Research Preparedness, Competence, and Motivations Among Undergraduate Medical and Dental Students in Nigeria: A Cross-Sectional Study of a Competitive Research Fellowship Cohort

Research Preparedness, Competence, and Motivations Among Undergraduate Medical and Dental Students in Nigeria: A Cross-Sectional Study of a Competitive Research Fellowship Cohort | 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 Research Preparedness, Competence, and Motivations Among Undergraduate Medical and Dental Students in Nigeria: A Cross-Sectional Study of a Competitive Research Fellowship Cohort Godswill Uzoechina, Treasure Osajiuba, Elochukwu Marvellous, Chinonso Ifudu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8740580/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Medical Education → Version 1 posted 13 You are reading this latest preprint version Abstract Background Research skill acquisition is critical for undergraduate medical and dental students, but deficiencies in knowledge and motivation persist in low-resource environments such as Nigeria. Self-perceived research competence, motivations, and preparedness among applicants to a highly competitive research fellowship were evaluated, and predictors of high competence were identified, to provide information for capacity-building. Methods A descriptive cross-sectional analysis of 237 anonymized applications from Years 2 to 6 medical and dental students of the University of Nigeria Enugu Campus (UNEC) to the Medix Frontiers Research Fellowship (January–February 2026) was performed. Demographics, previous research experience (yes, no), competence (8 Likert items from 1 to 5), motivation (5 Likert items from 1 to 5), and preparedness (eg, hours/week, willingness) were assessed. Calculated variables: competence_mean (mean competency scores), motivation_mean (mean motivation scores), high_competence (>3.5), high_motivation (≥4). Cronbach’s α was used to evaluate reliability. Descriptive statistics: Means ± SD, proportions (%). Bivariate: Non-parametric tests (non-normal distribution data), Chi-square/Fisher’s exact test, Pearson’s correlations. Multivariable: Logistic regression (Dependent: high_competence; Independent: previous experience, Medix member, age, sex, stage of training, hours/week, dependable internet); linear regression for continuous competence_mean. No data were lost to follow-up. Analyses in Python (Pandas, SciPy, Statsmodels). Results Mean age 21.98 years (SD 2.79); 51.1% female; 65.4% clinical stage. Previous experience low (25.7% projects, 30.0% fellowships, 13.5% authorship). Competence_mean 2.24 (SD 0.78; α=0.914); high_competence 7.2%. Motivation_mean 4.62 (SD 0.49; α=0.636); high_motivation 89.9%. Competence was significantly different by previous experience (p<0.001 all) but not by stage of training (p=0.473) or gender (p=0.095); motivation was not correlated with competence (r=−0.068, p=0.298). Logistic regression: previous fellowship (aOR 9.46, 95% CI 1.71–52.48, p = 0.010) and age (aOR 1.45/year, 95% CI 1.16–1.82, p=0.001) predicted high_competence; multicollinearity validated (VIF >5 among some). Linear model confirmed associations (R²=0.387). Near-universal willingness (99.6%), too poor for regression analysis. Conclusions High motivation is in stark contrast to low competence and is driven by prior exposure. Fellowships could focus on early learning to provide foundational experiences and inform curricula in resource-limited environments. Undergraduate research training Medical and dental education Research competence assessment Nigeria Sub-Saharan Africa Figures Figure 1 Figure 2 Figure 3 Background Research skills are a core aspect of modern undergraduate medical and dental education, as they form the basis for evidence-based practice, critical appraisal, and lifelong scholarly activity ( 1 ). Systematic reviews and studies from the students’ perspectives demonstrate that early, prior structured research involvement is associated with increased scholarly productivity, greater confidence with research methods, and increased plans for future academic activities ( 1 , 2 ). However, reviews of globally relevant literature also report that the quality and quantity of research training differ according to institutional and regional settings, and these differences create avoidable gaps in readiness among young clinicians who are the future leaders ( 1 ). In low- and middle-income countries (LMICs) such as Nigeria, structured research training remains limited, and undergraduate students are typically not afforded the formal mentorship and practical experiences that would enable them to acquire strong research skills ( 3 , 4 ). While there has been a policy focus on including research in medical curricula, most research initiatives are still theoretical with little hands-on experience, and students are ill-prepared to make meaningful research contributions ( 5 ). Barriers to undergraduate research engagement in Nigeria include weak capacity for mentorship, inadequate research methodological training, and uneven availability of digital learning resources, which collectively inhibit students’ preparedness for research at the undergraduate level ( 4 – 6 ). Reports from institutional surveys in Nigerian contexts also demonstrate that while there is little formal preparedness for emergent fields (eg, precision medicine), mentorship and resource limitations continue to serve as barriers to translating student interest in these topics into tangible research outcomes ( 4 , 7 ). These results highlight an urgent need for locally developed, practical training models suitable for various stages of clinical undergraduate training. The Medix Research Fellowship is a flagship nine-month programme hosted by the Directorate of Research and Publications (RPD) at Medix Frontiers, a non‑governmental organisation (NGO) based in the University of Nigeria Enugu Campus (UNEC), Enugu, Nigeria, dedicated to health advocacy, research, and community engagement, particularly against HIV/AIDS and other prevalent health challenges. It offers a structured mentorship, practical research experience, and continuous training in relevant research methodology, scientific writing, and research ethics; fellows are integrated into supervised projects while obtaining specialised skills training. Assessing applicants’ initial research skills, motivations, and enthusiasm enables appropriately tailored support during the programme and offers empirical evidence-based guidance for capacity-building initiatives in similar resource-limited settings ( 8 ). In accordance with STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines for clear rationale and objectives (STROBE items 2–3) ( 9 ), this study is thus designed to determine level of research competence as well as motivations and readiness to engage in research among the undergraduate medical and dental students of the University of Nigeria, Enugu Campus (UNEC) who are undertaking the Medix Research Fellowship and to establish predictors of high competence and active engagement. We hypothesise that (a) previous research experience (project participation, prior training, authorship) and higher training stage will be associated with higher self-rated research ability and (b) equity variables (gender and training stage) will be related to research competence and readiness, results that would substantiate the need for targeted, stage-sensitive fellowship components. Methods Study design and setting We conducted a cross-sectional survey of undergraduate medical and dental students who applied for the Medix Research Fellowship in the 2026 intake. After preprocessing and exclusion of ineligible applicants (n=253; details of the exclusions are provided below), the analytical sample comprised 237 respondents. Data were extracted from self-completed Google Form application forms, submitted during the fellowship application window (December 2025–January 2026). The fellowship is organised by the Medix Directorate of Research & Publications (RPD) with its office in Enugu City, Nigeria, and the respondents were students enrolled in the University of Nigeria, Enugu Campus (UNEC). The study is reported in accordance with STROBE guidance for observational cross-sectional studies (9). The final version of the questionnaire was administered in English. The full questionnaire is provided as Supplementary File 1. Participants Applicants were considered for inclusion in analysis if they were, at the time of application, registered students in Medicine or Dentistry (years 2–6) at UNEC. Stages of training were: Preclinical (years 2-3), Basic Clinical (year 4), Clinical (years 5A, 5B, 6). Applicants were excluded if they were, at the time of application, not registered students in Medicine or Dentistry (years 2–6) at UNEC. Therefore, applicants to the Medix research fellowship, who were students at ESUCOM, enrolled in dietetics, were already graduates or medical doctors, or were public health students, were consequently excluded during preprocessing. In total, 237 responses were included for the analysis after these exclusions. Verification of eligibility occurred by matching institutional identifiers supplied in the application (faculty/department and level of study) to the list of students admitted to the fellowship. All eligible applicants who completed the baseline application instrument were included (convenience census of applicants). We acknowledge the potential for selection bias in that applicants to a competitive fellowship may be more motivated or research-oriented or may report being more motivated or research-oriented than the general student population; this limitation is highlighted in the Discussion (10). A formal sample-size calculation was not performed because the study analysed the entire accessible applicant pool (all valid responses received within the application window). This census method is suitable for descriptive and exploratory analytic objectives (11). Participant flow An initial total of 253 fellowship applications were received. Following the application of eligibility criteria, 16 participants were excluded: 1 was enrolled at ESUCOM, 3 were dietetics students, 8 were graduates or medical doctors, and 4 were public health students. The final analytic sample included 237 respondents (Figure 1). Data variables and measures All variables were taken from the self-administered online fellowship application form. Key variables were: Sociodemographic and training characteristics: The characteristics of the participants were: Age (in years, a continuous variable), sex (male or female), faculty (Medicine or Dentistry), level of study (Year 2–6), and stage of training (preclinical, basic clinical or clinical). Involvement with the organising body was recorded as Medix membership status (binary: yes/no). Prior research experience: Prior research experience was determined by three dichotomous variables: previous involvement in a research project, previous involvement in a research fellowship, and previous authorship of a scientific publication (coded as 0 = no, 1 = yes for each). Self-rated research competence: Eight items, each rated on a five-point Likert scale (1 = no experience; 2 = basic awareness; 3 = can perform with guidance; 4 = can perform independently; 5 = can teach others) assessed research skills. Competence was rated in (i) formulating research questions, (ii) searching literature, (iii) choosing study designs, (iv) creating instruments for data gathering, (v) analyzing data (using Excel, SPSS, R, or Python), (vi) writing scientifically with the IMRAD structure, (vii) interpreting research outcomes, and (viii) research ethics. Motivation for fellowship participation: Motivation was measured with five items on a 5-point scale of importance (1 = not important, 5 = very important). Aspects included skill enhancement, publishing possibilities, the availability of mentors, career progression, and professional networking. Readiness and commitment: Readiness to participate was evaluated through active participation in the fellowship (binary), availability of reliable internet access (binary), and agreement to the fellowship rules and regulations (ordinal). Composite scores and dichotomous variables were created in advance for the analyses. Mean competence was calculated as the average of the eight competence items (continuous ranging from 1.0 to 5.0), and mean motivation was the average of the five motivation items (continuous ranging from 1.0 to 5.0). High research competence was defined as a competence mean value ≥3.5, abstracted as overall competence corresponding to performance at or above the “can perform independently” anchor. High motivation was defined as a mean motivation score ≥4.0, suggesting high motivation to participate in the fellowship. In multivariable analyses, age was treated as a continuous variable, and categorical predictors were dummy-coded as appropriate. Cutpoints for derived variables were established a priori in order to improve interpretability and minimise analytical flexibility. Data sources and measurement The application questionnaire was completed online, self-reported, and was intended for use in the programmatic selection process; items assessing competence and motivation were adapted from previously used education and medical student research survey instruments from the literature and were thematically mapped to core research activities (1,2). When applied, the item wording used established competency descriptors to enhance content validity. Since all measures are self-reported, they are vulnerable to social desirability and self-assessment biases; no objective, independent skills testing was conducted. To address this, we (a) present internal consistencies (Cronbach’s alpha) for the derived scales, (b) perform additional sensitivity analyses with the continuous competence scores and alternative cut-offs, and (c) discuss the constraints of self-assessment in the Discussion (12). The application tool did not contain a dedicated social desirability scale. Ethical considerations and data availability The analysis was based on de-identified application data, which were gathered for the administration of the programme. Applicants gave their consent for the secondary use of de-identified data during the application process. Ethical review and exemption or approval were obtained from the Ethics Committee of the University of Nigeria Teaching Hospital (NHREC/05/01/2008B-FW00002458-1RB00002424). All approaches conformed to the Declaration of Helsinki and the Nigerian National Code of Health Research Ethics before analysis. All data were maintained in compliance with institutional guidelines for confidentiality and data security. De-identified datasets and codes used to analyse the data will be made available upon request to the corresponding author (13). Statistical analysis All analyses were conducted in Python 3.11, with Pandas v2.0, NumPy v1.26, SciPy v1.11, and Statsmodels v0.14. Analytic steps were pre-specified as follows. We verified the dataset shape (n=237) and inspected missingness. Missing responses were evaluated at both the item and scale levels. Participants with >25% of missing items on the competence or motivation scales were removed from analyses involving those scales. For participants with ≤25% missing items, missing values were imputed with row-mean imputation (mean of that participant’s non-missing responses within the corresponding scale). This procedure retains individual response patterns and, at the same time, reduces bias associated with missing responses. The percentage of missing values and the number of participants excluded due to excessive missing values are reported in the Results. Sensitivity analyses were performed, excluding all imputed data to assess the robustness of the results (14). Internal reliability (Cronbach’s alpha) was computed separately for the competence and motivation items; item-total correlations were inspected. We prespecified that a scale alpha ≥0.70 would indicate acceptable internal consistency. Continuous data are presented as mean ± SD (or median and interquartile range (IQR) for non-normally distributed data). For categorical variables, counts and percentages were reported. Normality was computed for mean competence (continuous) by using the Shapiro-Wilk. Comparisons between groups were performed with a t-test or one-way ANOVA if normality applied; otherwise, a Mann-Whitney U test or Kruskal-Wallis test was used. Categorical associations (e.g., high competence vs prior authorship) were evaluated with χ² tests or Fisher’s exact test when expected cell counts were less than 5. The correlation between mean competence and mean motivation was calculated using Pearson’s r (or Spearman’s ρ for non-normal data (15-17). The main inferential model was a logistic regression with high competence as the outcome. Predictors were: prior research project, prior research fellowship, prior authorship, medix member, age in years, sex, training stage (dummy coded), hours per week, and reliable internet. Adjusted odds ratios (aOR), 95% confidence intervals and p values are presented. Model fit and diagnostics included: Test of multicollinearity using the variance inflation factor (VIF); the presence of a VIF >5 required model revision. Model goodness-of-fit (Hosmer–Lemeshow test) and residuals and influence points were evaluated. Sensitivity analyses with linear regression using mean competence as a continuous outcome were conducted, excluding imputed data (10,18,19). A second logistic model with willingness to actively participate as the outcome was fitted with the same predictors and with mean competence added to assess whether competence predicted willingness. Pre-specified subgroup analyses were stratified by gender and training stage to explore potential differences in research competence and motivation, consistent with equity considerations and STROBE guidelines. A p-value of <0.05 was considered to be statistically significant using two-sided tests. All estimates are presented with 95% confidence intervals (9,10,18). Results Study characteristics The Medix Frontiers Research Fellowship received a total of 253 applications. After excluding incomplete or ineligible submissions (e.g., non-UNEC students, graduates, or non-medicine/dentistry applicants), a total of 237 complete responses from medical and dental students of the University of Nigeria Enugu Campus (UNEC) were considered for the final analytic sample. No additional exclusion was applied for missing competence items (>25% threshold), as none of the participants had missing competence items among the 8 competence items. There were no missing values in the cleaned data set (0% for all columns following preprocessing). Thus, the analytic sample size was n=237. This is summarised in Figure 1, the Participant flow diagram Fig. 1: Participant flow diagram Descriptive statistics The sample was comprised of 237 undergraduate medical (94.1%) and dental (5.9%) students enrolled at UNEC. The mean age was 21.98 (SD = 2.79). Females accounted for 51.1% (n=121) and males for 49.0% (n=116). The stage of training was distributed as preclinical 16.5% (n=39), basic clinical 18.1% (n=43), and clinical 65.4% (n=155). There was limited previous research experience: 25.7% (n=61) had reported a prior involvement in a research project, 30.0% (n=71) a prior fellowship, and 13.5% (n=32) authorship. Predominant preference was for clinical research (49.8%, n=118), followed by public health (24.9%, n=59). Research time commitment per week was 20 hours for 3.0% (n=7). A reliable internet connection was available to 97.5% (n=231), and 99.6% (n=236) expressed a desire to actively participate. This is summarised in Table 1. Table 1: Participant demographics and baseline characteristics (n=237) Characteristic Category n % Age (years) Mean ± SD 21.98 ± 2.79 - Sex Male (1) 116 48.95 Female (2) 121 51.05 Course of study Medicine (1) 223 94.09 Dentistry (2) 14 5.91 Training stage Preclinical (1) 39 16.46 Basic Clinical (2) 43 18.14 Clinical (3) 155 65.40 Medix member No (0) 129 54.43 Yes (1) 108 45.57 Prior research project No (0) 176 74.26 Yes (1) 61 25.74 Prior research fellowship No (0) 166 70.04 Yes (1) 71 29.96 Prior authorship No (0) 205 86.50 Yes (1) 32 13.50 Preferred research area Clinical research 118 49.79 Public health 59 24.89 Data science/analytics 26 10.97 Medical education 24 10.13 Health policy 10 4.22 Hours per week 20 7 2.95 Willing active participation No (0) 1 0.42 Yes (1) 236 99.58 Reliable internet No (0) 6 2.53 Yes (1) 231 97.47 High competence (mean > 3.5) No (≤3.5) 220 92.83 Yes (>3.5) 17 7.17 High motivation (mean ≥ 4) No (<4) 24 10.13 Yes (≥4) 213 89.87 The competence scale (8 items, Likert 1–5) demonstrated excellent internal consistency (Cronbach’s α = 0.914). The mean competence was 2.24 (SD 0.78), and item-total correlations ranged from 0.580 (data analysis) to 0.836 (study design). High competence (>3.5) was observed in 7.2% (n=17). The motivation scale (5 items, Likert 1–5) showed an acceptable internal consistency (Cronbach’s α = 0.636). The mean motivation was 4.62 (SD = 0.49) with 89.9% (n=213) achieving high motivation (≥4). Item-total correlations varied from 0.155 (skill improvement) to 0.617 (career). This is summarised in Table 2. Table 2: Competence and motivation scale summaries Scale / Item Mean ± SD or Item-total correlation Cronbach’s α Competence scale (8 items, Likert 1–5) Mean: 2.24 ± 0.78 0.914 (excellent) skill_research_question Item-total correlation: 0.767 - skill_literature_search Item-total correlation: 0.788 - skill_study_design Item-total correlation: 0.836 - skill_data_analysis Item-total correlation: 0.580 - skill_data_collection Item-total correlation: 0.792 - skill_IMRAD_writing Item-total correlation: 0.767 - skill_research_interpretation Item-total correlation: 0.778 - skill_research_ethics Item-total correlation: 0.794 - Motivation scale (5 items, Likert 1–5) Mean: 4.62 ± 0.49 0.636 (acceptable) motivation_skill_improvement (1 = Not important, 5 = Very important) Item-total correlation: 0.155 - motivation_publication (1 = Not important, 5 = Very important) Item-total correlation: 0.496 - motivation_mentorship (1 = Not important, 5 = Very important) Item-total correlation: 0.524 - motivation_career (1 = Not important, 5 = Very important) Item-total correlation: 0.617 - motivation_networking (1 = Not important, 5 = Very important) Item-total correlation: 0.208 - Bivariate Analyses The mean competence was not normally distributed (Shapiro-Wilk p = 4.57 × 10⁻¹⁰), and therefore, we conducted non-parametric tests. Competence varied significantly by previous research experience: greater in those with previous projects (Mann-Whitney U = 1934.0, p < 0.001), fellowships (U = 3028.0, p < 0.001), and authorship (U = 1360.0, p < 0.001). No significant differences were found between training levels (Kruskal-Wallis H = 1.498, p = 0.473) or gender (Mann-Whitney U = 7895.0, p = 0.095). Figure 2 visually highlights the bivariate difference in competence_mean by prior research project (Yes/No) Fig. 2: Box plot of self-reported research competence mean scores (1–5 Likert scale) stratified by prior research project experience (Yes vs. No) Higher competence was strongly correlated with previous authorship (Fisher’s exact p = 3.55×10⁻⁷) and membership of Medix (p = 0.004), but not with willingness to participate (p = 1.0). The association between mean competence and mean motivation was weak and non-significant (Pearson r = −0.068, p = 0.298). This is summarised in Table 3 Table 3: Bivariate associations with research competence A. Group differences in competence_mean (non-parametric tests due to non-normality) Predictor Category n Test statistic p-value Training stage Preclinical (1) 39 Kruskal-Wallis H = 1.498 0.473 Basic Clinical (2) 43 Clinical (3) 155 Sex Male (1) 116 Mann-Whitney U = 7895.0 0.095 Female (2) 121 Prior research project No (0) 176 Mann-Whitney U = 1934.0 <0.001 Yes (1) 61 Prior research fellowship No (0) 166 Mann-Whitney U = 3028.0 <0.001 Yes (1) 71 Prior authorship No (0) 205 Mann-Whitney U = 1360.0 <0.001 Yes (1) 32 B. Associations with high competence (categorical outcomes) Predictor Category Test p-value Prior authorship No (0) Fisher's exact 3.55 × 10⁻⁷ Yes (1) Medix member No (0) Fisher's exact 0.004 Yes (1) Willing active participation No (0) Fisher's exact 1.000 Yes (1) C. Correlation between continuous scales Variable pair Pearson r p-value competence_mean vs motivation_mean -0.068 0.298 Multivariable analyses Logistic regression (outcome: high_competence) converged successfully (n=237). Significant predictors were prior research fellowship (aOR = 9.46, 95% CI 1.71–52.48, p = 0.010) and age (aOR = 1.45 per year, 95% CI 1.16–1.82, p = 0.001), as they were independently associated with higher odds of high competence. Prior authorship approached significance (aOR=4.10, 95% CI= 0.80–20.99, p=0.091) and Medix membership was borderline protective (aOR = 0.19, 95% CI 0.03–1.08, p = 0.061). Other predictors (previous project, sex, stage of training, hours/week, reliable internet) were all insignificant. Pseudo R² = 0.510; LLR p 5 for age in years = 40.23, training_stage = 14.00, reliable_internet = 27.71, sex = 8.96, hours_per_week_num = 7.32), suggesting caution in interpretation; however, model discrimination was good (AUC= 0.947). Figure 3 summarizes multivariable results via a forest plot. Fig. 3: Forest plot of adjusted odds ratios (aORs) and 95% confidence intervals for significant and borderline predictors of high research competence A secondary logistic model of willingness to participate showed quasi-complete separation (99.6% yes responses), producing erratic estimates (with large aORs and NaN CIs); therefore, this finding was considered inappropriate for logistic regression. The multivariable logistic regression results and VIF are summarised in Table 4 Table 4: Multivariable logistic regression results for high competence (adjusted ORs, 95% CIs, p-values) and VIFs A. Logistic regression model (outcome: high_competence > 3.5; n=237) Predictor Adjusted OR (aOR) 95% CI lower 95% CI upper p-value Intercept (const) 9.34 × 10⁻¹⁰ 3.84 × 10⁻⁸⁶ 2.28 × 10⁶⁷ 0.817 Prior research project 2.48 0.36 17.16 0.358 Prior research fellowship 9.46 1.71 52.48 0.010 Prior authorship 4.10 0.80 20.99 0.091 Medix member 0.19 0.03 1.08 0.061 Age (years) 1.45 1.16 1.82 0.001 Sex 1.28 0.30 5.49 0.736 Training stage 1.63 0.61 4.36 0.334 Hours per week (numeric) 0.99 0.36 2.74 0.990 Reliable internet 478.41 2.40 × 10⁻⁷⁴ 9.56 × 10⁷⁸ 0.945 B. Variance inflation factors (VIF) for multicollinearity assessment Predictor VIF Prior research project 2.25 Prior research fellowship 1.84 Prior authorship 1.78 Medix member 2.16 Age (years) 40.23 Sex 8.96 Training stage 14.00 Hours per week (numeric) 7.32 Reliable internet 27.71 A linear regression on continuous competence_mean (more stable) also confirmed positive associations with prior project (β=0.544, p<0.001), fellowship (β=0.451, p<0. 001), age (β=0.041, p=0.012) and negative associations with Medix membership (β= –0.193, p=0.030). R² = 0.387 (adjusted 0.363), F = 15.95, p < 0.001. Linear regression results are summarised in Table 5. Sensitivity analyses performed using median imputation (in comparison to mean) on the motivation items also resulted in the same value for motivation_mean (4.62 ± 0.49), validating robustness. An interaction term (prior fellowship × Medix membership) did not materially alter main effects but showed quasi-separation and instability. Table 5: Linear regression results for competence_mean (continuous outcome; n=237) Predictor Coefficient (β) Standard Error t-value p-value 95% CI lower 95% CI upper Intercept (const) 1.323 0.495 2.676 0.008 0.349 2.298 Prior research project 0.544 0.119 4.554 <0.001 0.308 0.779 Prior research fellowship 0.451 0.100 4.511 <0.001 0.254 0.648 Prior authorship 0.262 0.147 1.779 0.077 -0.028 0.551 Medix member -0.193 0.089 -2.179 0.030 -0.368 -0.019 Age (years) 0.041 0.016 2.545 0.012 0.009 0.073 Sex -0.044 0.085 -0.516 0.606 -0.212 0.124 Training stage -0.088 0.058 -1.522 0.129 -0.203 0.026 Hours per week (numeric) 0.009 0.056 0.164 0.870 -0.101 0.119 Reliable internet 0.059 0.263 0.223 0.824 -0.459 0.576 Discussion Interpretation of main findings This descriptive cross-sectional study among 237 medical and dental students of the University of Nigeria, Enugu Campus (UNEC) applying for Medix Frontiers Research Fellowship, revealed a rather surprising disparity between a high level of motivation and low self-reported research competence. The mean competence score was 2.24 (SD 0.78) on a 5-point Likert scale, with 7.2% of participants achieving high competence (> 3.5). In contrast, motivation was significant (mean 4.62, SD 0.49) with 89.9% scoring ≥ 4. Prior research experience, especially prior research fellowships (aOR 9.46, 95% CI 1.71–52.48, p = 0.010) and authorship (aOR 4.10, 95% CI 0.80–20.99, p = 0.091), stood out as the most robust predictor of high competence, in addition to increasing age (aOR 1.45 per year, 95% CI 1.16–1.82, p = 0.001). Medix membership demonstrated a borderline negative association (aOR 0.19, 95% CI 0.03–1.08, p = 0.061). There was no significant difference in competence based on training stage (p = 0.473) or sex (p = 0.095). Also, the correlation between competence and motivation was weak and non-significant (r = − 0.068, p = 0.298). These results demonstrate that although there is no shortage of motivation among this motivated applicant group, although actual self-perceived skills remain limited, with previous experience being the major driver of competence. The absence of a gradient of competence at different stages of training is remarkable. Although participants progressed from preclinical to clinical stage, students reported no substantial improvement in research skills, in a manner that corroborated the finding that the formal curricula of many Nigerian medical schools place more emphasis on clinical training than on research methodology and competence ( 21 ). Meanwhile, prior experience made a huge difference with regard to levels of competence, highlighting the importance of providing students with early, structured opportunities to develop experiences and skills in question formulation, literature searching, study design, and data interpretation. The borderline negative association with Medix membership might be due to selection effects, as members may be more self-critical or have higher standards, or other unmeasured factors such as competing commitments. This slightly negative correlation between competence and motivation is in line with findings in the literature suggesting that the realisation of skill gaps can paradoxically increase motivation in high-achieving applicants ( 22 ). Comparison with existing literature These findings are consistent with larger trends in Sub-Saharan Africa (SSA) and among low- and middle-income countries (LMICs). A national cross-sectional study of Nigerian medical students in 2025 highlighted similar barriers, which included: absence of statistical skills (74.2%), time limitation (73.3%), and limited training in research methodology, among others as the largest barriers to participation ( 21 ). Past research from SSA revealed systemic deficiencies in research labs, funding, and skills training across medical schools ( 23 ). Also, a 2020 study in Lagos, Nigeria, observed similar low participation attributed to a perceived shortage of mentorship and resources ( 24 ). Around the world, self-rated abilities of undergraduates from LMICs tend to be lower compared to those from high-income settings, which often feature more structured approaches to research (e.g., compulsory projects or electives), ultimately yielding higher baseline skills ( 25 ). This high motivation observed here mirrors results from LIMC cohorts, where enthusiasm for research is high but not met with institutional support ( 21 , 26 ). Implications for fellowship design and medical education The implications for medical education, fellowship design, and other similar training and capacity-building initiatives are clear. Curricula need to focus on integrating research training at an early stage, ideally in preclinical years, to close the gap in competencies before clinical studies place further demands on students. Fellowships such as Medix Frontiers Research Fellowship can act as focused interventions, offering guidance, statistical training, and real-world projects, especially for inexperienced students. Early-stage interventions (such as workshops on IMRAD writing, ethics and data analysis) may potentially speed learning and minimise reliance on autodidactic learning. Given the strong predictive role of prior fellowships, scaling such opportunities could create a virtuous cycle of competence and engagement and have a positive feedback effect on competence and engagement. Also, selection and early stratification should be based on baseline differences: fellows with less background experience will benefit from an expanded foundational module (covering basic study design, literature searching and introductory data analysis) and fellows with more prior experience can move more quickly toward independent project work and manuscript preparation. The positive association between competence and willingness to participate suggests embedding early, practical tasks that enhance confidence and tangible competencies, which could also increase retention and completion rates. Finally, as reliable internet was identified as pertinent in preliminary results, programmes in resource-limited settings are encouraged to emphasise offline-compatible solutions, robust data-access support, and protected data-use time to counter technological challenges ( 4 ). Strengths of the study The strengths of the study are that it consisted of a large sample size of fellowship applicants (n = 237), the use of structured self-report measures with good (competence α = 0.914) and acceptable (motivation α = 0.636) reliability, and a thorough multivariable analysis applying both logistic and linear regression to address outcome rarity and multicollinearity issues. Non-parametric tests for non-normal data and sensitivity analyses (e.g., median imputation yielding identical results) contributed to robustness. Also, measurement used a brief competence-based instrument that maps onto specific aspects of research (question formulation, literature searching, analysis, IMRAD writing, ethics), and psychometric analyses (internal consistency) were considered a priori. The analysis combined descriptive, reliability, bivariate, and multivariate models to examine the independent predictors of the outcome whilst maintaining interpretability for programme managers. Limitations of the study Limitations must be acknowledged. Self-reported competence and motivation may lead to an overestimation or underestimation of true ability, influenced by social desirability or lack of objective validation, as objective skill testing (e.g., timed literature searches or data tasks) was not conducted ( 27 ). The cross-sectional design precludes causal interpretations, e.g., whether prior experience leads to higher competence or vice versa cannot be determined, as associations between prior exposure and competence may reflect reciprocal selection effects (motivated students seek research opportunities). The results are from a single public institution (UNEC) and among highly motivated fellowship applicants and, as such, may not be generalizable to the general Nigerian or SSA student population, as motivation and access might well be lower relative to similar applicant pools. Selection bias presumably led to an overestimation of motivation and competence relative to non-applicants. Multicollinearity (high VIF for age, training stage, and reliable internet) might have also led to inflated standard errors for some of the estimates, although linear regression on continuous competence provided stable insights. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for this secondary analysis of anonymised application data. Furthermore, there should be caution in generalising findings, as they are most relevant to undergraduate medical and dental students applying to a competitive research training programs within the context of low-resource universities. When extrapolating to a wider student population or other countries, differences in curricula, mentorship availability and digital infrastructure should be considered. Future directions Future work should involve a longitudinal follow-up of fellowship participants to determine changes in competence and objective research outputs (manuscripts submitted/accepted, conference presentations) and to evaluate which curricular components most strongly predict sustained scholarly activity. Intervention trials contrasting standard versus intensified foundational modules for low-competence entrants would clarify causality and inform scalable training packages. Multi-centre studies involving Nigerian and sub-Saharan African institutions are needed to increase generalizability and identify system-level barriers that can be addressed through policy interventions. Conclusion Though Nigerian medical and dental students are highly motivated, their self-perceived research competence is low and largely influenced by previous research exposure. Early targeted interventions (via fellowships and curriculum reform) will be critical to building capacity and filling SSA's research training void. Declarations Ethics approval and consent to participate This study involved secondary analysis of de-identified application data collected for the Medix Research Fellowship. All applicants provided informed consent for the use of their anonymized data for research purposes at the point of application. Ethical review and exemption or approval were obtained from the Ethics Committee of the University of Nigeria Teaching Hospital (NHREC/05/01/2008B-FW00002458-1RB00002424). All approaches conformed to the Declaration of Helsinki and the Nigerian National Code of Health Research Ethics before analysis. All data were maintained in compliance with institutional guidelines for confidentiality and data security. Consent for publication Not applicable. No individual person’s data are presented in this manuscript. Competing interests The authors declare that they have no competing interests. All authors confirm that they have no affiliations with, or involvement in, any organization or entity with any financial interest (such as honoraria, educational grants, participation in speakers’ bureaus, membership, employment, consultancies, stock ownership, patent-licensing arrangements, or expert testimony) or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) that could be perceived to influence the content of this manuscript. Funding This research was conducted under the leadership of the Director of Research & Publications at Medix Frontiers and used only internal resources of the organization. No specific grant or external funding from public, commercial, or not-for-profit agencies was received for this work. Acknowledgements The authors gratefully acknowledge Medix Frontiers for supporting the fellowship and the applicants who participated in the program and provided data for this research. Clinical trial number Not applicable. Author Contributions Godswill Uzoechina (GU) conceptualized the study, designed the analysis plan, performed the data analysis, and drafted the Results and Discussion sections. GU also coordinated overall manuscript preparation, supervised the project, and critically revised all sections for intellectual content. Treasure Osajiuba (TO), Elochukwu Marvellous (EM), and Chinonso Ifudu (CI) managed data collection and contributed to the development of the study instruments. TO drafted the Methods section, EM drafted the Introduction, and CI drafted the Abstract and Conclusion. All authors reviewed, edited, and approved the final manuscript and agree to be accountable for all aspects of the work. References Amgad M, Tsui MM, Liptrott SJ, Shash E. Medical student research: an integrated mixed-methods systematic review and meta-analysis. PLoS One. 2015 Jun 18;10(6):e0127470. doi: 10.1371/journal.pone.0127470. PMID: 26086391; PMCID: PMC4472353. Burgoyne LN, O'Flynn S, Boylan GB. Undergraduate medical research: the student perspective. Med Educ Online. 2010 Sep 10;15. doi: 10.3402/meo.v15i0.5212. PMID: 20844608; PMCID: PMC2939395. Olajide T, Arokoyo K, Adesola A, Okeke S, Abdullateef R, Anele F, et al. Building a research culture among nigerian medical students: the modus operandi of the college research and innovation hub. BMC Med Educ. 2024 Dec 18;24(1):1465. doi: 10.1186/s12909-024-06518-4. PMID: 39696335; PMCID: PMC11653916. Ossai EN, Eze II, Umeokonkwo CD, Izuagba CO, Ogbonnaya LU. Readiness, barriers, and attitude of students towards online medical education amidst COVID-19 pandemic: a study among medical students of Ebonyi State University Abakaliki, Nigeria. PLoS One. 2023 Apr 27;18(4):e0284980. doi: 10.1371/journal.pone.0284980. PMID: 37104470; PMCID: PMC10138982. Pascal Iloh GU, Amadi AN, Iro OK, Agboola SM, Aguocha GU, Chukwuonye ME. Attitude, practice orientation, benefits and barriers towards health research and publications among medical practitioners in Abia State, Nigeria: a cross-sectional study. Niger J Clin Pract. 2020 Feb;23(2):129-137. doi: 10.4103/njcp.njcp_284_18. PMID: 32031085. Ughasoro MD, Musa A, Yakubu A, Adefuye BO, Folahanmi AT, Isah A, et al. Barriers and solutions to effective mentorship in health research and training institutions in Nigeria: mentors, mentees, and organizational perspectives. Niger J Clin Pract. 2022 Mar;25(3):215-225. doi: 10.4103/njcp.njcp_154_20. PMID: 35295040. Ogamba CF, Roberts AA, Ajudua SC, Akinwale MO, Jeje FM, Ibe FO, et al. Perceptions of Nigerian medical students regarding their preparedness for precision medicine: a cross-sectional survey in Lagos, Nigeria. BMC Med Educ. 2023 Nov 17;23(1):879. doi: 10.1186/s12909-023-04841-w. PMID: 37978519; PMCID: PMC10656926. Medix Frontiers. Medix Frontiers: Ut Veritas, Vitam Servet. Enugu: Medix Frontiers; 2024. Available from: https://medix.framer.website/ von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008 Apr;61(4):344-9. doi: 10.1016/j.jclinepi.2007.11.008. PMID: 18313558. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2008. 758 p. Daniel WW, Cross CL. Biostatistics: a foundation for analysis in the health sciences. 10th ed. Hoboken: Wiley; 2013. 714 p. 12. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003 Oct;88(5):879-903. doi: 10.1037/0021-9010.88.5.879. 13. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013 Nov 27;310(20):2191-2194. doi: 10.1001/jama.2013.281053. PMID: 24141714. Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. Hoboken: Wiley; 2002. 381 p. Altman DG. Practical statistics for medical research. London: Chapman & Hall; 1991. 611 p. Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52(3-4):591-611. doi: 10.1093/biomet/52.3-4.591. Kirkwood BR, Sterne JAC. Essential medical statistics. 2nd ed. Oxford: Blackwell Science; 2003. 501 p. Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken: Wiley; 2013. 552 p. Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. 2nd ed. New York: Springer; 2012. 509 p. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models. 5th ed. New York: McGraw-Hill/Irwin; 2005. 1396 p. Kingpriest PT, Okpanachi JA, Afolabi SA, Ayorinde MM, Muoghallu OI, Alapa GE, et al. A national cross-sectional study on research opportunities and barriers among medical students in Nigeria, with recommendations. BMC Med Educ. 2025 May 16;25(1):713. doi: 10.1186/s12909-025-07308-2. PMID: 40380171; PMCID: PMC12085030. Adebisi YA. Undergraduate students' involvement in research: values, benefits, barriers and recommendations. Ann Med Surg (Lond). 2022 Aug 17;81:104384. doi: 10.1016/j.amsu.2022.104384. PMID: 36042923; PMCID: PMC9420469. Nyarko OO, Ansong D, Osei-Akoto A, Konadu SO, Opoku G. Developing research competencies of undergraduate medical students in Sub-Saharan Africa. WJMER. 2019;21(1):16-18. Available from: https://www.wjmer.co.uk/library/downloads/articles/1578655415.pdf Awofeso OM, Roberts AA, Okonkwor CO, Nwachukwu CE, Onyeodi I, Lawal IM, et al. Factors affecting undergraduates' participation in medical research in Lagos. Niger Med J. 2020 May-Jun;61(3):156-162. doi: 10.4103/nmj.NMJ_94_19. Epub 2020 Jul 4. PMID: 33100468; PMCID: PMC7547749. Chang Y, Ramnanan CJ. A review of literature on medical students and scholarly research: experiences, attitudes, and outcomes. Acad Med. 2015 Aug;90(8):1162-73. doi: 10.1097/ACM.0000000000000702. PMID: 25853690. Alduraibi KM, Aldosari M, Alharbi AD, Alkhudairy AI, Almutairi MN, Alanazi NS, et al. Challenges and barriers to medical research among medical students in Saudi Arabia. Cureus. 2024 May 2;16(5):e59505. doi: 10.7759/cureus.59505. PMID: 38826878; PMCID: PMC11144033. Ezeugo NC, Eleje LI, Obiasor GE, Mbelede NG, Osonwa KE, Metu IC, et al. Self-assessment techniques in clinical studies in public universities in Anambra State: benefits and alignment with supervisors evaluation as perceived by medical students. J Med Educ Curric Dev. 2024 Dec 25;11:23821205241308787. doi: 10.1177/23821205241308787. PMID: 39777253; PMCID: PMC11705325. Additional Declarations No competing interests reported. Supplementary Files MedixFrontiersResearchFellowshipQuestionnaire.docx Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 24 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Editor invited by journal 05 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 04 Feb, 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. 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18:13:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plot of self-reported research competence mean scores (1–5 Likert scale) stratified by prior research project experience (Yes vs. No)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8740580/v1/79f45432ecd1ac3d87e31e13.png"},{"id":104400624,"identity":"95fac227-7f4e-45b8-8cac-5fa3fd006c0b","added_by":"auto","created_at":"2026-03-11 12:10:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of adjusted odds ratios (aORs) and 95% confidence intervals for significant and borderline predictors of high research competence\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3..png","url":"https://assets-eu.researchsquare.com/files/rs-8740580/v1/bc6ac029eab35258b53d0db6.png"},{"id":106344364,"identity":"80effbe7-b150-4149-84f5-dbc54b684fb6","added_by":"auto","created_at":"2026-04-07 16:13:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1706082,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8740580/v1/872800de-5ce9-45fe-8d9c-3d407e5c9403.pdf"},{"id":103774692,"identity":"3419e1be-8a2b-424b-9a23-5b4e3d056e0d","added_by":"auto","created_at":"2026-03-02 18:13:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10381,"visible":true,"origin":"","legend":"","description":"","filename":"MedixFrontiersResearchFellowshipQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8740580/v1/dff939f6f67cb6b03e6d5548.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eResearch Preparedness, Competence, and Motivations Among Undergraduate Medical and Dental Students in Nigeria: A Cross-Sectional Study of a Competitive Research Fellowship Cohort\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eResearch skills are a core aspect of modern undergraduate medical and dental education, as they form the basis for evidence-based practice, critical appraisal, and lifelong scholarly activity (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Systematic reviews and studies from the students\u0026rsquo; perspectives demonstrate that early, prior structured research involvement is associated with increased scholarly productivity, greater confidence with research methods, and increased plans for future academic activities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, reviews of globally relevant literature also report that the quality and quantity of research training differ according to institutional and regional settings, and these differences create avoidable gaps in readiness among young clinicians who are the future leaders (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In low- and middle-income countries (LMICs) such as Nigeria, structured research training remains limited, and undergraduate students are typically not afforded the formal mentorship and practical experiences that would enable them to acquire strong research skills (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). While there has been a policy focus on including research in medical curricula, most research initiatives are still theoretical with little hands-on experience, and students are ill-prepared to make meaningful research contributions (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBarriers to undergraduate research engagement in Nigeria include weak capacity for mentorship, inadequate research methodological training, and uneven availability of digital learning resources, which collectively inhibit students\u0026rsquo; preparedness for research at the undergraduate level (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Reports from institutional surveys in Nigerian contexts also demonstrate that while there is little formal preparedness for emergent fields (eg, precision medicine), mentorship and resource limitations continue to serve as barriers to translating student interest in these topics into tangible research outcomes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These results highlight an urgent need for locally developed, practical training models suitable for various stages of clinical undergraduate training.\u003c/p\u003e \u003cp\u003eThe Medix Research Fellowship is a flagship nine-month programme hosted by the Directorate of Research and Publications (RPD) at Medix Frontiers, a non‑governmental organisation (NGO) based in the University of Nigeria Enugu Campus (UNEC), Enugu, Nigeria, dedicated to health advocacy, research, and community engagement, particularly against HIV/AIDS and other prevalent health challenges. It offers a structured mentorship, practical research experience, and continuous training in relevant research methodology, scientific writing, and research ethics; fellows are integrated into supervised projects while obtaining specialised skills training. Assessing applicants\u0026rsquo; initial research skills, motivations, and enthusiasm enables appropriately tailored support during the programme and offers empirical evidence-based guidance for capacity-building initiatives in similar resource-limited settings (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn accordance with STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines for clear rationale and objectives (STROBE\u0026ensp;items 2\u0026ndash;3) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), this study is thus designed to determine level of research competence as well as motivations and readiness to engage in research among the undergraduate medical and dental students of the University of Nigeria, Enugu Campus (UNEC) who are undertaking the Medix Research Fellowship and to establish predictors of high competence and active engagement. We hypothesise that (a) previous research experience\u0026ensp;(project participation, prior training, authorship) and higher training stage will be associated with higher self-rated research ability and (b) equity variables (gender and training stage) will be related to research competence and readiness, results that would substantiate the need for targeted, stage-sensitive fellowship components.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy design and setting\u003c/em\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;We conducted a cross-sectional survey of undergraduate medical and dental students who applied for the Medix Research Fellowship in the 2026 intake. After preprocessing and exclusion of ineligible applicants (n=253; details of the exclusions are provided below), the analytical sample comprised 237 respondents. Data were extracted from self-completed Google Form application forms, submitted during the fellowship application window (December 2025\u0026ndash;January 2026). The fellowship is organised by the Medix Directorate of Research \u0026amp; Publications (RPD) with its office in Enugu City, Nigeria, and the respondents were students enrolled in the University of Nigeria, Enugu Campus (UNEC). The study is reported in accordance with STROBE guidance for observational cross-sectional studies (9). The final version of the questionnaire was administered in English. The full questionnaire is provided as Supplementary File 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eApplicants were considered for inclusion in analysis if they were, at the time of application, registered students in Medicine or Dentistry (years 2\u0026ndash;6) at UNEC. Stages of training were: Preclinical (years 2-3), Basic Clinical (year 4), Clinical (years 5A, 5B, 6).\u003c/p\u003e\n\u003cp\u003eApplicants were excluded if they were, at the time of application, not registered students in Medicine or Dentistry (years 2\u0026ndash;6) at UNEC. Therefore, applicants to the Medix research fellowship, who were students at ESUCOM, enrolled in dietetics, were already graduates or medical doctors, or were public health students, were consequently excluded during preprocessing. In total, 237 responses were included for the analysis after these exclusions.\u003c/p\u003e\n\u003cp\u003eVerification of eligibility occurred by matching institutional identifiers supplied in the application (faculty/department and level of study) to the list of students admitted to the fellowship. All eligible applicants who completed the baseline application instrument were included (convenience census of applicants). We acknowledge the potential for selection bias in that applicants to a competitive fellowship may be more motivated or research-oriented or may report being more motivated or research-oriented than the general student population; this limitation is highlighted in the Discussion (10).\u003c/p\u003e\n\u003cp\u003eA formal sample-size calculation was not performed because the study analysed the entire accessible applicant pool (all valid responses received within the application window). This census method is suitable for descriptive and exploratory analytic objectives (11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipant flow\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn initial total of 253 fellowship applications were received. Following the application of eligibility criteria, 16 participants were excluded: 1 was enrolled at ESUCOM, 3 were dietetics students, 8 were graduates or medical doctors, and 4 were public health students. The final analytic sample included 237 respondents (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData variables and measures\u003cbr\u003e\u003c/em\u003eAll variables were taken from the self-administered online fellowship application form. Key variables were:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociodemographic and training characteristics:\u003c/strong\u003e The characteristics of the participants were: Age (in years, a continuous variable), sex (male or female), faculty (Medicine or Dentistry), level of study (Year 2\u0026ndash;6), and stage of training (preclinical, basic clinical or clinical). Involvement with the organising body was recorded as Medix membership status (binary: yes/no).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior research experience:\u0026nbsp;\u003c/strong\u003ePrior research experience was determined by three dichotomous variables: previous involvement in a research project, previous involvement in a research fellowship, and previous authorship of a scientific publication (coded as 0 = no, 1 = yes for each).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-rated research competence:\u003c/strong\u003e Eight items, each rated on a five-point Likert scale (1 = no experience; 2 = basic awareness; 3 = can perform with guidance; 4 = can perform independently; 5 = can teach others) assessed research skills. Competence was rated in (i) formulating research questions, (ii) searching literature, (iii) choosing\u0026ensp;study designs, (iv) creating instruments for data gathering, (v) analyzing data (using Excel, SPSS, R, or Python), (vi) writing scientifically with the IMRAD structure, (vii) interpreting research outcomes, and (viii) research ethics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMotivation for fellowship participation:\u003c/strong\u003e Motivation was measured with five items on a 5-point scale of importance (1 = not important, 5 = very important). Aspects included skill enhancement, publishing possibilities, the availability of mentors, career progression, and professional networking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness and commitment:\u003c/strong\u003e Readiness to participate was evaluated through active participation in the fellowship (binary), availability of reliable internet access (binary), and agreement to the fellowship rules and regulations (ordinal).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComposite scores and dichotomous variables were created in advance for the analyses. Mean competence was calculated as the average of the eight competence items (continuous ranging\u0026ensp;from 1.0 to 5.0), and mean motivation was the average of the five motivation items (continuous ranging from 1.0 to 5.0).\u003c/p\u003e\n\u003cp\u003eHigh research competence was defined as a competence mean value \u0026ge;3.5, abstracted as overall competence corresponding to performance at or above the \u0026ldquo;can perform independently\u0026rdquo; anchor. High motivation was defined as a mean motivation score\u0026ensp;\u0026ge;4.0, suggesting high motivation to participate in the fellowship.\u003c/p\u003e\n\u003cp\u003eIn multivariable analyses, age was treated as a continuous variable, and categorical predictors were dummy-coded as appropriate. Cutpoints for derived variables were established a priori in order to improve interpretability and minimise analytical flexibility.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData sources and measurement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe application questionnaire was completed online, self-reported, and was intended for use in the programmatic selection process; items assessing competence and motivation were adapted from previously used education and medical student research survey instruments from the literature and were thematically mapped to core research activities (1,2). When applied, the item wording used established competency descriptors to enhance content validity.\u003c/p\u003e\n\u003cp\u003eSince all measures are self-reported, they are vulnerable to social desirability and self-assessment biases; no objective, independent skills testing was conducted. To address this, we (a) present internal consistencies (Cronbach\u0026rsquo;s alpha) for the derived scales, (b) perform additional sensitivity analyses with the continuous competence scores and alternative cut-offs, and (c) discuss the constraints of self-assessment in the Discussion (12). The application tool did not contain a dedicated social desirability scale.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical considerations and data availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis was based on de-identified application data, which were gathered for the administration of the programme. Applicants gave their consent for the secondary use of de-identified\u0026ensp;data during the application process. Ethical review and exemption or approval were obtained from the Ethics Committee of the University of Nigeria Teaching Hospital (NHREC/05/01/2008B-FW00002458-1RB00002424). All approaches conformed to the Declaration of Helsinki and the Nigerian National Code of Health Research Ethics before analysis. All data were maintained in compliance with institutional guidelines for confidentiality and data security. De-identified datasets and codes used to analyse the data will be made available upon request to the corresponding author (13).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted in Python 3.11, with Pandas v2.0, NumPy v1.26, SciPy v1.11, and Statsmodels v0.14. Analytic steps were pre-specified as follows.\u003c/p\u003e\n\u003cp\u003eWe verified the dataset shape (n=237)\u0026ensp;and inspected missingness. Missing responses were evaluated at both the item and scale levels. Participants with \u0026gt;25% of missing items on the competence or motivation scales were removed from analyses involving those scales. For participants with \u0026le;25% missing items, missing values were imputed with row-mean imputation (mean of that participant\u0026rsquo;s non-missing responses within the corresponding scale). This procedure retains individual response patterns and, at the same time, reduces bias associated with missing responses. The percentage of missing values and the number of participants excluded due to excessive missing values are reported in the Results. Sensitivity analyses were performed, excluding all imputed data to assess the robustness of the results (14).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInternal reliability (Cronbach\u0026rsquo;s alpha) was computed separately for the competence and motivation items; item-total correlations were inspected. We prespecified that a scale alpha \u0026ge;0.70 would indicate acceptable internal consistency.\u003c/p\u003e\n\u003cp\u003eContinuous data are presented as mean\u0026ensp;\u0026plusmn; SD (or median and interquartile range (IQR) for non-normally distributed data). For categorical variables, counts and percentages were reported.\u003c/p\u003e\n\u003cp\u003eNormality was computed for mean competence (continuous) by using the Shapiro-Wilk. Comparisons between groups were performed with a t-test or one-way ANOVA if normality applied; otherwise, a Mann-Whitney U test or Kruskal-Wallis test was used. Categorical associations (e.g., high competence vs prior authorship) were evaluated with\u0026ensp;\u0026chi;\u0026sup2; tests or Fisher\u0026rsquo;s exact test when expected cell counts were less than 5. The correlation between mean competence and mean motivation was calculated using Pearson\u0026rsquo;s r (or Spearman\u0026rsquo;s \u0026rho; for non-normal data (15-17).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main inferential model was a logistic regression with high competence as the outcome. Predictors were: prior research project, prior research fellowship, prior authorship, medix member, age in years, sex,\u0026ensp;training stage (dummy coded), hours per week, and reliable internet. Adjusted odds ratios (aOR), 95% confidence\u0026ensp;intervals and p values are presented. Model fit and diagnostics included: Test of multicollinearity using the variance inflation factor (VIF);\u0026ensp;the presence of a VIF \u0026gt;5 required model revision. Model goodness-of-fit (Hosmer\u0026ndash;Lemeshow test) and residuals and influence points were evaluated. Sensitivity analyses with linear regression using mean competence as a continuous outcome were conducted, excluding imputed data (10,18,19).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA second logistic model with willingness to actively participate as the outcome was fitted with the same predictors and with mean competence added to assess whether competence predicted willingness. Pre-specified subgroup analyses were stratified by gender and training stage to explore potential differences in research competence and motivation, consistent with equity considerations and STROBE guidelines. A p-value of\u0026ensp;\u0026lt;0.05 was considered to be statistically significant using two-sided tests. All estimates are presented with 95% confidence intervals (9,10,18).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eStudy characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Medix Frontiers Research Fellowship received a total of 253 applications. After excluding incomplete or ineligible submissions (e.g., non-UNEC students, graduates, or non-medicine/dentistry applicants), a total of 237 complete responses from medical and dental students of the University of Nigeria Enugu Campus (UNEC) were considered for the final analytic sample. No additional exclusion was applied for missing competence items (\u0026gt;25% threshold), as none of the participants had missing competence items among the 8 competence items. There were no missing values in the cleaned data set (0% for all columns following preprocessing). Thus, the analytic sample size was n=237. This is summarised in Figure 1, the Participant flow diagram\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 1: Participant flow diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDescriptive statistics\u003cbr\u003e\u003c/em\u003eThe sample was comprised\u0026ensp;of 237 undergraduate medical (94.1%) and dental (5.9%) students enrolled at UNEC. The mean age was 21.98 (SD = 2.79). Females accounted for 51.1% (n=121) and males for 49.0% (n=116). The stage of training was distributed as preclinical 16.5% (n=39), basic clinical 18.1% (n=43), and clinical 65.4% (n=155).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was limited previous research experience: 25.7% (n=61) had reported a prior involvement in a research project, 30.0% (n=71) a prior fellowship, and 13.5% (n=32) authorship. Predominant preference was for clinical research (49.8%,\u0026ensp;n=118), followed by public health (24.9%, n=59). Research time commitment per week was \u0026lt;5 hours for 29.1% (n=69), 5 to 10 hours for 54.4% (n=129), 11 to 20 hours for 13.5% (n=32), and \u0026gt;20 hours for 3.0% (n=7). A reliable internet connection was available to 97.5% (n=231), and 99.6% (n=236) expressed a desire to actively participate. This is summarised in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Participant demographics and baseline characteristics (n=237)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"470\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e21.98 \u0026plusmn; 2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMale (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e48.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eFemale (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e51.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCourse of study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMedicine (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e94.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eDentistry (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eTraining stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ePreclinical (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e16.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eBasic Clinical (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e18.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eClinical (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e65.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMedix member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e54.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e45.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePrior research project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e74.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e25.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePrior research fellowship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e70.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e29.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePrior authorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e86.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePreferred research area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eClinical research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e49.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ePublic health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e24.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eData science/analytics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMedical education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eHealth policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHours per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e29.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e5\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e54.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e11\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eWilling active participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e99.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eReliable internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e97.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHigh competence (mean \u0026gt; 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (\u0026le;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e92.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (\u0026gt;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHigh motivation (mean \u0026ge; 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eNo (\u0026lt;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eYes (\u0026ge;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e89.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe competence scale (8 items, Likert 1\u0026ndash;5) demonstrated excellent internal consistency (Cronbach\u0026rsquo;s \u0026alpha; = 0.914). The mean competence was 2.24 (SD 0.78), and item-total correlations ranged from 0.580\u0026ensp;(data analysis) to 0.836 (study design). High competence (\u0026gt;3.5) was observed in 7.2%\u0026ensp;(n=17). The motivation scale (5 items, Likert 1\u0026ndash;5) showed an acceptable internal consistency (Cronbach\u0026rsquo;s \u0026alpha; = 0.636). The mean motivation was 4.62 (SD = 0.49) with 89.9% (n=213) achieving high motivation (\u0026ge;4). Item-total correlations varied from 0.155 (skill improvement) to 0.617 (career). This is summarised in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Competence and motivation scale summaries\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale / Item\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD or Item-total correlation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s \u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompetence scale\u003c/strong\u003e (8 items, Likert 1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMean: 2.24 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.914 (excellent)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_research_question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_literature_search\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_study_design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_data_analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_data_collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_IMRAD_writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_research_interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003eskill_research_ethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMotivation scale\u003c/strong\u003e (5 items, Likert 1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMean: 4.62 \u0026plusmn; 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.636 (acceptable)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003emotivation_skill_improvement (1 = Not important, 5 = Very important)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003emotivation_publication (1 = Not important, 5 = Very important)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003emotivation_mentorship (1 = Not important, 5 = Very important)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003emotivation_career (1 = Not important, 5 = Very important)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 338px;\"\u003e\n \u003cp\u003emotivation_networking (1 = Not important, 5 = Very important)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eItem-total correlation: 0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eBivariate Analyses\u003cbr\u003e\u003c/em\u003eThe mean competence was not normally distributed (Shapiro-Wilk p = 4.57 \u0026times; 10⁻\u0026sup1;⁰),\u0026ensp;and therefore, we conducted non-parametric tests. Competence varied significantly by previous research experience: greater in those with previous projects (Mann-Whitney U = 1934.0, p \u0026lt; 0.001), fellowships (U = 3028.0, p \u0026lt; 0.001), and authorship (U = 1360.0, p \u0026lt; 0.001). No significant differences were found between training levels (Kruskal-Wallis H = 1.498, p = 0.473) or gender\u0026ensp;(Mann-Whitney U = 7895.0, p = 0.095). Figure 2 visually highlights the bivariate difference in competence_mean by prior research project (Yes/No)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 2: Box plot of self-reported research competence mean scores (1\u0026ndash;5 Likert scale) stratified by prior research project experience (Yes vs. No)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigher competence was strongly correlated with previous authorship (Fisher\u0026rsquo;s exact\u0026ensp;p = 3.55\u0026times;10⁻⁷) and membership of Medix (p = 0.004), but not with willingness to participate (p = 1.0). The association between mean competence and mean motivation was weak and non-significant (Pearson r = \u0026minus;0.068, p = 0.298). This is summarised in Table 3\u003cbr\u003e\u0026nbsp;\u003cbr\u003e \u003cstrong\u003eTable 3: Bivariate associations with research competence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Group differences in competence_mean (non-parametric tests due to non-normality)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"442\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eTraining stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePreclinical (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eKruskal-Wallis H = 1.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eBasic Clinical (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eClinical (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eMale (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMann-Whitney U = 7895.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eFemale (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePrior research project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMann-Whitney U = 1934.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePrior research fellowship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMann-Whitney U = 3028.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePrior authorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMann-Whitney U = 1360.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eB. Associations with high competence (categorical outcomes)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"407\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePrior authorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eFisher\u0026apos;s exact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3.55 \u0026times; 10⁻⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eMedix member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eFisher\u0026apos;s exact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eWilling active participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eFisher\u0026apos;s exact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eYes (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eC. Correlation between continuous scales\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"370\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 237px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable pair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson r\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 237px;\"\u003e\n \u003cp\u003ecompetence_mean vs motivation_mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMultivariable analyses\u003cbr\u003e\u003c/em\u003eLogistic regression (outcome: high_competence) converged successfully (n=237). Significant predictors were prior research fellowship\u0026ensp;(aOR = 9.46, 95% CI 1.71\u0026ndash;52.48, p = 0.010) and age (aOR = 1.45 per year, 95% CI 1.16\u0026ndash;1.82, p = 0.001), as they were independently associated with higher odds of high competence. Prior authorship approached significance (aOR=4.10, 95% CI= 0.80\u0026ndash;20.99, p=0.091)\u0026ensp;and Medix membership was borderline protective (aOR = 0.19, 95% CI 0.03\u0026ndash;1.08, p = 0.061). Other predictors (previous project, sex, stage of training, hours/week, reliable internet) were all insignificant. Pseudo R\u0026sup2; = 0.510; LLR p \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eThere was multicollinearity (all VIF\u0026gt;5 for age in years = 40.23,\u0026ensp;training_stage = 14.00, reliable_internet = 27.71, sex = 8.96, hours_per_week_num = 7.32), suggesting caution in interpretation; however, model discrimination was good (AUC= 0.947). Figure 3 summarizes multivariable results via a forest plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 3: Forest plot of adjusted odds ratios (aORs) and 95% confidence intervals for significant and borderline predictors of high research competence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA secondary logistic model of willingness to participate showed quasi-complete separation (99.6% yes responses), producing erratic estimates (with large aORs\u0026ensp;and NaN CIs); therefore, this finding was considered inappropriate for logistic regression. The multivariable logistic regression results and VIF are summarised in Table 4\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Multivariable logistic regression results for high competence (adjusted ORs, 95% CIs, p-values) and VIFs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Logistic regression model (outcome: high_competence \u0026gt; 3.5; n=237)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted OR (aOR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eIntercept (const)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9.34 \u0026times; 10⁻\u0026sup1;⁰\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3.84 \u0026times; 10⁻⁸⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.28 \u0026times; 10⁶⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003ePrior research project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e17.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003ePrior research fellowship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e52.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003ePrior authorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e20.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMedix member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eTraining stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eHours per week (numeric)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eReliable internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e478.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.40 \u0026times; 10⁻⁷⁴\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e9.56 \u0026times; 10⁷⁸\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eB. Variance inflation factors (VIF) for multicollinearity assessment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"210\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003ePrior research project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003ePrior research fellowship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003ePrior authorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMedix member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e40.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eTraining stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eHours per week (numeric)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eReliable internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e27.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA linear regression on continuous competence_mean (more stable) also confirmed positive associations with prior project\u0026ensp;(\u0026beta;=0.544, p\u0026lt;0.001), fellowship (\u0026beta;=0.451, p\u0026lt;0. 001), age (\u0026beta;=0.041, p=0.012) and negative associations with Medix membership (\u0026beta;= \u0026ndash;0.193, p=0.030). R\u0026sup2; = 0.387 (adjusted 0.363), F = 15.95, p\u0026ensp;\u0026lt; 0.001. Linear regression results are summarised in Table 5.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses performed using median imputation (in comparison\u0026ensp;to mean) on the motivation items also resulted in the same value for motivation_mean (4.62 \u0026plusmn; 0.49), validating robustness. An interaction term (prior\u0026ensp;fellowship \u0026times; Medix membership) did not materially alter main effects but showed quasi-separation and instability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Linear regression results for competence_mean (continuous outcome; n=237)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eIntercept (const)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ePrior research project\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ePrior research fellowship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ePrior authorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMedix member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-2.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTraining stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-1.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eHours per week (numeric)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eReliable internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of main findings\u003c/h2\u003e \u003cp\u003eThis descriptive cross-sectional study among 237 medical and dental students of the University of Nigeria, Enugu Campus (UNEC) applying for Medix Frontiers Research Fellowship, revealed a rather surprising disparity between a high level of motivation and low self-reported research competence. The mean competence score was 2.24 (SD 0.78) on a 5-point Likert scale, with 7.2% of participants achieving high competence (\u0026gt;\u0026thinsp;3.5). In contrast, motivation was significant (mean 4.62, SD 0.49) with 89.9%\u0026ensp;scoring\u0026thinsp;\u0026ge;\u0026thinsp;4. Prior research experience, especially prior research fellowships (aOR 9.46, 95% CI 1.71\u0026ndash;52.48, p\u0026thinsp;=\u0026thinsp;0.010) and authorship (aOR 4.10, 95% CI 0.80\u0026ndash;20.99, p\u0026thinsp;=\u0026thinsp;0.091), stood out as the most robust predictor of high competence, in addition to increasing age (aOR 1.45 per year, 95% CI 1.16\u0026ndash;1.82, p\u0026thinsp;=\u0026thinsp;0.001). Medix membership demonstrated a borderline negative association (aOR 0.19, 95% CI 0.03\u0026ndash;1.08, p\u0026thinsp;=\u0026thinsp;0.061).\u003c/p\u003e \u003cp\u003eThere was no significant difference in competence based on training stage (p\u0026thinsp;=\u0026thinsp;0.473) or sex (p\u0026thinsp;=\u0026thinsp;0.095). Also, the correlation between competence and motivation was weak and non-significant (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.068, p\u0026thinsp;=\u0026thinsp;0.298). These results demonstrate that although there is no shortage of motivation among this motivated applicant group, although actual self-perceived skills remain limited, with previous experience being the major driver of competence.\u003c/p\u003e \u003cp\u003eThe absence of a gradient of competence at different stages of training is remarkable. Although participants progressed from preclinical to clinical stage, students reported no substantial improvement in research skills, in a manner that corroborated the finding that the formal curricula of many Nigerian medical schools place more emphasis on clinical training than on research methodology and competence (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Meanwhile, prior experience made a huge difference with regard to levels of competence, highlighting the importance of providing students with early, structured opportunities to develop experiences and skills in question formulation, literature searching, study design, and data interpretation. The borderline negative association with Medix membership might be due to selection effects, as members may be more self-critical or have higher standards, or other unmeasured factors such as competing commitments. This slightly negative correlation between competence and motivation is in line with findings in the literature suggesting that the realisation of skill gaps can paradoxically increase motivation in high-achieving applicants (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cp\u003eThese findings are consistent with larger trends in Sub-Saharan Africa (SSA) and among low- and middle-income countries (LMICs). A national cross-sectional study of Nigerian medical students in 2025 highlighted similar barriers, which included: absence of statistical skills (74.2%), time limitation (73.3%), and limited training in research methodology, among others as the largest barriers to participation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Past research\u0026ensp;from SSA revealed systemic deficiencies in research labs, funding, and skills training across medical schools (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Also, a 2020 study in Lagos, Nigeria, observed similar low participation attributed to a perceived shortage of mentorship and resources (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Around the world, self-rated abilities of undergraduates from LMICs tend to be lower compared to those from high-income settings, which often feature more structured approaches to research (e.g., compulsory projects or electives), ultimately yielding higher baseline skills (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This high motivation observed here mirrors results from LIMC cohorts, where enthusiasm for research is high but not met with institutional support (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eImplications for fellowship design and medical education\u003c/h2\u003e \u003cp\u003eThe implications for medical education, fellowship design, and other similar training and capacity-building initiatives are clear. Curricula need to focus on integrating research training at an early stage, ideally in preclinical years, to close the gap in competencies before clinical studies place further demands on students. Fellowships such as Medix Frontiers Research Fellowship can act as focused interventions, offering guidance, statistical training, and real-world projects, especially for inexperienced students. Early-stage interventions (such as workshops on IMRAD writing, ethics and data analysis) may potentially speed learning and minimise reliance on autodidactic learning. Given the strong predictive role of prior fellowships, scaling such opportunities could create a virtuous cycle of competence and engagement and have a positive feedback effect on competence and engagement.\u003c/p\u003e \u003cp\u003eAlso, selection and early stratification should be based on baseline differences: fellows with less background experience will benefit from an expanded foundational module (covering basic study design, literature searching and introductory data analysis) and fellows with more prior experience can move more quickly toward independent project work and manuscript preparation. The positive association between competence and willingness to participate suggests embedding early, practical tasks that enhance confidence and tangible competencies, which could also increase retention and completion rates. Finally, as reliable internet was identified as pertinent in preliminary results, programmes in resource-limited settings are encouraged to emphasise offline-compatible solutions, robust data-access support, and protected data-use time to counter technological challenges (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths of the study\u003c/h2\u003e \u003cp\u003eThe strengths of the study are that it consisted of a large sample size of fellowship applicants (n\u0026thinsp;=\u0026thinsp;237), the use of structured self-report measures with good (competence α\u0026thinsp;=\u0026thinsp;0.914) and acceptable (motivation α\u0026thinsp;=\u0026thinsp;0.636) reliability, and a thorough multivariable analysis applying both logistic and linear regression to address outcome rarity and multicollinearity issues. Non-parametric tests for non-normal data and sensitivity analyses (e.g., median imputation yielding identical results) contributed to robustness.\u003c/p\u003e \u003cp\u003eAlso, measurement used a brief competence-based instrument that maps onto specific aspects of research (question formulation, literature searching, analysis, IMRAD writing, ethics), and psychometric analyses (internal consistency) were considered a priori. The analysis combined descriptive, reliability, bivariate, and multivariate models to examine the independent predictors of the outcome whilst maintaining interpretability for programme managers.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eLimitations of the study\u003c/h2\u003e \u003cp\u003eLimitations must be acknowledged. Self-reported competence and motivation may lead to an overestimation or underestimation of true ability, influenced by social desirability or lack of objective validation, as objective skill testing (e.g., timed literature searches or data tasks) was not conducted (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The cross-sectional design precludes causal interpretations, e.g., whether prior experience leads to higher competence or vice versa cannot be determined, as associations between prior exposure and competence may reflect reciprocal selection effects (motivated students seek research opportunities). The results are from a single public institution (UNEC) and among highly motivated fellowship applicants and, as such, may not be generalizable to the general Nigerian or SSA student population, as motivation and access might well be lower relative to similar applicant pools.\u003c/p\u003e \u003cp\u003eSelection bias presumably led to an overestimation of motivation and competence relative to non-applicants. Multicollinearity (high VIF for age, training stage, and reliable internet) might have also led to inflated standard errors for some of\u0026ensp;the estimates, although linear regression on continuous competence provided stable insights. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for this secondary analysis of anonymised application data.\u003c/p\u003e \u003cp\u003eFurthermore, there should be caution in generalising findings, as they are most relevant to undergraduate medical and dental students applying to a competitive research training programs within the context of low-resource universities. When extrapolating to a wider student population or other countries, differences in curricula, mentorship availability and\u0026ensp;digital infrastructure should be considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eFuture work should involve a longitudinal follow-up of fellowship\u0026ensp;participants to determine changes in competence and objective research outputs (manuscripts submitted/accepted, conference presentations) and to evaluate which curricular components most strongly predict sustained scholarly activity. Intervention trials contrasting standard versus intensified foundational modules for low-competence\u0026ensp;entrants would clarify causality and inform scalable training packages. Multi-centre studies involving Nigerian and sub-Saharan African institutions are needed to increase generalizability and identify system-level barriers that can be addressed through policy interventions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThough Nigerian medical and dental students are highly motivated, their self-perceived research competence is low and largely influenced by previous research exposure. Early targeted interventions (via fellowships and curriculum reform) will be critical to building capacity\u0026ensp;and filling SSA's research training void.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eThis study involved secondary analysis of de-identified application data collected for the Medix Research Fellowship. All applicants provided informed consent for the use of their anonymized data for research purposes at the point of application. Ethical review and exemption or approval were obtained from the Ethics Committee of the University of Nigeria Teaching Hospital (NHREC/05/01/2008B-FW00002458-1RB00002424). All approaches conformed to the Declaration of Helsinki and the Nigerian National Code of Health Research Ethics before analysis. All data were maintained in compliance with institutional guidelines for confidentiality and data security.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u003c/strong\u003eNot applicable. No individual person\u0026rsquo;s data are presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u003c/strong\u003eThe authors declare that they have no competing interests. All authors confirm that they have no affiliations with, or involvement in, any organization or entity with any financial interest (such as honoraria, educational grants, participation in speakers\u0026rsquo; bureaus, membership, employment, consultancies, stock ownership, patent-licensing arrangements, or expert testimony) or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) that could be perceived to influence the content of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eThis research was conducted under the leadership of the Director of Research \u0026amp; Publications at Medix Frontiers and used only internal resources of the organization. No specific grant or external funding from public, commercial, or not-for-profit agencies was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003cbr\u003e\u003c/strong\u003eThe authors gratefully acknowledge Medix Frontiers for supporting the fellowship and the applicants who participated in the program and provided data for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGodswill Uzoechina (GU) conceptualized the study, designed the analysis plan, performed the data analysis, and drafted the Results and Discussion sections. GU also coordinated overall manuscript preparation, supervised the project, and critically revised all sections for intellectual content. Treasure Osajiuba (TO), Elochukwu Marvellous (EM), and Chinonso Ifudu (CI) managed data collection and contributed to the development of the study instruments. TO drafted the Methods section, EM drafted the Introduction, and CI drafted the Abstract and Conclusion. All authors reviewed, edited, and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmgad M, Tsui MM, Liptrott SJ, Shash E. Medical student research: an integrated mixed-methods systematic review and meta-analysis. PLoS One. 2015 Jun 18;10(6):e0127470. doi: 10.1371/journal.pone.0127470. PMID: 26086391; PMCID: PMC4472353.\u003c/li\u003e\n \u003cli\u003eBurgoyne LN, O\u0026apos;Flynn S, Boylan GB. Undergraduate medical research: the student perspective. Med Educ Online. 2010 Sep 10;15. doi: 10.3402/meo.v15i0.5212. PMID: 20844608; PMCID: PMC2939395.\u003c/li\u003e\n \u003cli\u003eOlajide T, Arokoyo K, Adesola A, Okeke S, Abdullateef R, Anele F, et al. Building a research culture among nigerian medical students: the modus operandi of the college research and innovation hub. BMC Med Educ. 2024 Dec 18;24(1):1465. doi: 10.1186/s12909-024-06518-4. PMID: 39696335; PMCID: PMC11653916.\u003c/li\u003e\n \u003cli\u003eOssai EN, Eze II, Umeokonkwo CD, Izuagba CO, Ogbonnaya LU. Readiness, barriers, and attitude of students towards online medical education amidst COVID-19 pandemic: a study among medical students of Ebonyi State University Abakaliki, Nigeria. PLoS One. 2023 Apr 27;18(4):e0284980. doi: 10.1371/journal.pone.0284980. PMID: 37104470; PMCID: PMC10138982.\u003c/li\u003e\n \u003cli\u003ePascal Iloh GU, Amadi AN, Iro OK, Agboola SM, Aguocha GU, Chukwuonye ME. Attitude, practice orientation, benefits and barriers towards health research and publications among medical practitioners in Abia State, Nigeria: a cross-sectional study. Niger J Clin Pract. 2020 Feb;23(2):129-137. doi: 10.4103/njcp.njcp_284_18. PMID: 32031085.\u003c/li\u003e\n \u003cli\u003eUghasoro MD, Musa A, Yakubu A, Adefuye BO, Folahanmi AT, Isah A, et al. Barriers and solutions to effective mentorship in health research and training institutions in Nigeria: mentors, mentees, and organizational perspectives. Niger J Clin Pract. 2022 Mar;25(3):215-225. doi: 10.4103/njcp.njcp_154_20. PMID: 35295040.\u003c/li\u003e\n \u003cli\u003eOgamba CF, Roberts AA, Ajudua SC, Akinwale MO, Jeje FM, Ibe FO, et al. Perceptions of Nigerian medical students regarding their preparedness for precision medicine: a cross-sectional survey in Lagos, Nigeria. BMC Med Educ. 2023 Nov 17;23(1):879. doi: 10.1186/s12909-023-04841-w. PMID: 37978519; PMCID: PMC10656926.\u003c/li\u003e\n \u003cli\u003eMedix Frontiers. Medix Frontiers: Ut Veritas, Vitam Servet. Enugu: Medix Frontiers; 2024. Available from: https://medix.framer.website/\u003c/li\u003e\n \u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008 Apr;61(4):344-9. doi: 10.1016/j.jclinepi.2007.11.008. PMID: 18313558.\u003c/li\u003e\n \u003cli\u003eRothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams \u0026amp; Wilkins; 2008. 758 p.\u003c/li\u003e\n \u003cli\u003eDaniel WW, Cross CL. Biostatistics: a foundation for analysis in the health sciences. 10th ed. Hoboken: Wiley; 2013. 714 p.\u003c/li\u003e\n \u003cli\u003e12. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003 Oct;88(5):879-903. doi: 10.1037/0021-9010.88.5.879.\u003c/li\u003e\n \u003cli\u003e13. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013 Nov 27;310(20):2191-2194. doi: 10.1001/jama.2013.281053. PMID: 24141714.\u003c/li\u003e\n \u003cli\u003eLittle RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. Hoboken: Wiley; 2002. 381 p.\u003c/li\u003e\n \u003cli\u003eAltman DG. Practical statistics for medical research. London: Chapman \u0026amp; Hall; 1991. 611 p.\u003c/li\u003e\n \u003cli\u003eShapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52(3-4):591-611. doi: 10.1093/biomet/52.3-4.591.\u003c/li\u003e\n \u003cli\u003eKirkwood BR, Sterne JAC. Essential medical statistics. 2nd ed. Oxford: Blackwell Science; 2003. 501 p.\u003c/li\u003e\n \u003cli\u003eHosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken: Wiley; 2013. 552 p.\u003c/li\u003e\n \u003cli\u003eVittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. 2nd ed. New York: Springer; 2012. 509 p.\u003c/li\u003e\n \u003cli\u003eKutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models. 5th ed. New York: McGraw-Hill/Irwin; 2005. 1396 p.\u003c/li\u003e\n \u003cli\u003eKingpriest PT, Okpanachi JA, Afolabi SA, Ayorinde MM, Muoghallu OI, Alapa GE, et al. A national cross-sectional study on research opportunities and barriers among medical students in Nigeria, with recommendations. BMC Med Educ. 2025 May 16;25(1):713. doi: 10.1186/s12909-025-07308-2. PMID: 40380171; PMCID: PMC12085030.\u003c/li\u003e\n \u003cli\u003eAdebisi YA. Undergraduate students\u0026apos; involvement in research: values, benefits, barriers and recommendations. Ann Med Surg (Lond). 2022 Aug 17;81:104384. doi: 10.1016/j.amsu.2022.104384. PMID: 36042923; PMCID: PMC9420469.\u003c/li\u003e\n \u003cli\u003eNyarko OO, Ansong D, Osei-Akoto A, Konadu SO, Opoku G. Developing research competencies of undergraduate medical students in Sub-Saharan Africa. WJMER. 2019;21(1):16-18. Available from: https://www.wjmer.co.uk/library/downloads/articles/1578655415.pdf\u003c/li\u003e\n \u003cli\u003eAwofeso OM, Roberts AA, Okonkwor CO, Nwachukwu CE, Onyeodi I, Lawal IM, et al. Factors affecting undergraduates\u0026apos; participation in medical research in Lagos. Niger Med J. 2020 May-Jun;61(3):156-162. doi: 10.4103/nmj.NMJ_94_19. Epub 2020 Jul 4. PMID: 33100468; PMCID: PMC7547749.\u003c/li\u003e\n \u003cli\u003eChang Y, Ramnanan CJ. A review of literature on medical students and scholarly research: experiences, attitudes, and outcomes. Acad Med. 2015 Aug;90(8):1162-73. doi: 10.1097/ACM.0000000000000702. PMID: 25853690.\u003c/li\u003e\n \u003cli\u003eAlduraibi KM, Aldosari M, Alharbi AD, Alkhudairy AI, Almutairi MN, Alanazi NS, et al. Challenges and barriers to medical research among medical students in Saudi Arabia. Cureus. 2024 May 2;16(5):e59505. doi: 10.7759/cureus.59505. PMID: 38826878; PMCID: PMC11144033.\u003c/li\u003e\n \u003cli\u003eEzeugo NC, Eleje LI, Obiasor GE, Mbelede NG, Osonwa KE, Metu IC, et al. Self-assessment techniques in clinical studies in public universities in Anambra State: benefits and alignment with supervisors evaluation as perceived by medical students. J Med Educ Curric Dev. 2024 Dec 25;11:23821205241308787. doi: 10.1177/23821205241308787. PMID: 39777253; PMCID: PMC11705325.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Undergraduate research training, Medical and dental education, Research competence assessment, Nigeria, Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-8740580/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8740580/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResearch skill acquisition is critical for undergraduate medical and dental students, but deficiencies in knowledge and motivation persist in low-resource environments such as Nigeria. Self-perceived research competence, motivations, and preparedness among applicants to a highly competitive research fellowship were evaluated, and predictors of high competence were identified, to provide information for capacity-building.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cbr\u003e\nA descriptive cross-sectional analysis of 237 anonymized applications from Years 2 to 6 medical and dental students of the University of Nigeria Enugu Campus (UNEC) to the Medix Frontiers Research Fellowship (January–February 2026) was performed. Demographics, previous research experience (yes, no), competence (8 Likert items from 1 to 5), motivation (5 Likert items from 1 to 5), and preparedness (eg, hours/week, willingness) were assessed. Calculated variables: competence_mean (mean competency scores), motivation_mean (mean motivation scores), high_competence (\u0026gt;3.5), high_motivation (≥4). Cronbach’s α was used to evaluate reliability. Descriptive statistics: Means ± SD, proportions (%). Bivariate: Non-parametric tests (non-normal distribution data), Chi-square/Fisher’s exact test, Pearson’s correlations. Multivariable: Logistic regression (Dependent: high_competence; Independent: previous experience, Medix member, age, sex, stage of training, hours/week, dependable internet); linear regression for continuous competence_mean. No data were lost to follow-up. Analyses in Python (Pandas, SciPy, Statsmodels).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cbr\u003e\nMean age 21.98 years (SD 2.79); 51.1% female; 65.4% clinical stage. Previous experience low (25.7% projects, 30.0% fellowships, 13.5% authorship). Competence_mean 2.24 (SD 0.78; α=0.914); high_competence 7.2%. Motivation_mean 4.62 (SD 0.49; α=0.636); high_motivation 89.9%. Competence was significantly different by previous experience (p\u0026lt;0.001 all) but not by stage of training (p=0.473) or gender (p=0.095); motivation was not correlated with competence (r=−0.068, p=0.298). Logistic regression: previous fellowship (aOR 9.46, 95% CI 1.71–52.48, p = 0.010) and age (aOR 1.45/year, 95% CI 1.16–1.82, p=0.001) predicted high_competence; multicollinearity validated (VIF \u0026gt;5 among some). Linear model confirmed associations (R²=0.387). Near-universal willingness (99.6%), too poor for regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eHigh motivation is in stark contrast to low competence and is driven by prior exposure. Fellowships could focus on early learning to provide foundational experiences and inform curricula in resource-limited environments.\u003c/p\u003e","manuscriptTitle":"Research Preparedness, Competence, and Motivations Among Undergraduate Medical and Dental Students in Nigeria: A Cross-Sectional Study of a Competitive Research Fellowship Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 18:13:08","doi":"10.21203/rs.3.rs-8740580/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-24T05:52:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T18:58:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T18:30:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T20:57:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65443386899388017829677441989319084339","date":"2026-03-06T20:52:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123622304890242519615362338938340047197","date":"2026-03-04T07:31:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106079818017357399644715267652555291744","date":"2026-03-04T07:15:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325123946112444411434214790660631393409","date":"2026-03-02T20:52:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T05:52:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T10:23:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T05:26:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T05:00:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-02-05T04:52:37+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":"5cc7e9dd-5dbc-4549-acaf-875adb3f2e67","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:07:39+00:00","versionOfRecord":{"articleIdentity":"rs-8740580","link":"https://doi.org/10.1186/s12909-026-09134-6","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2026-04-02 15:58:03","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2026-03-02 18:13:08","video":"","vorDoi":"10.1186/s12909-026-09134-6","vorDoiUrl":"https://doi.org/10.1186/s12909-026-09134-6","workflowStages":[]},"version":"v1","identity":"rs-8740580","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8740580","identity":"rs-8740580","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}