to 67.9% (25.3% improvement; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), while mean confidence scores increased from 39.2% to 72.6% (33.5% improvement; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), both with large effect sizes. Participants without prior ECG training and medical students demonstrated significantly greater improvements. Overall satisfaction with the training was high (mean score 4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study demonstrates that structured online ECG training significantly improved ECG interpretation knowledge and confidence among medical students and early-career doctors in Nigeria. By addressing gaps in undergraduate and early postgraduate medical training through scalable, interactive ECG workshops can equip trainees to make informed clinical decisions on ECG interpretation and strengthen healthcare delivery in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Effectiveness of a structured ECG training program among medical students and early-career doctors in Nigeria: A pre-post quasi-experimental study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 12:37:42","doi":"10.21203/rs.3.rs-9204737/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-29T00:37:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T19:48:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T13:08:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226337398371041845360517864680987741266","date":"2026-04-25T12:06:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T06:43:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156097871714051886421645199223007169915","date":"2026-04-23T05:35:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232106921914347013690179420426783197741","date":"2026-04-21T23:11:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170885982774973740652682448779981884892","date":"2026-04-21T19:46:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T15:13:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T09:47:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-28T20:08:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T11:50:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-03-27T11:44:04+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":"caa4fada-1b58-40ec-9189-d93de4088ea2","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T12:37:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 12:37:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9204737","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9204737","identity":"rs-9204737","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}