Evaluating Speciality Preferences and Career Choices Among Nigerian Medical Students, Interns, and Preresidency Doctors: A Systematic Review and Meta-Analysis

Evaluating Speciality Preferences and Career Choices Among Nigerian Medical Students, Interns, and Preresidency Doctors: A Systematic Review and Meta-Analysis | 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 Evaluating Speciality Preferences and Career Choices Among Nigerian Medical Students, Interns, and Preresidency Doctors: A Systematic Review and Meta-Analysis Godswill Uzoechina, Chukwuemelie Obidike, Treasure Osajiuba, Victor Igwesi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8470357/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Specialty selection among medical trainees directly impacts service provision capacity and future clinical workforce in Nigeria, with low physician density and uneven specialty distribution. We synthesised the literature to quantify specialty preferences, timing of decision-making, and the determinants across the different stages of training. Methods Adhering to PRISMA guidelines, a systematic review and meta-analysis of primary quantitative studies involving Nigerian medical students, interns/house officers and pre-residency doctors was performed. PubMed, Google Scholar and hand-searches were conducted to 2025. Eligible cross-sectional surveys that reported the determinants or preferences for specialisation were considered. Quality of study was assessed using AXIS. For meta-analysis, we pooled specialty-specific proportions using random-effects models with Freeman–Tukey double‐arcsine transformation and evaluated heterogeneity using I². Predefined subgroup analyses investigated training stage, region, decade, and study-level gender composition. Results Twenty studies (1992–2025) met the inclusion criteria (pooled participants > 4,000); 14 studies contributed to pooled proportions. Surgery was the most preferred specialty (pooled 28.5%, 95% CI 24.0–33.3%; n ≈ 3,127; I²=88.6%). Obstetrics & Gynaecology 18.1% (95% CI 14.3–22.3%); Paediatrics 13.8% (95% CI 10.1–18.0%); Internal Medicine 11.8% (95% CI 9.3–14.6%). Low-preference fields included Radiology (1.5%, 95% CI 0.6–2. 9%) and Pathology (1.8%, 95% CI 0.9–3.0%). Subgroup analysis: Surgical preference was higher in undergraduates (Tier-1 pooled ≈ 33.0%) compared with interns (Tier-2 ≈ 22.1%). Time: the weighted pooled proportion indicated 57.5% decided during clinical rotations/final years. Determinants (pooled/proxy): personal interest 75.1%, clinical rotations influence ~ 62.2%, work–life considerations ~ 64.2%; ~11–13% remained undecided. Only ~ 34% (in studies reporting guidance; n = 1,453) received any form of career counselling. Between-study heterogeneity was high; most studies had moderate RoB. Conclusions Medical trainees in Nigeria strongly prefer surgical and core clinical specialties, with interest in diagnostic/non-clinical specialties remaining persistently low. The majority of choices consolidate during clinical years and are mainly influenced by personal interest, exposure and perceived prospects. Interventions, including earlier structured career guidance, enhanced exposure and speciality-specific mentorship for under-selected specialities, are required to rebalance the workforce imbalance. Medical specialty preference Nigeria Workforce distribution Career guidance Health workforce planning Figures Figure 1 Figure 2 Figure 3 Introduction The strength of the healthcare team is deeply rooted in the diversity of services provided. This diversity is lost with the uneven distribution of manpower throughout various medical specialties. The burden of chronic non-communicable diseases (like hypertension) in Nigeria has been steadily increasing (1). The complications of these multifactorial illnesses are widespread and frequently involve multiple systems in the body, and this calls for multidisciplinary management. In situations where the uneven distribution of medical manpower is significant, the quality of care that these patients can receive becomes significantly reduced. With hypertensive emergencies often being life-threatening, the urgency for healthier distributions of medical manpower cannot be overemphasised. With males being more heavily affected than females (1), the economic implications of the lack of specialist care with respect to this disease can significantly reduce the standard of living of affected families. With a specialty imbalance in place, complications are often fatal. The physician density is alarmingly low in Nigeria; even though over 74,500 doctors are registered in the country, only a small proportion are practising, resulting in a real physician-to-population ratio of about 2.9 per 10,000 people, which is far below the World Health Organisation (WHO)-recommended thresholds (2,3). This scarcity is exacerbated by maldistribution, with the rural and northern areas being particularly neglected, alongside a constant brain drain of specialists, widening the rift in service coverage (3,4). Against this background, the choice of specialty among medical students and early-career doctors is of strategic concern. Decisions made in medical school and during the year of internship effectively govern the trajectory into graduate medical training. These preliminary decisions shape not only which disciplines trainees enter but also the geographical distribution of specialists, and in turn affect national capacity in critical areas such as surgery, maternal and child health, and preventive medicine. Recent preliminary studies indicate that Nigerian medical trainees are strongly drawn to a limited number of clinically “prestigious” specialties. In 2024–2025, a cross-sectional survey of 439 penultimate and final year medical students, 40.7% of the students considered core surgical specialties (including subspecialties) as their top choice (5). Obstetrics & Gynaecology was the second most common choice (14.7%), and there were significant gender and class-year variances: for instance, 10.6% of penultimate-year students had chosen O&G, compared to 18.9% final year; males were more likely to choose cardiothoracic surgery (85.7% of those who did so) than females (5). Worryingly, 13.3% of the respondents to that very survey were still undecided on their preferred specialty (5). The situation is similar according to other surveys. Among medical students who are in their final year and house officers (n = 760), the most popular specialties were surgery (26.6%), internal medicine (14.5%), and obstetrics & gynaecology (14.3%), and only 3.6% planned to specialise in anaesthesia, which is an area with considerable workforce shortages (6). A study in Southeast Nigeria (n = 457), discovered that the majority of the students decided their specialty preference during clinical rotations (52% deciding during the clinical years), and only a quarter (25.4%) had some sort of career counseling; 24% opted for surgery, 18.8% for paediatrics, 15.6% for O&G, and 11% for internal medicine (7). Gendered trends were pronounced: male students were far more likely than females to select surgery (32.3% vs 13.0%, p < 0.001), and female students more frequently selected paediatrics (28.8% vs 11.2%, p < 0.001) (7). Determinants of specialty choice tend to be similar and largely depend on personal interest in the subject and anticipated future earnings. According to a 2025 study in BMC Medical Education, 87% of students are attracted to their specialty preference due to personal interest and 85.3% by the intellectual content of the specialty; 77% rated potential lucrativeness as influential (5). Work-hour expectations, shorter training period, and desired practice settings were other characteristics that were significantly correlated with specialty selection (p < 0.001 for several of them) (5). In anaesthesia, students frequently named a lack of mentor exposure and insufficient clinical rotations as demotivators (6). Despite these insights, the literature remains fragmented, with studies primarily single-institution based. The studies span a long period, across decades (from the early 2000s to the present), with the sampling of different groups (undergraduates, house officers, interns), alongside geographical clusters (4,5,7). The hyper specificity of these findings makes it a poor predictor of national and even regional patterns with respect to the choice of specialties among medical students, interns, and pre-residency physicians. A comprehensive review of the various indicators influencing the specialty choices among medical trainees is therefore crucial. A systematic review and meta-analysis would both address the issue of the limited perspective provided by the fragmented and single institution studies, and provide a more reliable qualitative and quantitative backbone for the understanding of current patterns and trends, as well as provide a framework for making possible predictions of how the landscape could look in future. Objectives This study aims primarily to identify the current distribution of first-choice specialty preferences among medical trainees in Nigeria. Secondary objectives are to (a) identify when specialty decisions are made or are likely to be made (early or late in training), (b) assess and rank in importance, the factors influencing choice (interest, income, mentorship, work-life balance), and (c) explore geographic (regional), gender, and temporal variations in these patterns. In this holistic summary, we hope to highlight critical areas for improvement in workforce planning, targeted career counselling, and potentially national educational and policy interventions to better match trainee interests and national health priorities. Methods Study design and reporting This study was conducted as a systematic review of primary quantitative studies examining medical specialty preferences and their determinants among medical students and early-career doctors in Nigeria. The review focused on cross-sectional and descriptive survey studies conducted in Nigerian medical schools, teaching hospitals, and internship or residency settings. The review followed a predefined methodological framework and was reported in accordance with PRISMA 2020 guidelines (8). Eligibility criteria Eligibility criteria were defined a priori based on population, study design, and relevance to specialty preference outcomes. Studies were eligible if they met all of the following conditions: Population: Nigerian medical students (preclinical, clinical, final-year), interns/house officers, or pre-residency doctors. Study focus: Assessment of medical specialty preference, choice intention, or career interest, including influencing factors (e.g., personal interest, lifestyle, income, role models, training exposure). Study design: Primary quantitative studies, predominantly cross-sectional surveys, including institutional studies and multicenter Nigerian studies. Outcomes: Studies reporting data on medical specialty preference were eligible for inclusion. Setting and region: Conducted in Nigeria with a clearly stated institutional or geographic context. Language: Published in English. Studies were excluded if they were: Reviews, editorials, commentaries, or opinion pieces; Qualitative-only studies without extractable quantitative results; Conducted outside Nigeria or using non-Nigerian populations; Lacking sufficient detail on specialty preference outcomes or study population. Information sources Relevant studies were identified from indexed biomedical databases, open-access journal platforms, and institutional repositories. Sources included PubMed, Google Scholar, and journal websites hosting Nigerian medical education research. Reference lists of eligible studies were also screened to identify additional relevant publications. Search strategy Search strategies combined keywords related to medical education, specialty choice, and Nigeria using Boolean operators. Search terms were modified to suit the syntax of the specific database. PubMed searches were conducted by using a combination of Medical Subject Headings (MeSH) terms and free-text keywords through the use of Boolean operators, truncation and field tags. The searches were performed in Google Scholar using simple keyword strings without controlled vocabulary, applying phrase searching, and including the Boolean operators to increase sensitivity. Search was limited to and focused on medical students, house officers and pre-residency doctors in Nigeria, the choice of specialty/preference, the determinants, and the timing of career decision. No filters for study design were applied to allow for comprehensive capture of both cross-sectional and descriptive studies. Table 1 summarises the database-specific search strategies for PubMed and Google Scholar. Table 1: Database-specific search strategies Database Search components Search terms/syntax PubMed Population "Students, Medical"[MeSH] OR "Medical Students"[tiab] OR "Final year medical students"[tiab] OR "House Officers"[tiab] OR Interns[tiab] OR "Junior doctors"[tiab] Concept (career/specialty choice) "Career Choice"[MeSH] OR "Specialty Choice"[tiab] OR "Specialty Preference*"[tiab] OR "Residency Choice"[tiab] OR "Postgraduate training"[tiab] Outcome/determinants/timing preference*[tiab] OR choice*[tiab] OR determinant*[tiab] OR factor*[tiab] OR "career decision"[tiab] OR "timing of decision"[tiab] Setting "Nigeria"[MeSH] OR Nigeria[tiab] Combined strategy (Population) AND (Concept) AND (Setting) AND (Outcome terms) Google Scholar Population "medical students" OR "final year medical students" OR "house officers" OR interns Concept "specialty choice" OR "specialty preference" OR "career choice" Setting Nigeria Core search string ("medical students" OR "final year medical students" OR "house officers" OR interns) AND ("specialty choice" OR "specialty preference" OR "career choice") AND Nigeria Refinement (iterative) ("specialty choice" OR "specialty preference") AND ("medical students" OR "house officers") AND Nigeria AND (determinants OR factors OR "career decision") Study selection A total of 2,935 records were identified through database searches, comprising 2,900 records from Google Scholar and 35 records from PubMed. Prior to screening, 587 duplicate records were removed, leaving 2,348 unique records for title and abstract screening. During the initial screening phase, 1,927 records were excluded for failing to meet the predefined eligibility criteria. The remaining 421 reports were sought for full-text retrieval. Of these, 374 reports could not be retrieved due to inaccessibility, incomplete reporting, or unavailable full texts. Consequently, 47 full-text articles were assessed for eligibility. Following detailed evaluation, 27 reports were excluded for the following primary reasons: Wrong study population (n = 11) Lack of extractable specialty-preference outcomes (n = 9) Duplicate cohorts (n = 7). Ultimately, 20 studies met the inclusion criteria and were included in the qualitative synthesis. Of these, 14 studies provided sufficient quantitative data to be included in the meta-analysis. The full study selection process is summarised in the PRISMA 2020 flow diagram (Figure 1). Data extraction Data were extracted manually from included studies and subsequently organised and compiled in Table 2. Extracted variables corresponded directly to the spreadsheet columns and included: Bibliographic details: Author(s) and year of publication Study design: Cross-sectional, descriptive, or multicenter survey Study setting: Medical school, teaching hospital, internship level Geographic region: Nigerian geopolitical zone or specific state Population studied: Level of training (preclinical, clinical, final year, intern, preresident) Sample size and demographics: Mean age, gender distribution Top preferred specialties Least preferred specialties Undecided proportion, where reported Factors influencing specialty choice: Personal interest, lifestyle, income, clinical exposure, mentorship, family influence, etc. Method of data collection: Self-administered or interviewer-administered questionnaires Key findings Study strengths and limitations Risk-of-bias indicators All extracted data were checked for consistency and accuracy prior to synthesis Risk of bias and quality assessment Risk of bias was assessed using the AXIS tool for cross-sectional studies. The AXIS tool assesses study quality across domains, including participant selection, sampling strategy, non-response, measurement of outcomes, handling of missing data, selective reporting, and consideration of confounding factors (9). Each study was assessed across these domains and categorised as having low risk of bias, moderate risk of bias, or insufficient information, with overall judgments derived from the cumulative assessment of domain-level concerns. Risk-of-bias assessments were not used as exclusion criteria. Instead, AXIS ratings were incorporated into sensitivity analyses and meta-regression to examine the robustness of pooled estimates. The distribution of risk-of-bias judgments is presented using a traffic-light plot and summary plot. (Figures 2 and 3). Data synthesis Data synthesis followed a predefined approach agreed upon before analysis. After data extraction, studies were first reviewed descriptively, paying attention to differences in study population, stage of training, geographic setting, and reported specialty preferences. Outcomes were then examined to determine whether they were suitable for quantitative pooling, taking into account the availability of usable prevalence data, how specialties were defined and grouped across studies, and whether sample sizes were sufficient for meaningful aggregation. Outcomes that could not reasonably be pooled quantitatively, such as when career decisions were made, the availability of career guidance, and factors influencing specialty choice, were synthesised narratively. These results were grouped thematically and examined across different stages of training and institutional settings to highlight recurring patterns and areas of convergence. For outcomes where data were sufficiently comparable, quantitative synthesis was performed using meta-analytic techniques, with specialty-specific preference proportions pooled as detailed in the statistical analysis sections. Given the expected variation between studies, random-effects models and stratified subgroup analyses were planned, pooling prevalence estimates and accounting for clinical and methodological heterogeneity (I 2 ) across studies. Pooled prevalence estimates were calculated for individual preferences across medical specialties. Prevalence data were transformed using the Freeman-Tukey double-arcsine transformation to stabilise discrepancy before pooling, with back-transformation applied for presentation of results (10). Among the studies included, statistical heterogeneity was assessed using the I 2 statistic (11). Subgroup analyses were performed by trainee population (undergraduate, house officer/intern, preresidency), training stage (entry, final-year, internship), geographic zone (South-West, South-East, North-Central, North-West, multi-zone), decade of publication (≤ 2000, 2001–2010, 2011–2020, 2021–2025), and study-level gender composition (male-majority, female-majority, unknown). Sensitivity analysis was performed to assess the robustness of pooled estimates. This included repeating analysis after excluding studies that have a high risk of bias using the AXIS tool for cross-sectional studies (9). In addition, a leave-one-out sensitivity analysis was conducted by sequentially excluding individual studies to evaluate the influence of single studies on the overall pooled estimates (9). Decisions about whether an outcome was analysed quantitatively or narratively were based on data suitability alone and were not influenced by the size or direction of the reported findings. Data management The extracted tables were used to guide descriptive comparison across studies and to structure both narrative synthesis and subsequent quantitative analyses. Data management and analyses were conducted without the use of automated extraction tools. Certainty/strength of evidence A formal certainty-of-evidence assessment (e.g., GRADE) was not conducted. This decision was based on the observational cross-sectional design of all included studies, substantial heterogeneity across outcomes, and variability in outcome definitions and reporting formats. Consequently, findings are interpreted within the context of these methodological limitations, with emphasis placed on consistency of direction, subgroup patterns, and sensitivity analyses. Ethics This review relied exclusively on previously published data. No new data were collected from human participants, and ethical approval was therefore not required. Deviations from protocol (if any) No deviations from the predefined methodological framework were made. Results Study selection The electronic database query, combined with additional hand searching, yielded a total of 2,935 records. 2348 titles and abstracts were later screened for relevance, after the removal of 587 duplicates. Of these, 47 full-text articles were retrieved, and 374 were not able to be retrieved. Twenty (20) full-text articles were considered eligible, as they met the predefined inclusion criteria, and were included in the qualitative synthesis. Of these,14 studies reported sufficient quantitative data to be included in the meta-analysis of speciality preference proportions. Full-text exclusions were based on an ineligible study population, no speciality-specific preference data that could be extracted, and duplicate/overlapping cohorts. The PRISMA flow diagram summarises the study selection process (Figure 1) Characteristics of included studies The time of publication of all the included studies ranged from 1992 to 2025, with the number of participants from all studies totalling more than 4,000 medical trainees in Nigeria. All were cross-sectional studies, using a structured or semi-structured questionnaire to evaluate specialty preference and related determinants. The mode of data collection also varied; we observed that most of the earlier studies were conducted using paper-based questionnaires, administered physically, while more recent studies were conducted via online platforms, including Google Forms and Qualtrics, often distributed to potential respondents through institutional email or social media networks (6,12–13). The study population varied from preclinical students, clinical (final year) students, house officers/interns, to medical graduates not enrolled in residency programs, covering the entire medical training continuum. The most frequently studied cohort was final year medical students, highlighting their proximity to career decision-making (5,6,14). Several studies also focused specifically on interns or house officers to capture preferences at the crucial juncture between undergraduate training and postgraduate education (15-16). The studies were conducted in the six geopolitical zones of Nigeria; however, there was disproportionate and uneven representation. The majority of the included samples were recruited from the South-West and South-East zones, particularly from Lagos, Ibadan, Enugu and affiliated teaching hospitals (5,14,17). Conversely, fewer institution-based studies and relatively smaller sample sizes were found in the North-West and North-Central zones (18,19). Across studies, male participants were dominant, especially among intern and postgraduate cohorts, with male proportions ranging from approximately 52% to over 75% in several studies (16,19,20). Among undergraduate cohorts, females were more prevalent, but remained below parity in most samples. The reported mean age, or median age, increased with training level, from the early twenties among undergraduate students to the early thirties among pre-residency doctors, consistent with the anticipated progression of medical training (5,16). Overall, notwithstanding methodological imbalance and regional diversities, the included studies provided a broad, comprehensive and temporally sound overview of speciality preference patterns among Nigerian medical trainees. Table 2 summarises the characteristics of included studies. Table 2: Summary of Included Studies # Study (year) Region (geo-zone) Population (stage) n Age (mean ± SD/median) Gender (M / F) Top preferred specialty (%, n) Undecided (%) Top 3 determinants (as reported) Timing of decision (modal) Career guidance (% reported having) RoB 1 Olajide T et al. (2025) Multi-zone (SW 48.1% dominant) Penultimate & final-year med students 439 23.62 ± 2.95 233 / 199 (6 prefer not to say; 1 non-binary) Surgery 40.7% (n=179); O&G 14.7% (n=65) 13.3% (n=58) Personal interest (87%); specialty content (85.3%); practice setting (80.8%) Clinical/penultimate–final years 29.9% had guidance (70.1% had none pre-entry) Moderate 2 Okonkwo TC et al. (2024) Multi-zone (SW 36.3% dominant) Final-year students & House Officers 760 25.84 ± 2.81 413 / 347 Surgery 26.6%; Internal Med 14.5%; O&G 14.3% 11.2% undecided; 2% no intention to specialise Passion/interest; flexibility; potential income; role-model effect Clinical rotations (~63.95% decided) ~35.3% reported having received guidance Moderate 3 Ossai EN et al. (2016) South-East (all 6 med schools) Final-year med students 457 25.5 ± 2.9 NR (57.1% male reported) Surgery 24.0%; Paediatrics 18.8%; O&G 15.6% 11.2% Personal interest; lecturer/student interactions; clinical exposure Clinical rotations (51.8%) 25.4% had some guidance (74.6% none) Low 4 Eze BI et al. (2011) South-East (Enugu) Preresidency graduates (post-internship) 287 33.5 ± 1.1 219 / 68 O&G 22.6%; Surgery (combined) 19.6%; Paediatrics 16.0% 2.8% Personal interest (66.6%); career prospects; self-appraisal By the end of PGY-5 (97.2% decided) NR Moderate 5 Onyemaechi NOC et al. (2017) South-East (Enugu) Final-year undergraduates 152 25.8 ± 2.5 110 / 42 Surgical specialties 52.0% (n=79); O&G 14.5% 5.3% Personal interest (88.8%); competence; career prospects The majority final year (42.1% chose in final year) Limited (institutional guidance reported low) Moderate 6 Falase B et al. (2022) South-West (Lagos) Final-year students (two cohorts) 141 25.3 ± 2.0 74 / 67 Surgery 29.2%; O&G 17.4%; Paediatrics 14.2% 24.8% did not want to specialise; 9.9% undecided about practice Enjoyed rotation (62.3%); job/financial rewards (54.7%); work–life balance (50.0%) Consolidated during clinical/final year No formal/institutional guidance emphasised Moderate 7 Asani M.O. et al. (2016, Kano) North-West (Kano) Final-year students 67 Median 27 (24–44) 47 / 20 O&G 28.5% (n=19); Surgery 20.9% (n=14) NR Personal interest; perceived patient-outcomes; teacher influence At graduation (final-year) Not reported (formal guidance uncommon) Moderate 8 Okonta K.E. et al. (2015) Multi-zone (4 tertiary hospitals) House officers 129 22.4 yrs (mean) 79 / 50 O&G 26.3% (n=34); Surgery 21.7% (n=28) 5.4% (n=7) Personal interest (46.7%); role models (11.3%); expected financial reward (9.3%) During internship Teachers/residents/parents cited as influencers (no pooled %) Moderate 9 Odusanya OO et al. (2000) South-West (Lagos) Pioneer medical students (entry cohort) 52 23.6 ± 3.8 NR O&G 28.6%; Surgery 25.7% NR Primary interest (91%); desire to serve humanity (91%); job satisfaction (85.7%) Intent evident at entry NR Moderate 10 Akinyinka MR et al. (2017) South-West (Lagos) New intake (200-level) students 65 Majority 20–29 yrs Approx balanced Surgery 47.3% NR Interest/love for specialty (89.1%); love of profession (82.1%) Early (preferences at entry) NR Moderate 11 Ashipa T. et al. (2017, Babcock) South-West (Ogun) First-year medical students 51 M 18.3 ± 2.03; F 17.5 ± 1.20 18 / 33 Surgery 43.1% (n=22); O&G 13.7% 13.7% undecided (intent) Personal interest (73.2%); self-fulfilment; role models Early (at entry) No formal institutional guidance Moderate 12 Ohaeri J.U. et al. (1993) South-West (Ibadan) Interns/house officers 51 NR 38 / 13 Surgery most popular; core clinical specialties are 95.7% of decided ≈5.9% (3/51) Clinical postings; self-fulfilment; many decided pre-university 43% had decided before university; choices evolved during training No formal counselling reported Moderate 13 Odusanya O.O. & Alakija W. (1995) South-West (Lagos) House officers 51 NR 38 / 13 Core clinical specialties dominated (95.7% of those decided); Surgery most popular ~5.9% (3/51) Clinical exposure/rotations; personal interest 43% decided before entry; others during clinical/internship No formal guidance reported Moderate 14 Madu AJ et al. (2014) South-East (UNTH, Enugu) Fresh house officers/interns 110 Median 26 (22–40) 69 / 41 Surgery 26.4% (29/110); Paediatrics 25.7% 12.8% (14/109) Personal interest (78.9%); job satisfaction; career prospects Preferences captured during internship No formal institutional counselling reported Moderate 15 Adeboye A. et al. (2006) North-Central (Ilorin) Medical interns 76 NR NR Surgery 21.0% (n=16); Paediatrics 18.4% NR Perceptions of specialty interest; short rotation duration cited as barrier (ophth). During internship No formal guidance reported Moderate 16 Abioye I.A. et al. (2012) South-West (Lagos) Senior students & interns 177 NR NR Surgery topped the list; ~55% likely to choose trauma-related specialties NR Role models/mentoring; theatre experience; financial reward Late undergraduate/internship Not specifically reported Moderate 17 Ohaeri J.U., Akinyinka O.O., Asuzu M.C. (1992) South-West (Ibadan) First-year clinical & final-year clinical students (combined) 271 NR NR Surgery most popular; >81% preferred core clinical specialties 7.1% Clinical postings; many had pre-entry preferences (42.9%); choices evolved Many chose before entry (42.9%); evolved across years No formal counselling Moderate 18 Odusanya O.O., Nwawolo C.C. (2001) South-West (Lagos) House officers / Interns 105 NR NR Surgery 18.1%; O&G 18.1%; Dental sciences 10.5% 17.1% did not indicate a wish to specialise in qualifying Interest (72.4%); job satisfaction (67.6%); bright prospects (54.3%) The majority had a choice as undergraduates; some changed by internship No formal guidance reported Moderate 19 Imediegwu KU et al. (2022) South-East (Enugu universities) Final-year med students 132 NR 57% / 43% Orthopaedics interest 27.0% (n≈35) — study focused on ortho NR Clinical rotations (88.3% awareness); lack of mentorship major barrier Final-year (late undergraduate) 3.1% cited the mentor as the source for ortho awareness Moderate 20 Ezegwui C.O. et al. (2022) South-West (Ibadan) Final-year med students 236 23.6 ± 1.9 NR O&G 22.9% (n=54); Surgery 18.6% (n=44) NR Personal interest (78.4%); significant for O&G/family med/public health Final-year (MS-Final) 47.5% reported having received any career counselling Moderate Risk of bias assessment Risk of bias (RoB) was evaluated using the AXIS tool for cross-sectional studies, which assesses the quality of studies in the following domains: selection of participants, non-response, measurement of outcome, missing data, and selective reporting and confounding (9). Overall, most of the included studies were judged to have some concerns (moderate risk of bias). The most common sources of bias were related to the selection of participants and non-response, reflecting the dominance of convenience sampling; recruitment at a single institution and the dissemination of surveys online, which may limit the generalisability and representativeness of the target population (5,6,18). Concerns related to the measurement of outcomes and selective reporting, and confounding were also prevalent. Although speciality preference was self-reported using structured questionnaires, a limited number of studies did not explicitly account for potential confounding variables or demonstrate instrument validation, thus leaving some residual uncertainty in these areas (5,6,13,18), while older studies provided limited methodological detail on outcome measurement (12,13). A smaller proportion of studies demonstrated an overall low risk of bias, especially those that utilised near census or whole population sampling, reported high response rates and reported outcomes thoroughly with minimal missing data (7). The majority of the studies demonstrated a low risk of bias with respect to the missing outcome data domain, although some older reports did not contain enough information to allow for a definitive judgement. None of the studies were excluded solely based on risk of bias. Rather, AXIS evaluations were included in the sensitivity analysis and meta-regression to evaluate the robustness of pooled estimates to study quality. Table 3 summarises RoB of the included studies using AXIS, while Figures 2 and 3 visualise the RoB of the included studies into a summary plot and a traffic light plot, respectively. Discussion Principal findings In this systematic review and meta-analysis, our findings indicate a sustained dominance of surgical and core clinical specialty choices among medical trainees in Nigeria. Across all subgroups analysed (training stage, geopolitical zone, decade of publication and gender), surgery remained the predominant specialty choice. Obstetrics and gynecology was consistently the second most preferred choice of specialty across decades of publication, with pediatrics and internal medicine also featuring markedly. Despite notable heterogeneity, specialty preferences with respect to direction and ranking remained stable across subgroups and sensitivity analysis. On the other hand, non-clinical and diagnostic specialties were constantly underrepresented as first-choice options and attracted minimal interest across studies. The consistency of the low pooled estimates indicates a genuine lack of interest/preference in nonclinical and diagnostic specialties rather than statistical bias. Comparison with other countries The findings from our study are broadly consistent with the findings from previous meta-analyses conducted in Sub-Saharan Africa (SSA). In a study by Bajuniwre et al assessing specialty preference among medical students in SSA, surgery was indicated to be the most preferred choice of specialty across medical trainees with obstetrics and gynecology, internal medicine and pediatrics (28). Following similar findings, this trend was also observed in a study by Khamees et al, with surgery being the most preferred choice (29). Our findings were not consistent with the study from Togo by Teclessou et al , where medical specialties were the preferred choice, followed by surgical specialties and then obstetrics and gynecology. This difference in specialty choice can be attributed to the lack of funding and the limited choice of specialties in that region (30). Another study by Musa et al involving Zambian medical students found internal medicine to be the most preferred specialty choice, with surgery, specifically cardiovascular surgery and obstetrics and gynecology following (31). When taking into account findings from low and middle-income countries, beyond SSA, the choice of specialties among medical trainees is a multifactorial process influenced by personal, professional, educational and socioeconomic factors, which are in line with data from our analysis (32,33). A study by Dawood et al involving Pakistani medical students showed that surgery was the initial choice of most medical students early on in their training; however, as training progressed, there was a shift towards medicine (34). This shift in trend appears to be associated with clinical exposure, which may influence the students' perception of specialty demands, work-life balance and long-term career viability (34). Among international LMIC students in China, General Surgery was the most preferred, with Physical Medicine the least chosen option (35). A scoping review in LMICs reported life fulfilment and career prospects as important factors, similar to Nigeria’s emphasis on personal interest (32). But in Saudi Arabia (an upper-middle-income LMIC), Surgery and Internal Medicine dominated, due to concerns about job security and income (36). Indecision rates in LMICs (10–15%) are comparable to the results from Nigeria, suggesting that these settings may share similar difficulties with career guidance. This early preference for surgery among preclinical and initial clinical year students in low and middle-income countries may be attributed to initial excitement surrounding, and perceived sophistication of surgical procedures, coupled with restricted early exposure to medical specialties and the deemed prestige attained by surgeons (32,37). In high income countries such as America and Canada, specialty choices was greatly influenced by mentorship, education, social and economic background, this was particularly important because students who tend to pick primary care specialties had several characteristics such as parents without postgraduate education, experience volunteering in developing countries, a desire to practice medicine in a broader scope, an orientation toward society, limited interest in research and a desire for short postgraduate training (33,38). Also, high-income countries (HICs) had preferences for “controllable lifestyle” specialties such as Dermatology, Radiology, and Anesthesiology, influenced by the factors of work-life balance and income (40). Surgery is still widely favoured, but less so (e.g. among US medical students: 10-15%), primary care is becoming more attractive as incentives are being aligned in a way more favourable to primary care (40,41) Determinants, such as academic interest (75%) and ability (55%), strongly influence choices in HICs, but with lesser emphasis on financial reward as seen in LMICs (41). This divergence represents how resource availability and policy frameworks in HICs mitigate imbalances, mechanisms absent in Nigeria. Implications The limited and inadequate access to structured career guidance outlined in this review can convincingly suggest its contribution to delayed decision-making and concentrated specialty choices. Current patterns of specialty choices among medical trainees will significantly increase underrepresentation of non-clinical and diagnostic specialties, and raise concerns for long-term workforce sustainability. Introduction of structured guidance early on in medical training could help broaden awareness and lead to diversity in specialty selection. Evidence from a prior study on Asian medical schools showed an increased appeal for broad foundational specialties when backed by strong institutional support and mentorship (42). Another study by Myhre et al, which showed an increased interest in family medicine among students paired with family medicine advisors/mentors (43), strengthens the claim that improved structured guidance early in medical training can broaden awareness and promote diversity in specialty selection. Nigeria's low physician density of 0.4 per 1,000 calls for alignment with preferences to address brain drain and maldistribution (44). While the National Policy on Health Workforce Migration 2024 provides a framework, action is needed to keep talent in primary care (45). Policy needs to make career guidance compulsory from preclinical years and incorporate digital tools and role models to counteract gender stereotypes (e.g. males selecting Surgery) (46). Increase training positions in underrepresented areas while capping over-subscribed ones, informed by preferences to optimise resource allocation (47). Future research should prioritise nationally representative longitudinal studies to capture and characterise the evolution of specialty preferences over time, addressing the limitations of cross-sectional investigation and overcoming regional bias in existing evidence (42). Strengths of the review The long temporal coverage, spanning 1992-2025, strengthens confidence in the observed stability of specialty preferences among medical trainees, enhancing the generalizability of the findings. To our knowledge, this study represents the first quantitative synthesis and meta-analysis of specialty preference patterns among medical trainees in Nigeria, providing reliable pooled estimates that were lacking in prior narrative reviews. Furthermore, methodologically sound risk of bias assessment, using the AXIS tool and sensitivity analyses, further supports the reliability and robustness of our findings, despite moderate RoB predominance. Limitations The high heterogeneity of a considerable number of the studies due to differences in study design, statistical models and applicant differences reduces the reliability of pooled estimates, meaning they might not accurately reflect an individual study. This necessitates more complex methods for more definite answers, meaning observed findings should be interpreted with caution. Likewise, the predominant use of cross-sectional studies also brings with it innate disadvantages such as difficulty in determining causality and inability to track change over time. As a result, important factors like the evolution of preference from the influence of key experiences (clinical rotations) and overestimation of preference stability by treating applicants like a single continuum instead of distinct cohorts are left undetermined. There are also issues with generalizability and limited statistical power due to convenience sampling and modest sample sizes. Finally, the nature of the question at hand requires the need for self-reported preference, which cannot always be trusted due to social/peer pressure, recall bias, intentional misreporting, or situations in which the individual is not self-aware enough to know their preference. This can potentially inflate the choice of surgical specialties while underreporting interest in underrepresented fields. Conclusion The systematic review and meta-analysis show that career choices among medical students, interns, and pre-residency doctors in Nigeria are a complex decision process influenced by clinical experience, socioeconomic and personal factors, with surgical specialties favoured over medical and non-clinical specialties. These results highlight the underrepresentation of certain specialties in the Nigerian medical system, which can negatively impact the strength of comprehensive health care. This can be combated by policies aimed at making said specialties more appealing, like improved work-life balance (caps on call time, flexible residency options), increased residency positions of underrepresented specialties, financial incentives (salary top-ups, funded fellowships), earlier exposure to said specialties during early medical education, mandatory career counselling in preclinical years, formal mentorship programs, etc. Future research should focus on longitudinal and mixed-method studies to narrow down the causal agents of specialty choices. Further analysis should also be carried out to determine the effect of policy changes on applicant decisions, and policies should be reviewed if the effects are undesirable. Abbreviations ● AXIS Appraisal Tool for Cross–Sectional Studies ● CI Confidence Interval ● HICs High–Income Countries ● I² Higgins’ Inconsistency Index (Measure of Heterogeneity) ● LMICs Low–and Middle–Income Countries ● MeSH Medical Subject Headings ● O&G Obstetrics and Gynaecology ● PRISMA Preferred Reporting Items for Systematic Reviews and Meta–Analyses ● RoB Risk of Bias ● SD Standard Deviation ● SSA Sub–Saharan Africa ● SW / SE / NC / NW South–West / South–East / North–Central / North–West (Nigeria Geopolitical Zones) ● τ² (Tau squared) –Between–Study Variance in Random–Effects Meta–Analysis ● WHO World Health Organisation Declarations Ethics approval and consent to participate Not applicable. This study did not involve human participants, human data, or tissue. Consent for publication Not applicable. No person’s data in any form is presented in this manuscript. Competing interests The authors declare that they have no competing interests. All authors certify 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, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions GU conceived and drafted the study proposal, developed the search strategy, conducted data extraction, and performed the risk-of-bias (RoB) assessment, including RoB visualisations. GU wrote the Results section and prepared the PRISMA flow diagram. VI drafted the Introduction. CO wrote the Methods and Discussion sections. EN contributed to the Discussion and wrote the Conclusion. TO wrote the Abstract and contributed to the development of the search strategy, data extraction, and RoB assessment. GU, TO, and CO screened articles for eligibility. All authors contributed to the interpretation of the data, critically revised the manuscript for important intellectual content, approved the final version for submission, and agree to be accountable for all aspects of the work. GU is the guarantor of the manuscript. Acknowledgements Not applicable. Clinical trial number Not applicable Data Availability Statement The datasets generated and/or analyzed during the current systematic review and meta-analysis are available from the corresponding author on reasonable request. All included studies are publicly available through the databases searched (e.g., PubMed, and Google Scholar), and full details of the search strategy, inclusion/exclusion criteria, and extracted data are provided in the manuscript. 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Available from: https://www.who.int/teams/primary-health-care/evidence-and-innovation/primary-health-care-case-study-compendium/detail/nigeria--health-workforce-strategic-planning-and-management-for-uhc Tables Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8470357/v1/15ee7e6b005a121d0a0c1e37.png"},{"id":104266392,"identity":"e1eca2e3-336c-4b3d-91c0-cfec3963165e","added_by":"auto","created_at":"2026-03-09 20:24:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1199342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8470357/v1/a891be2a-164b-49ac-bc00-34a21cab4187.pdf"},{"id":101788132,"identity":"863a520f-28f5-442d-97e4-f91f2aabf516","added_by":"auto","created_at":"2026-02-03 15:52:45","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11686,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8470357/v1/9fa2e81476f2bb8d6e715209.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Speciality Preferences and Career Choices Among Nigerian Medical Students, Interns, and Preresidency Doctors: A Systematic Review and Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe strength of the healthcare team is deeply rooted in the diversity of services provided. This diversity is lost with the uneven distribution of manpower throughout various medical specialties. The burden of chronic non-communicable diseases (like hypertension) in Nigeria has been steadily increasing (1). The complications of these multifactorial illnesses are widespread and frequently involve multiple systems in the body, and this calls for multidisciplinary management. In situations where the uneven distribution of medical manpower is significant, the quality of care that these patients can receive becomes significantly reduced. With hypertensive emergencies often being life-threatening, the urgency for healthier distributions of medical manpower cannot be overemphasised. With males being more heavily affected than females (1), the economic implications of the lack of specialist care with respect to this disease can significantly reduce the standard of living of affected families. With a specialty imbalance in place, complications are often fatal.\u003c/p\u003e\n\u003cp\u003eThe physician density is alarmingly low in Nigeria; even though over 74,500 doctors are registered in the country, only a small proportion are practising, resulting in a real physician-to-population ratio of about 2.9 per 10,000 people, which is far below the World Health Organisation (WHO)-recommended thresholds (2,3). This scarcity is exacerbated by maldistribution, with the rural and northern areas being particularly neglected, alongside a constant brain drain of specialists, widening the rift in service coverage (3,4).\u003c/p\u003e\n\u003cp\u003eAgainst this background, the choice of specialty among medical students and early-career doctors is of strategic concern. Decisions made in medical school and during the year of internship effectively govern the trajectory into graduate medical training. These preliminary decisions shape not only which disciplines trainees enter but also the geographical distribution of specialists, and in turn affect national capacity in critical areas such as surgery, maternal and child health, and preventive medicine.\u003c/p\u003e\n\u003cp\u003eRecent preliminary studies indicate that Nigerian medical trainees are strongly drawn to a limited number of clinically \u0026ldquo;prestigious\u0026rdquo; specialties. In 2024\u0026ndash;2025, a cross-sectional survey of 439 penultimate and final year medical students, 40.7% of the students considered core surgical specialties (including subspecialties) as their top choice (5). Obstetrics \u0026amp; Gynaecology was the\u0026ensp;second most common choice (14.7%), and there were significant gender and class-year variances: for instance, 10.6% of penultimate-year students had chosen O\u0026amp;G, compared to 18.9% final year; males were more likely to choose cardiothoracic surgery (85.7% of those who did so) than females (5). Worryingly, 13.3% of the respondents to that very survey were still undecided on their preferred specialty (5).\u003c/p\u003e\n\u003cp\u003eThe situation is similar according to other surveys. Among medical students who are in their final year and house officers (n = 760), the most popular specialties were surgery (26.6%), internal medicine (14.5%), and obstetrics \u0026amp; gynaecology (14.3%), and only 3.6% planned to specialise in anaesthesia, which is an area with considerable workforce shortages (6). A study in Southeast Nigeria (n = 457), discovered that the majority of the students decided their specialty preference during clinical rotations (52% deciding during the clinical years), and only a quarter (25.4%) had some sort of career counseling; 24% opted for surgery, 18.8% for paediatrics, 15.6% for O\u0026amp;G, and 11% for internal medicine (7). Gendered trends were pronounced: male students were far more likely than females to select surgery (32.3% vs 13.0%, p \u0026lt; 0.001), and female students more frequently selected paediatrics (28.8% vs 11.2%, p \u0026lt; 0.001) (7).\u003c/p\u003e\n\u003cp\u003eDeterminants of specialty choice tend to be similar and largely depend on personal interest in the subject and anticipated future earnings. According to a 2025 study in BMC Medical Education, 87% of students are attracted to their specialty preference due to personal interest and 85.3% by the intellectual content of the specialty; 77% rated potential lucrativeness as influential (5). Work-hour expectations, shorter training period, and desired practice settings were other characteristics that were significantly correlated with specialty selection (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for several of them) (5). In anaesthesia, students frequently named a lack of mentor exposure and insufficient clinical rotations as demotivators (6).\u003c/p\u003e\n\u003cp\u003eDespite these insights, the literature remains fragmented, with studies primarily single-institution based. The studies span a long period, across decades (from the early 2000s to the present), with the sampling of different groups (undergraduates, house officers, interns), alongside geographical clusters (4,5,7). The hyper specificity of these findings makes it a poor predictor of national and even regional patterns with respect to the choice of specialties among medical students, interns, and pre-residency physicians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA comprehensive review of the various indicators influencing the specialty choices among medical trainees is therefore crucial. A systematic review and meta-analysis would both address the issue of the limited perspective provided by the fragmented and single institution studies, and provide a more reliable qualitative and quantitative backbone for the understanding of current patterns and trends, as well as provide a framework for making possible predictions of how the landscape could look in future.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eObjectives\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims primarily to identify the current distribution of first-choice specialty preferences among medical trainees in Nigeria. Secondary objectives are to (a) identify when specialty decisions are made or are likely to be made (early or late in training), (b) assess and rank in importance, the factors influencing choice (interest, income, mentorship, work-life balance), and (c) explore geographic (regional), gender, and temporal variations in these patterns. In this holistic summary, we hope to highlight critical areas for improvement in workforce\u0026ensp;planning, targeted career counselling, and potentially national educational and policy interventions to better match trainee interests and national health priorities.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy design and reporting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as a systematic review of primary quantitative studies examining medical specialty preferences and their determinants among medical students and early-career doctors in Nigeria. The review focused on cross-sectional and descriptive survey studies conducted in Nigerian medical schools, teaching hospitals, and internship or residency settings. The review followed a predefined methodological framework and was reported in accordance with PRISMA 2020 guidelines (8).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEligibility criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEligibility criteria were defined a priori based on population, study design, and relevance to specialty preference outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies were eligible if they met all of the following conditions:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePopulation: Nigerian medical students (preclinical, clinical, final-year), interns/house officers, or pre-residency doctors.\u003c/li\u003e\n \u003cli\u003eStudy focus: Assessment of medical specialty preference, choice intention, or career interest, including influencing factors (e.g., personal interest, lifestyle, income, role models, training exposure).\u003c/li\u003e\n \u003cli\u003eStudy design: Primary quantitative studies, predominantly cross-sectional surveys, including institutional studies and multicenter Nigerian studies.\u003c/li\u003e\n \u003cli\u003eOutcomes: Studies reporting data on medical specialty preference were eligible for inclusion.\u003c/li\u003e\n \u003cli\u003eSetting and region: Conducted in Nigeria with a clearly stated institutional or geographic context.\u003c/li\u003e\n \u003cli\u003eLanguage: Published in English.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudies were excluded if they were:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eReviews, editorials, commentaries, or opinion pieces;\u003c/li\u003e\n \u003cli\u003eQualitative-only studies without extractable quantitative results;\u003c/li\u003e\n \u003cli\u003eConducted outside Nigeria or using non-Nigerian populations;\u003c/li\u003e\n \u003cli\u003eLacking sufficient detail on specialty preference outcomes or study population.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eInformation sources\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRelevant studies were identified from indexed biomedical databases, open-access journal platforms, and institutional repositories. Sources included PubMed, Google Scholar, and journal websites hosting Nigerian medical education research. Reference lists of eligible studies were also screened to identify additional relevant publications.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSearch strategy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSearch strategies combined keywords related to medical education, specialty choice, and Nigeria using Boolean operators. Search terms were modified to suit the syntax of the specific database. PubMed searches were conducted by using a combination of Medical Subject Headings (MeSH) terms and free-text keywords through the use of Boolean operators, truncation and field tags. The searches were performed in Google Scholar using simple keyword strings without controlled vocabulary, applying phrase searching, and including the Boolean operators to increase sensitivity. Search was limited to and focused on medical students, house officers and pre-residency doctors in Nigeria, the choice of specialty/preference, the determinants, and the timing of career decision. No filters for study design were applied to allow for comprehensive capture of both cross-sectional and descriptive studies. Table 1 summarises the database-specific search strategies for PubMed and Google Scholar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Database-specific search strategies\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: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDatabase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSearch components\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSearch terms/syntax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePubMed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e\u0026quot;Students, Medical\u0026quot;[MeSH] OR \u0026quot;Medical Students\u0026quot;[tiab] OR \u0026quot;Final year medical students\u0026quot;[tiab] OR \u0026quot;House Officers\u0026quot;[tiab] OR Interns[tiab] OR \u0026quot;Junior doctors\u0026quot;[tiab]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eConcept (career/specialty choice)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e\u0026quot;Career Choice\u0026quot;[MeSH] OR \u0026quot;Specialty Choice\u0026quot;[tiab] OR \u0026quot;Specialty Preference*\u0026quot;[tiab] OR \u0026quot;Residency Choice\u0026quot;[tiab] OR \u0026quot;Postgraduate training\u0026quot;[tiab]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eOutcome/determinants/timing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003epreference*[tiab] OR choice*[tiab] OR determinant*[tiab] OR factor*[tiab] OR \u0026quot;career decision\u0026quot;[tiab] OR \u0026quot;timing of decision\u0026quot;[tiab]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eSetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e\u0026quot;Nigeria\u0026quot;[MeSH] OR Nigeria[tiab]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined strategy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e(Population) AND (Concept) AND (Setting) AND (Outcome terms)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGoogle Scholar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e\u0026quot;medical students\u0026quot; OR \u0026quot;final year medical students\u0026quot; OR \u0026quot;house officers\u0026quot; OR interns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eConcept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e\u0026quot;specialty choice\u0026quot; OR \u0026quot;specialty preference\u0026quot; OR \u0026quot;career choice\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eSetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore search string\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e(\u0026quot;medical students\u0026quot; OR \u0026quot;final year medical students\u0026quot; OR \u0026quot;house officers\u0026quot; OR interns) AND (\u0026quot;specialty choice\u0026quot; OR \u0026quot;specialty preference\u0026quot; OR \u0026quot;career choice\u0026quot;) AND Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRefinement (iterative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 429px;\"\u003e\n \u003cp\u003e(\u0026quot;specialty choice\u0026quot; OR \u0026quot;specialty preference\u0026quot;) AND (\u0026quot;medical students\u0026quot; OR \u0026quot;house officers\u0026quot;) AND Nigeria AND (determinants OR factors OR \u0026quot;career decision\u0026quot;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2,935 records were identified through database searches, comprising 2,900 records from Google Scholar and 35 records from PubMed. Prior to screening, 587 duplicate records were removed, leaving 2,348 unique records for title and abstract screening. During the initial screening phase, 1,927 records were excluded for failing to meet the predefined eligibility criteria. The remaining 421 reports were sought for full-text retrieval. Of these, 374 reports could not be retrieved due to inaccessibility, incomplete reporting, or unavailable full texts.\u003c/p\u003e\n\u003cp\u003eConsequently, 47 full-text articles were assessed for eligibility. Following detailed evaluation, 27 reports were excluded for the following primary reasons:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eWrong study population (n = 11)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLack of extractable specialty-preference outcomes (n = 9)\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDuplicate cohorts (n = 7).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUltimately, 20 studies met the inclusion criteria and were included in the qualitative synthesis. Of these, 14 studies provided sufficient quantitative data to be included in the meta-analysis. The full study selection process is summarised in the PRISMA 2020 flow diagram (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData extraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData were extracted manually from included studies and subsequently organised and compiled in Table 2. Extracted variables corresponded directly to the spreadsheet columns and included:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBibliographic details: Author(s) and year of publication\u003c/li\u003e\n \u003cli\u003eStudy design: Cross-sectional, descriptive, or multicenter survey\u003c/li\u003e\n \u003cli\u003eStudy setting: Medical school, teaching hospital, internship level\u003c/li\u003e\n \u003cli\u003eGeographic region: Nigerian geopolitical zone or specific state\u003c/li\u003e\n \u003cli\u003ePopulation studied: Level of training (preclinical, clinical, final year, intern, preresident)\u003c/li\u003e\n \u003cli\u003eSample size and demographics: Mean age, gender distribution\u003c/li\u003e\n \u003cli\u003eTop preferred specialties\u003c/li\u003e\n \u003cli\u003eLeast preferred specialties\u003c/li\u003e\n \u003cli\u003eUndecided proportion, where reported\u003c/li\u003e\n \u003cli\u003eFactors influencing specialty choice: Personal interest, lifestyle, income, clinical exposure, mentorship, family influence, etc.\u003c/li\u003e\n \u003cli\u003eMethod of data collection: Self-administered or interviewer-administered questionnaires\u003c/li\u003e\n \u003cli\u003eKey findings\u003c/li\u003e\n \u003cli\u003eStudy strengths and limitations\u003c/li\u003e\n \u003cli\u003eRisk-of-bias indicators\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll extracted data were checked for consistency and accuracy prior to synthesis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk of bias and quality assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRisk of bias was assessed using the AXIS tool for cross-sectional studies. The AXIS tool assesses study quality across domains, including participant selection, sampling strategy, non-response, measurement of outcomes, handling of missing data, selective reporting, and consideration of confounding factors (9). Each study was assessed across these domains and categorised as having low risk of bias, moderate risk of bias, or insufficient information, with overall judgments derived from the cumulative assessment of domain-level concerns.\u003c/p\u003e\n\u003cp\u003eRisk-of-bias assessments were not used as exclusion criteria. Instead, AXIS ratings were incorporated into sensitivity analyses and meta-regression to examine the robustness of pooled estimates. The distribution of risk-of-bias judgments is presented using a traffic-light plot and summary plot. (Figures 2 and 3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData synthesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData synthesis followed a predefined approach agreed upon before analysis. After data extraction, studies were first reviewed descriptively, paying attention to differences in study population, stage of training, geographic setting, and reported specialty preferences. Outcomes were then examined to determine whether they were suitable for quantitative pooling, taking into account the availability of usable prevalence data, how specialties were defined and grouped across studies, and whether sample sizes were sufficient for meaningful aggregation. Outcomes that could not reasonably be pooled quantitatively, such as when career decisions were made, the availability of career guidance, and factors influencing specialty choice, were synthesised narratively. These results were grouped thematically and examined across different stages of training and institutional settings to highlight recurring patterns and areas of convergence.\u003c/p\u003e\n\u003cp\u003eFor outcomes where data were sufficiently comparable, quantitative synthesis was performed using meta-analytic techniques, with specialty-specific preference proportions pooled as detailed in the statistical analysis sections. Given the expected variation between studies, random-effects models and stratified subgroup analyses were planned, pooling prevalence estimates and accounting for clinical and methodological heterogeneity (I\u003csup\u003e2\u003c/sup\u003e) across studies. Pooled prevalence estimates were calculated for individual preferences across medical specialties. Prevalence data were transformed using the Freeman-Tukey double-arcsine transformation to stabilise discrepancy before pooling, with back-transformation applied for presentation of results (10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the studies included, statistical heterogeneity was assessed using the I\u003csup\u003e2\u003c/sup\u003e statistic (11). Subgroup analyses were performed by trainee population (undergraduate, house officer/intern, preresidency), training stage (entry, final-year, internship), geographic zone (South-West, South-East, North-Central, North-West, multi-zone), decade of publication (\u0026le; 2000, 2001\u0026ndash;2010, 2011\u0026ndash;2020, 2021\u0026ndash;2025), and study-level gender composition (male-majority, female-majority, unknown). Sensitivity analysis was performed to assess the robustness of pooled estimates. This included repeating analysis after excluding studies that have a high risk of bias using the AXIS tool for cross-sectional studies (9). In addition, a leave-one-out sensitivity analysis was conducted by sequentially excluding individual studies to evaluate the influence of single studies on the overall pooled estimates (9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDecisions about whether an outcome was analysed quantitatively or narratively were based on data suitability alone and were not influenced by the size or direction of the reported findings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData management\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe extracted tables were used to guide descriptive comparison across studies and to structure both narrative synthesis and subsequent quantitative analyses. Data management and analyses were conducted without the use of automated extraction tools.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCertainty/strength of evidence\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA formal certainty-of-evidence assessment (e.g., GRADE) was not conducted. This decision was based on the observational cross-sectional design of all included studies, substantial heterogeneity across outcomes, and variability in outcome definitions and reporting formats. Consequently, findings are interpreted within the context of these methodological limitations, with emphasis placed on consistency of direction, subgroup patterns, and sensitivity analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis review relied exclusively on previously published data. No new data were collected from human participants, and ethical approval was therefore not required.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDeviations from protocol (if any)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo deviations from the predefined methodological framework were made.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eStudy selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe electronic database query, combined with additional hand searching, yielded a total of 2,935 records. 2348 titles and abstracts were later screened for relevance, after the removal of 587 duplicates. Of these, 47 full-text articles were retrieved, and 374 were not able to be retrieved. Twenty (20) full-text articles were considered eligible, as they met the predefined inclusion criteria, and were included in the qualitative synthesis. Of these,14 studies reported sufficient quantitative data to be included in the meta-analysis of speciality preference proportions.\u003c/p\u003e\n\u003cp\u003eFull-text exclusions were based on an ineligible study population, no speciality-specific preference data that could be extracted, and duplicate/overlapping cohorts. The PRISMA flow diagram summarises the study selection process (Figure 1)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCharacteristics of included studies\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe time of publication of all the included studies ranged from 1992 to 2025, with the number of participants from all studies totalling more than 4,000 medical trainees in Nigeria. All were cross-sectional studies, using a structured or semi-structured questionnaire to evaluate specialty preference and related determinants. The mode of data collection also varied; we observed that most of the earlier studies were conducted using paper-based questionnaires, administered physically, while more recent studies were conducted via online platforms, including Google Forms and Qualtrics, often distributed to potential respondents through institutional email or social media networks (6,12\u0026ndash;13).\u003c/p\u003e\n\u003cp\u003eThe study population varied from preclinical students, clinical (final year) students, house officers/interns, to medical graduates not enrolled in residency programs, covering the entire medical training continuum. The most frequently studied cohort was final year medical students, highlighting their proximity to career decision-making (5,6,14). Several studies also focused specifically on interns or house officers to capture preferences at the crucial juncture between undergraduate training and postgraduate education (15-16).\u003c/p\u003e\n\u003cp\u003eThe studies were conducted in the six geopolitical zones of Nigeria; however, there was disproportionate and uneven representation. The majority of the included samples were recruited from the South-West and South-East zones, particularly from Lagos, Ibadan, Enugu and affiliated teaching hospitals (5,14,17). Conversely, fewer institution-based studies and relatively smaller sample sizes were found in the North-West and North-Central zones (18,19).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcross studies, male participants were dominant, especially among intern and postgraduate cohorts, with male proportions ranging from approximately 52% to over 75% in several studies (16,19,20). Among undergraduate cohorts, females were more prevalent, but remained below parity in most samples. The reported mean age, or median age, increased with training level, from the early twenties among undergraduate students to the early thirties among pre-residency doctors, consistent with the anticipated progression of medical training (5,16).\u003c/p\u003e\n\u003cp\u003eOverall, notwithstanding methodological imbalance and regional diversities, the included studies provided a broad, comprehensive and temporally sound overview of speciality preference patterns among Nigerian medical trainees. Table 2 summarises the characteristics of included studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Summary of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"800\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion (geo-zone)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation (stage)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (mean \u0026plusmn; SD/median)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (M / F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop preferred specialty (%, n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUndecided (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop 3 determinants (as reported)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTiming of decision (modal)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCareer guidance (% reported having)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRoB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOlajide T et al. (2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eMulti-zone (SW 48.1% dominant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003ePenultimate \u0026amp; final-year med students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.62 \u0026plusmn; 2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e233 / 199 (6 prefer not to say; 1 non-binary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 40.7% (n=179); O\u0026amp;G 14.7% (n=65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e13.3% (n=58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest (87%); specialty content (85.3%); practice setting (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eClinical/penultimate\u0026ndash;final years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e29.9% had guidance (70.1% had none pre-entry)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOkonkwo TC et al. (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eMulti-zone (SW 36.3% dominant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFinal-year students \u0026amp; House Officers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e25.84 \u0026plusmn; 2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e413 / 347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 26.6%; Internal Med 14.5%; O\u0026amp;G 14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e11.2% undecided; 2% no intention to specialise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePassion/interest; flexibility; potential income; role-model effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eClinical rotations (~63.95% decided)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e~35.3% reported having received guidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOssai EN et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-East (all 6 med schools)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFinal-year med students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e25.5 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eNR (57.1% male reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 24.0%; Paediatrics 18.8%; O\u0026amp;G 15.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest; lecturer/student interactions; clinical exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eClinical rotations (51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e25.4% had some guidance (74.6% none)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eEze BI et al. (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-East (Enugu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003ePreresidency graduates (post-internship)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e33.5 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e219 / 68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eO\u0026amp;G 22.6%; Surgery (combined) 19.6%; Paediatrics 16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest (66.6%); career prospects; self-appraisal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eBy the end of PGY-5 (97.2% decided)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOnyemaechi NOC et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-East (Enugu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFinal-year undergraduates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e25.8 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e110 / 42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgical specialties 52.0% (n=79); O\u0026amp;G 14.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest (88.8%); competence; career prospects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eThe majority final year (42.1% chose in final year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eLimited (institutional guidance reported low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eFalase B et al. (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Lagos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFinal-year students (two cohorts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e25.3 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e74 / 67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 29.2%; O\u0026amp;G 17.4%; Paediatrics 14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e24.8% did \u003cstrong\u003enot\u003c/strong\u003e want to specialise; 9.9% undecided about practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEnjoyed rotation (62.3%); job/financial rewards (54.7%); work\u0026ndash;life balance (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eConsolidated during clinical/final year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal/institutional guidance emphasised\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAsani M.O. et al. (2016, Kano)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNorth-West (Kano)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFinal-year students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eMedian 27 (24\u0026ndash;44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e47 / 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eO\u0026amp;G 28.5% (n=19); Surgery 20.9% (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest; perceived patient-outcomes; teacher influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAt graduation (final-year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNot reported (formal guidance uncommon)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOkonta K.E. et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eMulti-zone (4 tertiary hospitals)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eHouse officers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.4 yrs (mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e79 / 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eO\u0026amp;G 26.3% (n=34); Surgery 21.7% (n=28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.4% (n=7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest (46.7%); role models (11.3%); expected financial reward (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDuring internship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eTeachers/residents/parents cited as influencers (no pooled %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOdusanya OO et al. (2000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Lagos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003ePioneer medical students (entry cohort)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.6 \u0026plusmn; 3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eO\u0026amp;G 28.6%; Surgery 25.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePrimary interest (91%); desire to serve humanity (91%); job satisfaction (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eIntent evident at entry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAkinyinka MR et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Lagos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eNew intake (200-level) students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eMajority 20\u0026ndash;29 yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eApprox balanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 47.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eInterest/love for specialty (89.1%); love of profession (82.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eEarly (preferences at entry)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAshipa T. et al. (2017, Babcock)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Ogun)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFirst-year medical students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eM 18.3 \u0026plusmn; 2.03; F 17.5 \u0026plusmn; 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e18 / 33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 43.1% (n=22); O\u0026amp;G 13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e13.7% undecided (intent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest (73.2%); self-fulfilment; role models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eEarly (at entry)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal institutional guidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOhaeri J.U. et al. (1993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Ibadan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eInterns/house officers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e38 / 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery most popular; core clinical specialties are 95.7% of decided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026asymp;5.9% (3/51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eClinical postings; self-fulfilment; many decided pre-university\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e43% had decided before university; choices evolved during training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal counselling reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOdusanya O.O. \u0026amp; Alakija W. (1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Lagos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eHouse officers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e38 / 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eCore clinical specialties dominated (95.7% of those decided); Surgery most popular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e~5.9% (3/51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eClinical exposure/rotations; personal interest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e43% decided before entry; others during clinical/internship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal guidance reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMadu AJ et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-East (UNTH, Enugu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFresh house officers/interns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eMedian 26 (22\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e69 / 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 26.4% (29/110); Paediatrics 25.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e12.8% (14/109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest (78.9%); job satisfaction; career prospects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ePreferences captured during internship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal institutional counselling reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAdeboye A. et al. (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNorth-Central (Ilorin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eMedical interns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 21.0% (n=16); Paediatrics 18.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePerceptions of specialty interest; short rotation duration cited as barrier (ophth).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDuring internship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal guidance reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAbioye I.A. et al. (2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Lagos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSenior students \u0026amp; interns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery topped the list; ~55% likely to choose trauma-related specialties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRole models/mentoring; theatre experience; financial reward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eLate undergraduate/internship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNot specifically reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOhaeri J.U., Akinyinka O.O., Asuzu M.C. (1992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Ibadan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFirst-year clinical \u0026amp; final-year clinical students (combined)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery most popular; \u0026gt;81% preferred core clinical specialties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eClinical postings; many had pre-entry preferences (42.9%); choices evolved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eMany chose before entry (42.9%); evolved across years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal counselling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eOdusanya O.O., Nwawolo C.C. (2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Lagos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eHouse officers / Interns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSurgery 18.1%; O\u0026amp;G 18.1%; Dental sciences 10.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e17.1% did not indicate a wish to specialise in qualifying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eInterest (72.4%); job satisfaction (67.6%); bright prospects (54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eThe majority had a choice as undergraduates; some changed by internship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNo formal guidance reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eImediegwu KU et al. (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-East (Enugu universities)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFinal-year med students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e57% / 43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eOrthopaedics interest 27.0% (n\u0026asymp;35) \u0026mdash; study focused on ortho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eClinical rotations (88.3% awareness); lack of mentorship major barrier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eFinal-year (late undergraduate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.1% cited the mentor as the source for ortho awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eEzegwui C.O. et al. (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eSouth-West (Ibadan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eFinal-year med students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.6 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eO\u0026amp;G 22.9% (n=54); Surgery 18.6% (n=44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePersonal interest (78.4%); significant for O\u0026amp;G/family med/public health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eFinal-year (MS-Final)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e47.5% reported having received any career counselling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk of bias assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRisk of bias (RoB) \u0026nbsp;was evaluated using the AXIS tool for cross-sectional studies, which assesses the quality of studies in the following domains: selection of participants, non-response, measurement of outcome, missing data, and selective reporting and confounding (9). Overall, most of the included studies were judged to have some concerns (moderate risk of bias). The most common sources of bias were related to the selection of participants and non-response, reflecting the dominance of convenience sampling; recruitment at a single institution and the dissemination of surveys online, which may limit the generalisability and representativeness of the target population (5,6,18).\u003c/p\u003e\n\u003cp\u003eConcerns related to the measurement of outcomes and selective reporting, and confounding were also prevalent. Although speciality preference was self-reported using structured questionnaires, a limited number of studies did not explicitly account for potential confounding variables or demonstrate instrument validation, thus leaving some residual uncertainty in these areas (5,6,13,18), while older studies provided limited methodological detail on outcome measurement (12,13). A smaller proportion of studies demonstrated an overall low risk of bias, especially those that utilised near census or whole population sampling, reported high response rates and reported outcomes thoroughly with minimal missing data (7). The majority of the studies demonstrated a low risk of bias with respect to the missing outcome data domain, although some older reports did not contain enough information to allow for a definitive judgement.\u003c/p\u003e\n\u003cp\u003eNone of the studies were excluded solely based on risk of bias. Rather, AXIS evaluations were included in the sensitivity analysis and meta-regression to evaluate the robustness of pooled estimates to study quality.\u003c/p\u003e\n\u003cp\u003eTable 3 summarises RoB of the included studies using AXIS, while Figures 2 and 3 visualise the RoB of the included studies into a summary plot and a traffic light plot, respectively.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003ePrincipal findings\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this systematic review and meta-analysis, our findings indicate a sustained dominance of surgical and core clinical specialty choices among medical trainees in Nigeria. Across all subgroups analysed (training stage, geopolitical zone, decade of publication and gender), surgery remained the predominant specialty choice. Obstetrics and gynecology was consistently the second most preferred choice of specialty across decades of publication, with pediatrics and internal medicine also featuring markedly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite notable heterogeneity, specialty preferences with respect to direction and ranking remained stable across subgroups and sensitivity analysis. On the other hand, non-clinical and diagnostic specialties were constantly underrepresented as first-choice options and attracted minimal interest across studies. The consistency of the low pooled estimates indicates a genuine lack of interest/preference in nonclinical and diagnostic specialties rather than statistical bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison with other countries\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe findings from our study are broadly consistent with the findings from previous meta-analyses conducted in Sub-Saharan Africa (SSA). In a study by Bajuniwre et al assessing specialty preference among medical students in SSA, surgery was indicated to be the most preferred choice of specialty across medical trainees with obstetrics and gynecology, internal medicine and pediatrics (28). Following similar findings, this trend was also observed in a study by Khamees \u003cem\u003eet al,\u0026nbsp;\u003c/em\u003ewith surgery being the most preferred choice (29). Our findings were not consistent with the study from Togo by Teclessou et al\u003cem\u003e,\u003c/em\u003e where medical specialties were the preferred choice, followed by surgical specialties and then obstetrics and gynecology. This difference in specialty choice can be attributed to the lack of funding and the limited choice of specialties in that region (30). Another study by Musa et al involving Zambian medical students found internal medicine to be the most preferred specialty choice, with surgery, specifically cardiovascular surgery and obstetrics and gynecology following (31). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen taking into account findings from low and middle-income countries, beyond SSA, the choice of specialties among medical trainees is a multifactorial process influenced by personal, professional, educational and socioeconomic factors, which are in line with data from our analysis (32,33). A study by Dawood\u003cem\u003e\u0026nbsp;\u003c/em\u003eet al\u003cem\u003e\u0026nbsp;\u003c/em\u003einvolving Pakistani medical students showed that surgery was the initial choice of most medical students early on in their training; however, as training progressed, there was a shift towards medicine (34). This shift in trend appears to be associated with clinical exposure, which may influence the students\u0026apos; perception of specialty demands, work-life balance and long-term career viability (34).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong international LMIC students in China, General Surgery was the most preferred, with Physical Medicine the least chosen option (35). A scoping review in LMICs reported life fulfilment and career prospects as important factors, similar to Nigeria\u0026rsquo;s emphasis on personal interest (32). But in Saudi Arabia (an upper-middle-income LMIC), Surgery and Internal Medicine dominated, due to concerns about job security and income (36). Indecision rates in LMICs (10\u0026ndash;15%) are comparable to the results from Nigeria, suggesting that these settings may share similar difficulties with career guidance. This early preference for surgery among preclinical and initial clinical year students in low and middle-income countries may be attributed to initial excitement surrounding, and perceived sophistication of surgical procedures, coupled with restricted early exposure to medical specialties and the deemed prestige attained by surgeons (32,37). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn high income countries such as America and Canada, specialty choices was greatly influenced by mentorship, education, social and economic background, this was particularly important because students who tend to pick primary care specialties had several characteristics such as parents without postgraduate education, experience volunteering in developing countries, a desire to practice medicine in a broader scope, an orientation toward society, limited interest in research and a desire for short postgraduate training (33,38).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, high-income countries (HICs) had preferences for \u0026ldquo;controllable lifestyle\u0026rdquo; specialties such as Dermatology, Radiology, and Anesthesiology, influenced by the factors of work-life balance and income (40). Surgery is still widely favoured, but less so (e.g. among US medical students: 10-15%), primary care is becoming more attractive as incentives are being aligned in a way more favourable to primary care (40,41) Determinants, such as academic interest (75%) and ability (55%), strongly influence choices in HICs, but with lesser emphasis on financial reward as seen in LMICs (41). This divergence represents how resource availability and policy frameworks in HICs mitigate imbalances, mechanisms absent in Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImplications\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe limited and inadequate access to structured career guidance outlined in this review can convincingly suggest its contribution to delayed decision-making and concentrated specialty choices. Current patterns of specialty choices among medical trainees will significantly increase underrepresentation of non-clinical and diagnostic specialties, and raise concerns for long-term workforce sustainability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIntroduction of structured guidance early on in medical training could help broaden awareness and lead to diversity in specialty selection. Evidence from a prior study on Asian medical schools showed an increased appeal for broad foundational specialties when backed by strong institutional support and mentorship (42). Another study by Myhre \u003cem\u003eet al,\u003c/em\u003e which showed an increased interest in family medicine among students paired with family medicine advisors/mentors (43), strengthens the claim that improved structured guidance early in medical training can broaden awareness and promote diversity in specialty selection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNigeria\u0026apos;s low physician density of 0.4 per 1,000 calls for alignment with preferences to address brain drain and maldistribution (44). While the National Policy on Health Workforce Migration 2024 provides a framework, action is needed to keep talent in primary care (45). Policy needs to make career guidance compulsory from preclinical years and incorporate digital tools and role models to counteract gender stereotypes (e.g. males selecting Surgery) (46). Increase training positions in underrepresented areas while capping over-subscribed ones, informed by preferences to optimise resource allocation (47).\u003c/p\u003e\n\u003cp\u003eFuture research should prioritise nationally representative longitudinal studies to capture and characterise the evolution of specialty preferences over time, addressing the limitations of cross-sectional investigation and overcoming regional bias in existing evidence (42).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStrengths of the review\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe long temporal coverage, spanning 1992-2025, strengthens confidence in the observed stability of specialty preferences among medical trainees, enhancing the generalizability of the findings. To our knowledge, this study represents the first quantitative synthesis and meta-analysis of specialty preference patterns among medical trainees in Nigeria, providing reliable pooled estimates that were lacking in prior narrative reviews. Furthermore, methodologically sound risk of bias assessment, using the AXIS tool and sensitivity analyses, further supports the reliability and robustness of our findings, despite moderate RoB predominance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe high heterogeneity of a considerable number of the studies due to differences in study design, statistical models and applicant differences reduces the reliability of pooled estimates, meaning they might not accurately reflect an individual study. This necessitates more complex methods for more definite answers, meaning observed findings should be interpreted with caution. Likewise, the predominant use of cross-sectional studies also brings with it innate disadvantages such as difficulty in determining causality and inability to track change over time.\u003cbr\u003e\u0026nbsp;As a result, important factors like the evolution of preference from the influence of key experiences (clinical rotations) and overestimation of preference stability by treating applicants like a single continuum instead of distinct cohorts are left undetermined. There are also issues with generalizability and limited statistical power due to convenience sampling and modest sample sizes. Finally, the nature of the question at hand requires the need for self-reported preference, which cannot always be trusted due to social/peer pressure, recall bias, intentional misreporting, or situations in which the individual is not self-aware enough to know their preference. This can potentially inflate the choice of surgical specialties while underreporting interest in underrepresented fields.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe systematic review and meta-analysis show that career choices among medical students, interns, and pre-residency doctors in Nigeria are a complex decision process influenced by clinical experience, socioeconomic and personal factors, with surgical specialties favoured over medical and non-clinical specialties. These results highlight the underrepresentation of certain specialties in the Nigerian medical system, which can negatively impact the strength of comprehensive health care. This can be combated by policies aimed at making said specialties more appealing, like improved work-life balance (caps on call time, flexible residency options), increased residency positions of underrepresented specialties, financial incentives (salary top-ups, funded fellowships), earlier exposure to said specialties during early medical education, mandatory career counselling in preclinical years, formal mentorship programs, etc. Future research should focus on longitudinal and mixed-method studies to narrow down the causal agents of specialty choices. Further analysis should also be carried out to determine the effect of policy changes on applicant decisions, and policies should be reviewed if the effects are undesirable.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eAXIS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAppraisal Tool for Cross\u0026ndash;Sectional Studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eHICs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh\u0026ndash;Income Countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eI\u0026sup2;\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHiggins\u0026rsquo; Inconsistency Index (Measure of Heterogeneity)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eLMICs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow\u0026ndash;and Middle\u0026ndash;Income Countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eMeSH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Subject Headings\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eO\u0026amp;G\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eObstetrics and Gynaecology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003ePRISMA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta\u0026ndash;Analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eRoB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRisk of Bias\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eSSA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSub\u0026ndash;Saharan Africa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eSW / SE / NC / NW\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSouth\u0026ndash;West / South\u0026ndash;East / North\u0026ndash;Central / North\u0026ndash;West (Nigeria Geopolitical Zones)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eτ\u0026sup2; (Tau\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003esquared)\u003c/b\u003e\u0026ndash;Between\u0026ndash;Study Variance in Random\u0026ndash;Effects Meta\u0026ndash;Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e● \u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study did not involve human participants, human data, or tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No person\u0026rsquo;s data in any form is presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. All authors certify 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, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGU conceived and drafted the study proposal, developed the search strategy, conducted data extraction, and performed the risk-of-bias (RoB) assessment, including RoB visualisations. GU wrote the Results section and prepared the PRISMA flow diagram.\u003c/p\u003e\n\u003cp\u003eVI drafted the Introduction. CO wrote the Methods and Discussion sections. EN contributed to the Discussion and wrote the Conclusion. TO wrote the Abstract and contributed to the development of the search strategy, data extraction, and RoB assessment.\u003c/p\u003e\n\u003cp\u003eGU, TO, and CO screened articles for eligibility. All authors contributed to the interpretation of the data, critically revised the manuscript for important intellectual content, approved the final version for submission, and agree to be accountable for all aspects of the work. GU is the guarantor of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current systematic review and meta-analysis are available from the corresponding author on reasonable request. All included studies are publicly available through the databases searched (e.g., PubMed, and Google Scholar), and full details of the search strategy, inclusion/exclusion criteria, and extracted data are provided in the manuscript. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkinlua JT, Meakin R, Umar AM, Freemantle N. 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PMID: 33568113; PMCID: PMC7877062.\u003c/li\u003e\n\u003cli\u003ePuertas EB, Ar\u0026oacute;squipa C, Guti\u0026eacute;rrez D. Factors that influence a career choice in primary care among medical students from high-, middle-, and low-income countries: a systematic review. Rev Panam Salud P\u0026uacute;blica. 2013 Nov;34(5):351-8. PMID: 24553763.\u003c/li\u003e\n\u003cli\u003eDawood MH, Mir F, Ahmed HH, Wasim M, Athar Khan M, Hasan A, et al. A cross-sectional investigation of trend in career specialty preference among clinical year medical undergraduates, including factors influencing preferences and discouragement. Front Med (Lausanne). 2025 Oct 17;12:1665043. doi: 10.3389/fmed.2025.1665043. PMID: 41179863; PMCID: PMC12576040.\u003c/li\u003e\n\u003cli\u003eLi W, Gillies RM, Liu C, Wu C, Chen J, Zhang X, et al. Specialty preferences of studying-abroad medical students from low- and middle-income countries. BMC Med Educ. 2023 Mar 15;23(1):158. doi: 10.1186/s12909-023-04123-5. PMID: 36922811; PMCID: PMC10015544.\u003c/li\u003e\n\u003cli\u003eSawan D, Alrefaei GM, Alesawi A, Abualross O, Alsuwaida SA, Meer N. Preferences, Career Aspects, and Factors Influencing the Choice of Specialty by Medical Students and Interns in Saudi Arabia: A Cross-Sectional Study. Cureus. 2023 Aug 6;15(8):e43018. doi: 10.7759/cureus.43018. PMID: 37674943; PMCID: PMC10478148.\u003c/li\u003e\n\u003cli\u003eGodbole AA, Oka GA, Ketkar MN, Solanki RS, Desai DT, Bangale SV, et al. Specialty preferences of undergraduate medical students: What do they choose and why? Med J Armed Forces India. 2025 Jan-Feb;81(1):66-71. doi: 10.1016/j.mjafi.2024.04.009. Epub 2024 May 27. PMID: 39872182; PMCID: PMC11762663.\u003c/li\u003e\n\u003cli\u003eScott I, Gowans M, Wright B, Brenneis F, Banner S, Boone J. Determinants of choosing a career in family medicine. CMAJ. 2011 Jan 11;183(1):E1-8. doi: 10.1503/cmaj.091805. Epub 2010 Oct 25. PMID: 20974721; PMCID: PMC3017271.\u003c/li\u003e\n\u003cli\u003eMurphy B. The 11 factors that influence med student specialty choice. American Medical Association. 2020. Available from: https://www.ama-assn.org/medical-students/specialty-profiles/11-factors-influence-med-student-specialty-choice\u003c/li\u003e\n\u003cli\u003eBland KI, Isaacs G. Contemporary trends in student selection of medical specialties: the potential impact on general surgery. Arch Surg. 2002 Mar;137(3):259-67. doi: 10.1001/archsurg.137.3.259. PMID: 11888445.\u003c/li\u003e\n\u003cli\u003eLadha FA, Pettinato AM, Perrin AE. Medical student residency preferences and motivational factors: a longitudinal, single-institution perspective. BMC Med Educ. 2022 Mar 17;22(1):187. doi: 10.1186/s12909-022-03244-7. PMID: 35300656; PMCID: PMC8929265.\u003c/li\u003e\n\u003cli\u003eNguyen QT, Bui NY, Nguyen MPN, Nguyen HV, Thuy MH. Do structured career counselling initiatives influence specialty preferences in medical students? A longitudinal observational survey study. BMJ Open. 2025 May 14;15(5):e099815. doi: 10.1136/bmjopen-2025-099815. PMID: 40374226; PMCID: PMC12083375.\u003c/li\u003e\n\u003cli\u003eMyhre DL, Sherlock K, Williamson T, Pedersen JS. Effect of the discipline of formal faculty advisors on medical student experience and career interest. Can Fam Physician. 2014 Dec;60(12):e607-12. PMID: 25642488; PMCID: PMC4264827.\u003c/li\u003e\n\u003cli\u003eGiwa A. Trust as Foundation: Can Nigeria\u0026rsquo;s New Health Workforce Policy Stem the Migration Tide? The International Journal of Health Planning and Management. 2024 Nov 24;\u003c/li\u003e\n\u003cli\u003eCommonwealth Secretariat. Nigeria has an ambitious plan for its health workforce, but can it afford it? Africa at LSE. 2024. Available from: https://blogs.lse.ac.uk/africaatlse/2024/10/21/nigeria-has-an-ambitious-plan-for-its-health-workforce-but-can-it-afford-it/\u003c/li\u003e\n\u003cli\u003eOssai EN, Azuogu BN. Future career plans of final year medical students in medical schools of southeast Nigeria: implications for policy. Afrischolar Discovery. 2018 Oct 29;\u003c/li\u003e\n\u003cli\u003eNigeria: Health workforce strategic planning and management for UHC. Who.int. 2020. Available from: https://www.who.int/teams/primary-health-care/evidence-and-innovation/primary-health-care-case-study-compendium/detail/nigeria--health-workforce-strategic-planning-and-management-for-uhc\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medical specialty preference, Nigeria, Workforce distribution, Career guidance, Health workforce planning","lastPublishedDoi":"10.21203/rs.3.rs-8470357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8470357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSpecialty selection among medical trainees directly impacts service provision capacity and future clinical workforce in Nigeria, with low physician density and uneven specialty distribution. We synthesised the literature to quantify specialty preferences, timing of decision-making, and the determinants across the different stages of training.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Adhering to PRISMA guidelines, a systematic review and meta-analysis of primary quantitative studies involving Nigerian medical students, interns/house officers and pre-residency doctors was performed. PubMed, Google Scholar and hand-searches were conducted to 2025. Eligible cross-sectional surveys that reported the determinants or preferences for specialisation were considered. Quality of study was assessed using AXIS. For meta-analysis, we pooled specialty-specific proportions using random-effects models with Freeman\u0026ndash;Tukey double‐arcsine transformation and evaluated heterogeneity using I\u0026sup2;. Predefined subgroup analyses investigated training stage, region, decade, and study-level gender composition.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwenty studies (1992\u0026ndash;2025) met the inclusion criteria (pooled\u0026ensp;participants\u0026thinsp;\u0026gt;\u0026thinsp;4,000); 14 studies contributed to pooled proportions. Surgery was the most preferred specialty (pooled 28.5%,\u0026ensp;95% CI 24.0\u0026ndash;33.3%; n\u0026thinsp;\u0026asymp;\u0026thinsp;3,127; I\u0026sup2;=88.6%). Obstetrics \u0026amp; Gynaecology 18.1% (95%\u0026ensp;CI 14.3\u0026ndash;22.3%); Paediatrics 13.8% (95% CI 10.1\u0026ndash;18.0%); Internal Medicine 11.8% (95% CI 9.3\u0026ndash;14.6%). Low-preference fields included Radiology (1.5%, 95% CI 0.6\u0026ndash;2. 9%) and Pathology (1.8%, 95% CI 0.9\u0026ndash;3.0%). Subgroup analysis: Surgical preference was higher in undergraduates (Tier-1 pooled\u0026thinsp;\u0026asymp;\u0026thinsp;33.0%) compared with interns\u0026ensp;(Tier-2\u0026thinsp;\u0026asymp;\u0026thinsp;22.1%). Time: the weighted pooled proportion indicated 57.5% decided during clinical rotations/final years. Determinants (pooled/proxy): personal interest 75.1%, clinical rotations influence\u0026thinsp;~\u0026thinsp;62.2%, work\u0026ndash;life considerations\u0026thinsp;~\u0026thinsp;64.2%; ~11\u0026ndash;13% remained undecided. Only\u0026thinsp;~\u0026thinsp;34% (in studies reporting guidance; n\u0026thinsp;=\u0026thinsp;1,453) received any form of career counselling. Between-study heterogeneity was high; most studies had moderate RoB.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMedical trainees in Nigeria strongly prefer surgical and core clinical specialties, with interest in diagnostic/non-clinical specialties remaining persistently low. The majority of choices consolidate during clinical years and are mainly influenced by personal interest, exposure and perceived prospects. Interventions, including earlier structured career guidance, enhanced exposure and speciality-specific mentorship for under-selected specialities, are required to rebalance the workforce imbalance.\u003c/p\u003e","manuscriptTitle":"Evaluating Speciality Preferences and Career Choices Among Nigerian Medical Students, Interns, and Preresidency Doctors: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:52:39","doi":"10.21203/rs.3.rs-8470357/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5cc7e9dd-5dbc-4549-acaf-875adb3f2e67","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T20:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:52:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8470357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8470357","identity":"rs-8470357","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}