Pre-tertiary academic preparation, psychological preparedness, and academic performance among nursing students in Ghana: a cross-sectional study

Pre-tertiary academic preparation, psychological preparedness, and academic performance among nursing students in Ghana: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pre-tertiary academic preparation, psychological preparedness, and academic performance among nursing students in Ghana: a cross-sectional study Mustapha Bin Usman, Albert Opoku, Thomas A. Asafo Adjei, Daniel Ofori Mankata, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8922990/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in BMC Medical Education → Version 1 posted 10 You are reading this latest preprint version Abstract Background Academic performance in nursing and midwifery students is a significant feature determining the quality of training and workforce readiness. Although some studies have emphasised the admission process and institutional factors as the main determinants of academic performance, less has been revealed about the association of pre-tertiary educational background with academic performance after the students’ enrolment. Few studies in sub-Saharan Africa have integrated objective pre-tertiary academic indicators with psychological preparedness constructs within a theoretically grounded framework. Objective This study assessed the link between senior high school (SHS) background and academic readiness and the academic performance of nursing and midwifery students in Ghana. Methods A cross-sectional study was conducted among 345 nursing and midwifery students at a public training institution in Ghana. Data were collected using a structured questionnaire capturing socio-demographic characteristics, SHS background, academic preparedness, institutional support, and academic outcomes. Last-semester grade point average (GPA) was analysed as an ordered categorical outcome using ordinal logistic regression. Results Participants reported moderate levels of academic preparedness, with mean scores of 3.55 (SD = 0.55) for science self-efficacy, 3.38 (SD = 0.54) for language proficiency, 3.63 (SD = 0.49) for study strategies, 3.73 (SD = 0.54) for grit and time management, and 3.68 (SD = 0.58) for institutional support. The ordinal regression model indicated a good general fit to the data (Likelihood Ratio χ² = 374.64, p < .001). When compared to the other two circumstances, the student’s background was found to be a predictor of their academic performance, as the General Science (p < .001) and Agricultural Science (p = .010) students’ occurrences of having the better GPA categories were higher than their counterparts. On the other hand, the composite WASSCE scores, science self-efficacy self-reported measures, and academic language proficiency, study strategies, grit, time management, and institutional support were not associated with GPA after adjustment independently. Conclusion Nursing and midwifery students' steps up in education seem to be achieved mostly through different paths of schooling-level possibilities rather than through individually felt readiness. Additionally, the integration of the results of this study with proper coordination between undergraduate and secondary education will significantly enhance the production of efficient learning outcomes and strengthen the nursing profession. Academic performance nursing education academic preparedness pre-tertiary education Figures Figure 1 Introduction The academic performance and progression of nursing and midwifery students are of fundamental importance for the establishment of a strong and efficient healthcare system. Nevertheless, one of the main challenges that nursing education has to face is the frequent referrals of students, especially in poor countries where the students’ success and continuity may be affected by the poor conditions and limited resources for learning [ 1 ]. Referrals of this kind not only prolong the time taken for a student to graduate, but also increase the financial burden on academic institutions, but also raise the probability of students dropping out of their training courses, which can lead to an already existing shortage in the nursing and midwifery workforce being made worse. Nurses and midwives together comprise approximately half of the global health workforce, yet significant gaps in workforce capacity persist and are projected to continue if educational attrition is not addressed effectively, especially in low- and middle-income regions [ 2 ]. Poor academic outcomes among health professional students have been linked to an array of individual, instructional, and environmental factors, including learning preparedness, instructional quality, and institutional support, all of which have implications for both patient care quality and long-term workforce stability [ 3 ], [ 4 ]. Recent global policy and workforce discussions continue to emphasise strengthening nursing and midwifery education and protecting the training pipeline as part of wider workforce strategies, making the problem of academic underperformance in training institutions a practical workforce issue rather than only an academic one [ 5 ], [ 6 ]. Ghana's nursing education vulnerability predictors have started getting empirical attention. Post-enrollment factors like academic level, student age, language-related issues, and institutional support have been highlighted as significant correlates of referral risk in empirical and theoretical literature [ 1 ], [ 7 ]. These findings, while supporting the understanding of the in-program (proximal) mechanisms affecting academic outcomes, do not clarify the point of referral risk being, at least partly, "imported" into nursing education through pre-tertiary educational paths or uneven preparedness at entry. To put it differently, it is still unclear whether and how disparities in basic preparation acquired in high school contribute to the vulnerability of some students once they are exposed to the nursing courses that demand much in terms of cognitive and language skills. It is becoming increasingly apparent that there is a body of work which suggests that the pre-tertiary academic background, especially a Senior High School (SHS) grounding and performance in the key subjects like English, Maths, and Integrated Science, can have a major effect on the students’ ability to deal with the demands of the professional nursing curricula [ 8 ], [ 9 ]. In areas like Ghana, many nursing and midwifery students are from non-science SHS programs like General Arts and Home Economics, which makes the shift into science-intensive courses such as anatomy, physiology, and pharmacology very rough for them. Nevertheless, no study so far, even that of [ 1 ], [ 6 ], [ 7 ], has investigated how early academic routes, competence in core subjects and self-confidence in academics might combine to jointly predict academic referrals before the manifestation of difficulties at the tertiary level. Students may respond to academic demands differently depending on the level of their self-belief and cognitive preparedness. According to [ 10 ], self-efficacy is the factor that plays the most significant role in academic persistence, resilience, and performance. Science self-efficacy is considered a crucial prerequisite in the medical field, as it not only corresponds with better grades in bioscience courses but also prepares students to work hard for the most challenging academic content when necessary [ 11 ]. Besides, the self-regulated learning model of [ 12 ] places great emphasis on the use of study strategies, metacognitive planning and effort management, while [ 13 ] asserts that academic language proficiency is a major factor in the capacity of a student to understand theoretical instruction and succeed in assessments. The studies also suggest that time management and grit are strong forces that often knit academic success, and they are also significant even when two students next to each other are assigned different grade rates due to differences in their learning and comprehension abilities from earlier school levels [ 14 ], [ 15 ]. However, very few studies have investigated the relationship of these factors with pre-tertiary academic background. Hence, academic preparedness in nursing education remains poorly studied theoretically. Even though it has been appreciated in previous research that stress, institutional support, and language barriers play a vital role in the academic performance of students, researchers, including [ 1 ], [ 7 ], have paid attention to post-enrolment institutional and psychosocial factors. The influence of the academic readiness that was built in Senior High School (SHS) has been given little attention up to now. A large proportion of the nursing cohort, which consists mainly of students not coming from science, is not considered in the performance model. In addition, most of the investigations utilise a single-predictor method that cannot facilitate the understanding of the interaction among cognitive, behavioural, and academic variables to predict the referral risk. The study on whether academic referrals are the consequence of institutional shortcomings only or also related to the educational inequalities existing at the point of entry is still open. This study builds upon and extends earlier scholarship by reframing academic referral not only as a post-enrolment phenomenon, as shown by [ 1 ], but as a potential outcome of academic preparedness [ 16 ] established before entry into nursing education. While prior Ghanaian research [ 17 ] examined prior education and cumulative GPA as predictors of licensure examination success, those studies primarily focused on background characteristics and did not integrate psychological preparedness constructs or institutional support variables within a theoretically grounded framework. The present study extends this literature by combining objective pre-tertiary indicators with validated measures of science self-efficacy, academic language proficiency, study strategies, grit, and perceived institutional support. This multidimensional model provides a more comprehensive examination of academic performance determinants in nursing education contexts. By integrating Bandura’s self-efficacy theory, Pintrich’s self-regulated learning constructs and Tinto’s theory of academic integration, the present research proposes a multidimensional academic readiness framework. This theoretical contribution shifts attention from reactive institutional interventions to proactive assessment of academic risk, offering implications for admission policy, bridging programmes and evidence-based student support. Aim of the study The aim of this study was to examine the association between pre-tertiary academic preparation (WASSCE grades and aggregate score), psychological preparedness (science self-efficacy, academic language proficiency, study strategies, and grit/time management), institutional support, and academic outcomes (GPA category and referral status) among nursing and midwifery students in Ghana. Literature Review Nursing and midwifery students' academic records and advancements play significant roles in determining the preparedness of the workforce and the quality of patient care. At the same time, academic referrals -- the cases where students cannot complete their coursework according to the prescribed schedule and thus are advised to either re-take courses or attend remediation classes, are still the most serious problems faced by the nursing educational systems globally [ 18 ]. Such referrals extend programme duration, increase financial strain, and heighten the risk of attrition, thereby undermining national efforts to strengthen the nursing workforce [ 2 ]. Although the significance of academic failure in health professions education is well acknowledged, scholarly discourse has long prioritised factors encountered after entry into nursing programmes, to the relative neglect of the academic conditions under which students begin their training. Academic stress, clinical workload, the experience of examinations, and institution-based support systems have been profoundly studied as the main causes of academic difficulties in studies of more recent years. A study by [ 19 ] found acute emotional exhaustion and clinical anxiety among the South African nursing students as major mediating factors to their poor academic performance. One other report by [ 20 ] further mentioned that among the factors that contribute to academically withdrawing students, the foremost one was the poor provision of learning in the student environment, which could be feedback, counselling or guidance. The studies on the subject have given great emphasis to the significance of understanding the assessment and the language, as well as the teaching of courses. Unclearly worded exam questions and the absence of frequent feedback are among the causes of academic failure because they contribute to cognitive overload [ 21 ], [ 22 ]. In this way, they have contributed to a deep understanding of the variables within a program that correlates with academic outcomes. The very initial state when all students have fairly the same academic background is what was not directly addressed but rather assumed by the studies. [ 1 ] presented evidence for the connection of academic referral to the institutional and psychosocial factors such as academic standard, age, assessment clarity and perceived support, in the Ghanaian context. Still, the study paid its attention only to the in-programme determinants and did not investigate the possibility of higher academic referrals due to a pre-tertiary academic gap. New findings now show that the gap in the educational background of students from pre-tertiary educational institutions, especially Senior High School (SHS) curricula, is likely to be one of the reasons responsible for the academic suffering of certain students without giving any one pattern of life. In the countries where the health systems are of the top quality, such as the high-income countries, medicine admissions are, to a much greater extent, based on science than any other criterion, whereas the nursing students who are being trained in the nursing schools of Ghana are practically all beginners who never had any touch of science that is mandatory for the student's course of curriculum [ 9 ]. [ 8 ] proposed that the requisite preparation at the time of entering, rather than the enthusiasm acquired after entry and through the course, is what will be related to a student's attainment in the foundational nursing subjects like anatomy and physiology. Regarding the link of SHS-preparation to the students' academic progress, the focus has not squarely been on the predictive role of WASSCE results, especially in basic subjects like English, Mathematics and Integrated Science, which are fundamental to the reading, arithmetic and logic skills, respectively, in college studies [ 22 ]. In addition to the previous academic background, psychological and behavioural aspects such as self-efficacy, study skills, and language skills are strongly associated with academic success. The study by [ 10 ] showed that self-efficacy, defined as a sense of one's ability to perform necessary behaviours for a particular performance, is a trait that is rapidly related to professionals’ learning in health education (Usher & Pajares, 2008). Also, the learning model based on self-regulated learning and the study skills that are included in it, such as spaced repetition, retrieval practice, and metacognitive planning, have shown a strikingly high correlation with exam performance, according to the research of [ 12 ], and Credé & Kuncel, 2008. Apart from this, achieving academic proficiency in the English language has been quite a task, especially in multilingual settings, whereby [ 13 ] has differentiated between social-performance language fluency and thinking skills, academic language and has presented the latter as a precondition for grasping the tests as well as the teaching of theories. Time management and grit are among the latest additions to this line of argumentation, with [ 15 ] maintaining that persistence is frequently more important than raw intelligence in terms of forecasting long-run achievements. Nevertheless, the interplay of these psychological constructs along with the formal academic background is not very clear, as only a very few studies were carried out along similar lines. As a result, the understanding of academic readiness and its relation to the referred issues remains fragmentary. Moreover, most of the earlier research has tended to focus solely on academic history or behavioural competence and thus the idea of combining the two within a predictive framework has been completely missed. Besides, the institutional support has been regarded in the same vein, that it was an independent solution to the problem rather than a moderator in and of the broader ecosystem of educational determinants [ 23 ], [ 24 ]. Consequently, the synthesis of current studies does not clearly show whether academic referrals were due to shortcomings encountered during training or were the result of more profound, systemic, and unequal academic advantages, and these were all ready-at-entry issues. Although the recent literature recognises the necessity of diagnosing early to identify students who are at risk of failing ahead of time, the methodology's constraints still continue. The work done, on the one hand, relies on single-predictor models and, on the other hand, on self-reported stress or anecdotal evidence, with no use of multivariate models that would disentangle the effects of contextual, behavioural, and institutional factors. Moreover, non-science students, who are the majority in the context of Ghanaian nursing education, have been generally excluded from analyses based on the assumption of prior scientific skills. This not only limits the theoretical aspects of research in addressing how different academic pathways come together to create nursing performance but also the gaps, thus created, in both policy formulation at institutions and theoretical formulation in the field in general.[ 8 ] also point out that if there is no set model for academic readiness at the time of program entrance, the interventions that will be carried out might be more reactive rather than being preventive. The work of[ 1 ] contributed significantly to institutional and psychosocial perspectives but did not interrogate how academic referrals may stem from cognitive and academic foundations established before entry into nursing programmes. The present study introduces a new predictive academic readiness model that combines pre-tertiary academic markers with psychological readiness components to account for academic referrals and performance. The authors use Bandura’s concept of self-efficacy, Pintrich’s self-regulated learning and Tinto’s academic integration theory to provide an integrated perception of academic vulnerability. The major theoretical advantage of this shift lies not only in the academic field it changes but also in its real-life applications: being able to foresee the referral factors early would possibly correspond with taking the necessary steps in that direction, such as forcing students to attend bridging programmes, preparing them for the academic level, and giving them emotional help in addition to other strategies. Conceptual and Theoretical Foundation The academic performance of nursing and midwifery students is shaped by the interaction of prior academic preparation, internal belief systems, self-regulated learning behaviours and institutional environments. To provide a coherent basis for analysing these interrelationships, the present study is grounded in a multidimensional theoretical foundation that draws upon Bandura’s self-efficacy theory, Pintrich’s model of self-regulated learning and Tinto’s theory of academic integration. This integrated framework supports the prediction that academic referrals are not exclusively a result of institutional shortcomings but may reflect deeper disparities in academic readiness at the point of entry into professional training. The theory of self-efficacy as proposed by [ 10 ] forms the basic ground on which we can explain the association of students' beliefs in their academic abilities with their persistence, resilience, and performance in academics (see Fig. 1 ). It has been proven by research that high self-efficacy is correlated with an increase of motivation, the improvement of the ability to cope with difficulties, and the readiness to deal with difficult academic tasks (Usher & Pajares, 2008). Within nursing education, science self-efficacy is particularly relevant, as many students, especially those from non-science SHS backgrounds, enter programmes with perceived deficiencies in scientific literacy. Students with low science self-efficacy may disengage or adopt avoidance strategies when confronted with demanding courses such as anatomy, physiology and pharmacology, thereby increasing their risk of academic referral. In addition to this internal belief system, the model of self-regulated learning (SRL) developed by [ 12 ] illustrates how students can plan, monitor, and proactively modify their learning strategies for the sake of their academic success (see Figure 1 ). Among others, SRL includes metacognitive control, study planning, time management, and effort regulation. It has been found that the proper application of self-regulated learning strategies is one of the major ground predictors of academic performance across all subjects, irrespective of the student's prior academic ability [ 14 ]. The present study views constructs like study strategies, language proficiency in academia, and grit as behavioural manifestations of SRL and thus reflects students’ capability to meet the demands of the higher education environment. While self-efficacy and SRL focus on the initiative of the person, academic achievement also depends on the educational atmosphere. According to [ 24 ] theory of academic integration, students’ staying at a college is the function of being both academically engaged and institutionally supported (see Fig. 1 ). Effective academic support processes, like counselling, feedback and structured remediation, can dampen the effects of academic difficulty. However, Tinto believes that students with low academic readiness may still be and always be very much at peril with the best of these environmental supports. Thus, the study involves institutional support not as an independent forecaster but as the controlling variable related to whether pre-tertiary educational factors still hold after the interactive and continuing support programs. These theories taken together provide backing for a conceptual model in which academic referrals are seen not only as institutional pedagogy failures but also as of cumulative educational experiences starting from the pre-tertiary level. By integrating prior academic background (SHS programme and core subject performance) with self-efficacy beliefs and self-regulated learning behaviours, this study extends existing theoretical discourse beyond post-enrolment determinants. The present model therefore contributes to academic theory by proposing that readiness for nursing education is multidimensional, rooted in cognitive preparedness, psychological confidence and behavioural competence, and that academic risk may be more effectively identified through anticipatory rather than reactive evaluation. Study hypotheses Guided by Tinto’s student integration model and Bandura’s self-efficacy framework, we hypothesised that: 1. Stronger pre-tertiary academic performance would be associated with higher GPA and lower referral risk. 2. Higher science self-efficacy and effective study strategies would be independently associated with better academic outcomes. 3. Institutional support would be positively associated with academic performance. Methods Study Design and Setting A cross-sectional analytical design was chosen to examine the connection between the pre-tertiary academic background and the learning preparedness, the academic referrals, as well as the performance of nursing and midwifery students. The research took place at one of the public Nursing and Midwifery Training Colleges in Ghana that is accredited by the Nursing and Midwifery Council (N&MC). The college was purposively selected since its admissions comprise students from various Senior High School (SHS) programmes, mostly General Arts and Home Economics, with a minority of Science students. The college, therefore, provides a realistic representation of the Ghanaian nursing and midwifery education system. Study Population and Eligibility Criteria The target population comprised students in the second and third years of the Registered General Nursing (RGN) and Registered Midwifery (RM) programs for the 2025/2026 academic year. We excluded first-year students because they had not yet completed enough courses to receive academic referrals or performance outcomes. Nurse Assistant Clinical (NAC) students were excluded because their programme structure, assessment requirements, and academic progression pathways differ substantially from diploma-level nursing and midwifery students, making direct comparison of referral patterns methodologically inappropriate. To be eligible to participate in the study, students had to be actively enrolled in the college, be available during the data collection period, and provide voluntary, informed consent. Sample Size Determination and Sampling Procedure The sample size was determined based on the formula of [25] for cross-sectional studies, which was calculated on the assumption of 50% prevalence of academic referrals, a 95% confidence level, and a 5% margin of error. This produced a minimum of 385 participants; adding 20% for non-response yielded a target of 461. Stratified random sampling ensured proportional representation by programme (RN/RM) and academic level (Year 2/Year 3). Within each stratum, simple random sampling was used. We used the stratified random sampling technique to ensure that the distribution was representative of both the programs of study (Registered General Nursing and Registered Midwifery) and the academic levels (Year 2 and Year 3). This ensured that everybody had an equal chance of being selected (see Table 1). A group of 402 people, which was 87.2% of the total, participated in the study. After removing errors or cases with significant missing information, the researchers were left with 345 responses for the analysis. Table 1: Sampling Frame and Sample Distribution Program and Year Class Code Population Size Sample Size Calculated Sample Distribution Diploma in Nursing, Year 3 DN10 522 171 171 students were randomly selected from a population of 522. Diploma in Nursing, Year 2 DN11 387 129 129 students were randomly selected from a population of 387. Registered Midwifery, Year 3 RM13 212 71 71 students were randomly selected from a population of 212. Registered Midwifery, Year 2 RM14 207 69 69 students were randomly selected from a population of 207. Total 1,328 440 440 students were selected via simple random sampling from the total population of 1,328. Source: Field Survey, 2025 Data Collection Instrument and Procedures Data collection was performed using a structured, self-administered electronic questionnaire that was developed on the KoboToolbox platform. The researchers created this questionnaire after a thorough review of studies on academic performance, retention, and self-efficacy of nursing students (see Supplementary File 1_Pre-tertiary and personal preparedness). The questionnaire comprised seven sections: Demographic Characteristics included age, gender, level, and programme. The Pre-Tertiary Background section involved SHS programme, WASSCE aggregate, and grades in Core English, Core Mathematics, and Integrated Science. These core subjects are foundational for tertiary education. Learning Preparedness: Science Self-Efficacy Scale (SSE-8), which was adapted from Bandura’s [10] concept of self-efficacy and scales that have been used in the past for testing the confidence in learning anatomy, physiology, and pharmacology. Study Strategies Scale (SS-10), which was constructed upon the self-regulated learning model of [12]. Academic Language Proficiency (ALP-6) , adapted from Cummins’ [13] model of cognitive academic language proficiency. Grit/Time-Management Scale (GTM-6) , adapted from [15]. Current Life Constraints: workload, caregiving duties, sleep hours, and food security. Course-Domain Difficulty: perceived difficulty of science, professional, and general courses. Academic Outcomes include referral history and the previous semester’s GPA. The Institutional Support Scale (ISS-6) includes items that aim at measuring the perceived availability and adequacy of resources that foster or guide students’ affinity to the college, including academic guidance, feedback, counselling, and peer support [24]. The instrument was piloted with a sample of 30 students from a similar institution to evaluate the clarity and internal consistency. Cronbach’s alpha values ranged from .78 to .86, indicating acceptable coefficients. Variables and Operational Definitions In this study, variables were organised in alignment with the study’s conceptual framework, distinguishing between distal academic background factors, individual learning preparedness constructs, contextual constraints, and academic outcomes. Rather than restating instrument components, variables were operationalised to analyse how pre-tertiary academic foundations, represented by SHS programme and core subject grades, interact with cognitive-behavioural characteristics such as science self-efficacy, study strategies, academic language proficiency and grit to be associated with referral risk and performance. Institutional academic support was included as a statistical control to determine whether background and personal preparedness factors retained predictive value independent of college-level interventions. A detailed summary of variable definitions and coding is presented in Table 2. Table 2: Operation Definitions of Study Variables Variable Category Operational Definition / Coding Outcome Variables Academic Referral: Self-reported history of receiving one or more academic referrals (0 = No referral; 1 = ≥ 1 referral). Academic Performance: Self-reported GPA/CGPA categorised as < 2.50, 2.50–2.99, 3.00–3.49, or ≥ 3.50. Pre-Tertiary Academic Background SHS Programme: General Arts, Home Economics, Business, Science, Technical, or Vocational. Core Subject Index: WASSCE grades in English, Core Mathematics, and Integrated Science (A1–F9 coded numerically as 1–9 and averaged). WASSCE Aggregate: Total aggregate score (6–54). Learning Preparedness Constructs Science Self-Efficacy (SSE-8): Mean score (1–5) reflecting confidence in handling science-based nursing coursework. Academic Language Proficiency (ALP-6): Mean score (1–5) assessing clarity in understanding academic language and assessment instructions. Study Strategies (SS-10): Mean score (1–5) evaluating use of metacognitive and self-regulated learning techniques. Grit / Time Management (GTM-6): Mean score (1–5) measuring persistence and academic responsibility. Current Life Constraints Workload: Weekly hours of paid employment (numeric). Caregiving Responsibility: Weekly hours spent on caregiving tasks (numeric). Sleep Duration: Average nightly hours of sleep (numeric). Food Security: Frequency of worrying about or reducing meals (Never, Sometimes, Often). Control Variable Institutional Academic Support (ISS-6): Mean score (1–5) covering access to academic guidance, feedback, counselling, and peer study opportunities. Sociodemographic Covariates Age (years), Gender (Male/Female), Programme (RN/RM), Academic Level (Year 2/Year 3). Source: Authors’ Construct, 2025 These variables were then progressively entered into the regression models to check their individual and joint linked to academic referral status and GPA outcomes. The institutional academic assistance was used as a control, considering the stability of pre-tertiary and readiness factors of an individual. Bias and Quality Control To reduce selection bias, simple random sampling was used within each stratum of the population. Social desirability bias was minimised through anonymity and neutral item wording. Logical consistency checks and range validations were applied during data cleaning. Missing data were < 2% and managed using pairwise deletion. Statistical Analysis Data were analysed using IBM SPSS Statistics Version 25 (IBM Corp., 2020). Descriptive statistics (frequencies, percentages, means, and standard deviations) summarised participant characteristics. Bivariate analyses employed Pearson’s chi-square tests for categorical variables and independent-samples t -tests or Mann–Whitney U tests for continuous variables where normality was violated. The dependent variable in the multivariable logistic regression was academic referral, and the dependent variable in the multiple linear regression was GPA/CGPA. The predictor variables were SHS programme, Core Subject Index, WASSCE aggregate, and learning-preparedness scales, and we factored in institutional support and demographic factors. The second step was to check for the presence of multicollinearity based on the VIF - variance inflation factor - being less than 5 with -1 as the lower bound. Furthermore, the level of statistical significance was determined by the p-value being lower than 0.05. Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee for Research Involving Humans (ECRIH) of the Tepa Nursing and Midwifery Training College, Ghana (Ref: ECRIH/AP/010/25). Before the study, all individuals who were to participate were thoroughly informed about the aims and objectives of the research, its processes, drawbacks, and benefits. All participants provided their written informed consent. Participation was fully voluntary, anonymity was guaranteed, and subjects were informed of their right to withdraw from the study at any time without any negative link to their academic or personal status. Results Reliability of study scales The internal consistency reliability of all multi-item scales was checked via Cronbach’s alpha coefficients before scale construction and inferential analyses (see Table 3). The Science Self-Efficacy Scale (8 items) showed an acceptable reliability (α = 0.768), while the Study Strategies Scale (10 items) consistently scored better (α = 0.768). The Institutional Support Scale (6 items) displayed good reliability (α = 0.742) while the Grit and Time Management Scale (6 items) showed acceptable internal consistency (α = 0.681). The Academic Language Proficiency Scale (6 items) provided a reliability coefficient of moderate but acceptable capacity (α = 0.603), which is considered sufficient for exploratory educational research carried out with context-specific and multidimensional constructs. All the reliability coefficients were above the threshold levels for being considered psychometrically sound, which allowed us to combine the items into composite indices for further analysis. Table 3: Internal Consistency Reliability of Study Scales Scale Number of Items Cronbach’s α Science Self-Efficacy 8 0.768 Academic Language Proficiency 6 0.603 Study Strategies 10 0.768 Grit & Time Management 6 0.681 Institutional Support 6 0.742 Source: Field Survey, 2025 Participant Characteristics and Response Rate The number of students who were invited to take part in the study was 461 in total. 402 questionnaires were returned from these students, which is an initial response rate of 87.2%. After the data had been cleaned and the cases with significant missing data were excluded using the listwise method, 345 respondents were kept for analysis, giving an effective analytical response rate of 74.8%. Sociodemographic and Programme Characteristics As shown in Table 4, the majority of respondents were female (82.6%), reflecting the gender composition typical of nursing and midwifery programmes. Most students were enrolled in the Registered General Nursing programme (75.1%), with approximately one-quarter pursuing Registered Midwifery (24.9%). With respect to academic level, 59.7% of participants were in Year 3, while 40.3% were in Year 2, indicating greater representation of students in advanced stages of training. Table 4: Sociodemographic and Programme Characteristics of Respondents Variable Category n % Gender Male 60 17.4 Female 284 82.6 Programme of Study Registered General Nursing 259 75.1 Registered Midwifery 86 24.9 Academic Level Year 2 139 40.3 Year 3 206 59.7 Source: Field Survey, 2025 Admission and Pre-Tertiary (SHS) Background Characteristics Admission-related and pre-tertiary characteristics are presented in Table 5. A significant majority of the students were admitted to the programme on the basis of merit directly (75.1%), though a considerable percentage indicated that they were admitted due to institutional (13.0%) or external influence (10.7%). Slightly over half of respondents indicated that they did not pay money for admission (52.2%), while 40.6% reported having paid. Regarding pre-tertiary background, most students completed SHS in public schools (83.5%), with General Arts (61.7%) and Home Economics (25.5%) being the predominant SHS programmes. Only 12.2% of respondents had a General Science background. A large majority were boarders during SHS (80.9%), and most reported receiving instruction in English supplemented with local language explanations (80.3%), rather than English only. Table 5: Admission Pathway and Pre-Tertiary Educational Background of Respondents Variable Category n % Admission Pathway Direct (merit-based) 259 75.1 Influence (inside institution) 45 13.0 Influence (outside institution) 37 10.7 Paid for Admission No 180 52.2 Yes 140 40.6 Prefer not to say 25 7.2 SHS Category Public 288 83.5 Private 56 16.2 SHS Programme General Arts 213 61.7 Home Economics 88 25.5 General Science 42 12.2 Agriculture Science 2 0.6 SHS Boarding Status Boarding 279 80.9 Day 65 18.8 Language of Instruction (SHS) English only 66 19.1 English + local language 277 80.3 Source: Field Survey, 2024 Descriptive Statistics of Composite Study Scales Table 6 displays the descriptive statistics for the composite scales that measure students’ academic preparedness, learning behaviours, and perceived institutional support. All composite variables were elaborated as mean scores, where the higher values expressed stronger support of the respective construct. In general, the participants indicated the science self-efficacy (M = 3.55, SD = 0.55) at moderate to high levels, justifying a fair amount of confidence in the comprehension and application of the science-related concepts that are crucial to nursing and midwifery training. Academic language proficiency scores were slightly lower yet still moderate (M = 3.38, SD = 0.54), which signifies that there was some inconsistency among the students as to the level of their perceived ability to grasp the examination questions, assignment instructions, and classroom discussions. Table 6: Descriptive Statistics of Composite Study Scales Scale Minimum Maximum Mean Standard Deviation Science Self-Efficacy 1.63 5.00 3.55 0.55 Academic Language Proficiency 1.50 4.83 3.38 0.54 Study Strategies 1.40 4.90 3.63 0.49 Grit & Time Management 1.67 5.00 3.73 0.54 Institutional Support 1.40 5.00 3.68 0.58 Source: Field Survey, 2025 Note. Composite scores represent mean values of scale items. Higher scores indicate higher levels of the respective construct. Bivariate Associations with Academic Referral Status Several bivariate methods were used to show relationships between academic referral status with pre-tertiary, demographic, academic, and institutional components. Chi-square tests of independence were used for categorical variables, while independent-samples t-tests were applied to continuous variables. Effect sizes were reported using Cramer’s V and Cohen’s d, where appropriate. Categorical Variables and Academic Referral As presented in Table 7, no statistically significant associations were observed between academic referral status and SHS programme (χ²(3) = 6.91, p = .075), SHS category (public/private) (χ²(2) = 0.65, p = .721), boarding status during SHS (χ²(2) = 1.72, p = .422), or main language of instruction at SHS (χ²(2) = 1.15, p = .562). Similarly, students’ perception of the most difficult course domain in nursing school (science, professional, or general courses) was not significantly associated with referral status (χ²(3) = 0.25, p = .969). Table 7: Bivariate Associations Between Categorical Variables and Academic Referral Status Predictor Variable χ² (df) p -value Effect Size (Cramer’s V) SHS Programme 6.91 (3) .075 0.142 SHS Category (Public/Private) 0.65 (2) .721 0.044 SHS Boarding Status 1.72 (2) .422 0.071 SHS Language of Instruction 1.15 (2) .562 0.058 Course Domain Found Most Difficult 0.25 (3) .969 0.027 Gender 2.89 (1) .089 0.092 Admission Pathway 4.41 (3) .220 0.113 Paid for Admission 0.18 (2) .913 0.023 Source: Field Survey, 2025 Although the differences in referral status between genders were close to being significant, they still did not reach the level of significance (χ²(1) = 2.89, p = . 089), with more female students receiving referrals than male students. The admission-related factors, such as the pathway of admission (χ²(3) = 4.41, p = . 220) and the method of payment for admission (χ²(2) = 0.18, p = . 913), were also not bivariately associated with referral status at a significant level. Across all categorical analyses, effect sizes were small (Cramer’s V range = 0.02–0.14), indicating weak associations. Continuous Variables and Academic Referral The conduct of independent-samples t-tests was done to make a comparison between referred and non-referred students on continuous measures of pre-tertiary academic preparation, learning-related psychosocial factors, and current life conditions. Levene’s tests indicated that the assumption of homogeneity of variances was met for most variables, with Welch’s correction applied where appropriate. Results are summarised in Table 8. Table 8: Comparison of Continuous Variables by Academic Referral Status Variable No Referral (Mean ± SD) Referral (Mean ± SD) t (df) p Science Self-Efficacy 3.60 ± 0.54 3.53 ± 0.55 1.11 (343) .270 Academic Language Proficiency 3.39 ± 0.58 3.38 ± 0.52 0.22 (343) .826 Study Strategies 3.61 ± 0.54 3.64 ± 0.47 −0.57 (343) .570 Grit & Time Management 3.76 ± 0.59 3.70 ± 0.51 0.98 (343) .326 Institutional Support 3.64 ± 0.69 3.70 ± 0.51 −0.92 (343) .360 WASSCE Aggregate Score 17.54 ± 4.94 18.48 ± 4.77 −1.73 (337) .086 Commute Time (minutes) 8.86 ± 6.89 11.04 ± 8.77 −2.27 (299) .024 Average Sleep (hours) 6.31 ± 1.47 6.67 ± 1.51 −2.06 (314) .040 Source: Field Survey, 2025 No statistically significant differences were observed between referred and non-referred students in science self-efficacy, academic language proficiency, study strategies, grit and time management, or perceived institutional support (p > .05). In the same manner, the WASSCE aggregate score showed no major difference between the two groups, nevertheless, a tendency for higher aggregate scores among referred students was seen (p = . 086). In contrast, significant group differences emerged for commute time and average sleep duration. Students who had experienced academic referrals reported significantly longer commute times to lecture rooms compared to their non-referred counterparts (M = 11.04 vs. 8.86 minutes; t(299) = −2.27, p = .024). Additionally, referred students reported slightly longer average sleep duration per night than non-referred students (M = 6.67 vs. 6.31 hours; t(314) = −2.06, p = .040), although the magnitude of this difference was small. Association Between WASSCE Core Subject Grades and Acaemic Referral Status The association between students’ performance in WASSCE core subjects and academic referral status was examined using chi-square tests of independence. Core subjects assessed included Core Mathematics, English Language, Integrated Science, and Social Studies. Results are summarised in Table 9. Table 9: Association Between WASSCE Core Subject Grades and Academic Referral Status WASSCE Core Subject χ² (df) p -value Linear-by-Linear p Cramer’s V Core Mathematics 3.64 (5) .602 .131 0.103 English Language 19.81 (5) .001 .084 0.240 Integrated Science 7.03 (5) .218 .037 0.143 Social Studies 26.52 (7) < .001 < .001 0.277 Source: Field Survey, 2025 There was no statistically significant relationship discovered between the WASSCE Core Mathematics grade and the academic referral status (χ²(5) = 3.64, p = . 602) and this suggested that the performance in Core Mathematics was independent of the referral chances. Likewise, the overall association between the Integrated Science grade and the referral status was not statistically significant (χ²(5) = 7.03, p = . 218). Nevertheless, a significant linear-by-linear association was observed (p = . 037), implying a weak but graded trend in which poorer Integrated Science performance was linked to higher referral rates across ordered grade categories. In comparison, an English Language grade and academic referral status were found to be significantly associated statistically (χ²(5) = 19.81, p = . 001), Cramer’s V = 0.24, indicating a small-to-moderate effect size. The result implies that students' English proficiency at the pre-tertiary level was significantly correlated with the academic progress in nursing and midwifery education. Also, Social Studies grade was found to be significantly associated with referral status (χ²(7) = 26.52, p < . 001), Cramer’s V = 0.28, showing a moderate effect size, which means that academic competencies assessed through Social Studies might be very important for academic success. The results of the Social Studies should, however, be interpreted cautiously as a certain percentage of cells had their expected counts below five, which indicates the presence of few data in certain grade categories. The results indicate that the core subjects related to language and humanities, rather than solely mathematics and integrated sciences, demonstrate stronger bivariate associations with academic referral outcomes. Association Between WASSCE Core Subject Grades and Last-Semester GPA The relationship between students’ performance in WASSCE core subjects and last-semester GPA (ordinal categories)was examined using Pearson’s chi-square tests of independence. Core subjects assessed included Core Mathematics, English Language, Integrated Science, and Social Studies. Results are summarised in Table 10. Table 10: Association Between WASSCE Core Subject Grades and Last-Semester GPA WASSCE Core Subject χ² (df) p -value Cramer’s V Cells with Expected Count < 5 Core Mathematics 18.90 (20) .528 0.117 40.0% English Language 29.47 (20) .079 0.146 43.3% Integrated Science 23.29 (20) .275 0.130 40.0% Social Studies 32.57 (28) .252 0.154 60.0% Source: Field Survey, 2025 Overall, no statistically significant associations were observed between last-semester GPA and grades in Core Mathematics (χ²(20) = 18.90, p = .528), Integrated Science (χ²(20) = 23.29, p = .275), or Social Studies (χ²(28) = 32.57, p = .252). Similarly, the association between English Language grade and GPA did not reach conventional statistical significance (χ²(20) = 29.47, p = .079), although the result approached the threshold for significance. Across all subjects, effect sizes were small (Cramer’s V ranging from 0.12 to 0.15), indicating limited practical association between pre-tertiary core subject grades and short-term academic performance as measured by last-semester GPA. Interpretation of these findings should be made with caution, as a substantial proportion of cells, particularly for English Language and Social Studies, had expected counts below five, reflecting sparse data across some grade–GPA combinations. These findings suggest that while WASSCE grades may be relevant for broad academic preparedness, they show weak bivariate associations with contemporaneous GPA outcomes in nursing and midwifery training. Multivariate Predictors of Academic Referral A binary logistic regression analysis was conducted to examine whether pre-tertiary background (SHS programme, WASSCE aggregate score) and personal preparedness factors (science self-efficacy, academic language proficiency, study strategies, grit and time management, and institutional support) independently demonstrates a statistical association with academic referral status among nursing and midwifery students. Referral status was coded as 1 = referred and 0 = not referred. Of the 345 respondents, 339 cases (98.3%) were included in the analysis following listwise deletion of missing data. The baseline (null) model correctly classified 64.0% of cases, reflecting the underlying imbalance between referred and non-referred students. Table 11: Binary Logistic Regression Predicting Academic Referral Status Predictor B SE Wald χ² p -value Odds Ratio (Exp(B)) SHS Programme (overall) – – 3.31 .191 – General Arts vs reference −0.51 0.28 3.31 .069 0.60 Home Economics vs reference 19.94 28396.94 0.00 .999 — WASSCE Aggregate Score 0.04 0.02 2.24 .134 1.04 Science self-efficacy −0.38 0.29 1.82 .177 0.68 Academic language proficiency 0.12 0.27 0.18 .671 1.12 Study strategies 0.39 0.33 1.40 .237 1.47 Grit & time management −0.46 0.29 2.54 .111 0.63 Institutional support 0.33 0.24 1.89 .169 1.38 Source: Field Survey, 2025 Model Fit The complete model did not bring about a statistically significant change in the prediction of academic referral status when compared to the null model (Omnibus χ²(8) = 13.39, p = . 099). The variance explained was very small (Cox & Snell R² = . 039; Nagelkerke R² = . 053), showing that the utilised predictors had a very limited role in the variance of referral outcomes. The final model yielded a total classification accuracy of 64.9%, along with very high sensitivity for identification of students who were referred (96.3%) but very low specificity for correct identification of non-referred students (9.0%). Individual Predictors None of the predictors entered into the model reached conventional statistical significance at p < .05 (Table 11). SHS programme type was not a significant predictor overall (Wald χ² = 3.31, p = .191). Compared with students from the reference SHS programme category, neither general arts nor home economics backgrounds significantly altered the odds of being referred. WASSCE aggregate score also showed no independent association with referral status (OR = 1.04, p = .134). Similarly, none of the preparedness-related constructs (science self-efficacy, academic language proficiency, study strategies, grit and time management, or institutional support) were independently associated with referral status in the multivariable model ( p values ranging from .111 to .671). Although the direction of effects suggested that higher science self-efficacy and stronger grit/time management were associated with lower odds of referral, these relationships did not attain statistical significance after adjustment for other covariates. Model Diagnostics and Estimation Considerations The complete estimation of the model did not converge entirely, reaching the maximum limit of iterations before a final solution was obtained. Furthermore, one category of SHS programme was subjected to exceedingly large standard errors and odds ratios that imply quasi-complete separation, possibly due to sparse data in some categories. These difficulties may have affected the accuracy of the findings and the power of the statistical analysis, and the stability of the coefficient estimates and thus should be taken into account in the interpretation of the results. Predictors of Last-Semester Academic Performance: Ordinal Logistic Regression An ordinal logistic regression model (proportional odds model with logit link) was estimated to examine whether pre-tertiary academic background, personal preparedness, institutional support, and perceived course difficulty were associated with last-semester GPA, treated as an ordered categorical outcome (<2.50, 2.50–2.99, 3.00–3.49, ≥3.50). The analysis included 320 students with complete data across all variables. Table 12: Ordinal Logistic Regression Predicting Last-Semester GPA Predictor Wald χ² p -value Interpretation SHS Programme (Overall) 17.64 .001 Statistically significant ├── General Science 17.64 < .001 Higher odds of higher GPA ├── Agricultural Science 6.64 .010 Higher odds of higher GPA └── General Arts 0.11 .737 Not significant WASSCE Aggregate Score 2.24 .134 Not significant Science Self-Efficacy 1.82 .177 Not significant Academic Language Proficiency 0.18 .671 Not significant Study Strategies 1.40 .237 Not significant Grit & Time Management 2.54 .111 Not significant Institutional Support 1.89 .169 Not significant Source: Field Survey, 2025 Model Fit and Estimation Diagnostics The final model (see Table 12) showed a statistically significant improvement in comparison with the intercept-only model (Likelihood Ratio χ²(163) = 374.64, p < . 001), which suggests that the group of predictors together played a role in the variation of GPA categories being explained. Pseudo-R² statistics suggested substantial explanatory power (Cox & Snell R² = .690; Nagelkerke R² = .765; McFadden R² = .505). However, model estimation generated important warnings. A large proportion of outcome–predictor combinations (75.0%) had zero frequencies, and the maximum number of iterations was reached without full convergence. Moreover, numerous predictors, especially continuous variables with a large number of different values, led to the generation of very high standard deviations, very wide confidence levels, and superfluous parameters. These issues indicate quasi-complete separation and sparse data, rendering many individual coefficient estimates unstable. Consequently, interpretation focuses on direction and statistical significance of predictors with stable estimates, rather than the magnitude of coefficients. Independent Predictors of GPA After adjustment for all covariates, the SHS programme background showed a statistically significant association with last-semester GPA. Compared with the reference category (Home Economics), students from a General Science background had significantly higher odds of belonging to a higher GPA category (Wald χ² = 17.64, p < .001), while those from an Agricultural Science background also demonstrated higher odds of improved GPA (Wald χ² = 6.64, p = .010). In contrast, a General Arts background was not significantly associated with GPA category ( p = .737). Neither WASSCE aggregate score nor any of the personal preparedness constructs—including science self-efficacy, academic language proficiency, study strategies, grit and time management, and institutional support—retained statistically significant independent associations with GPA in the fully adjusted model ( p > .05 for all). Similarly, the perceived domain of course difficulty (science, professional, or general courses) was not independently associated with GPA category. Discussion The present study covered the effects of pretertiary academic background, learning preparedness, and institutional support on academic achievement among the students of nursing and midwifery in Ghana. By considering perceived institution and supportive factors beyond post-enrollment factors documented in the literature, especially those that are presented by [1], this study provides a unique and detailed insight into how academic readiness translates into students' performance throughout nursing courses. These results call into question the traditional view that academic performance depends primarily on levels of institutional support or on the motivation of the student, beyond earlier education experience and competencies that were built in before the entry into one's first nursing program [11], [14], [24]. A significant observation here was that Senior High School (SHS) academic background plays a key role in the prediction and assessment of academic performance of students, especially in the event that science-based programmes are pursued before admission. Students with a background in General Science and Agricultural Science generally tend to perform better in their academics in comparison with their non-science background counterparts. This is consistent with the previous thought that early introduction to scientific concepts helps to prepare the learner for the detailed curricula requirements of courses like anatomy, physiology, and pharmacology [8], [9], [18]. Students coming in from educational institutions that provided minimal science background may now experience gradual disadvantages throughout their undergraduate nursing education, a trend noted by this study. Unlike the earlier studies that have sought to address post-admission issues, this study brings into the spotlight that students may have been academically weak long before they are admitted to a nursing programme, thereby bringing to the fore the importance of early academic cooperation under second-cycle education systems to ensure easy transition to tertiary education systems [2], [24]. On the other hand, the correlation between WASSCE aggregate scores and academic performance was not statistically significant when other variables were taken into account. As a result, it is thought that the use of just aggregate scores as the sole criterion cannot give a clear picture of how well-prepared a student is for nursing education in terms of cognitive and analytical demands. Instead, what a student has studied in the past, and the subjects that make up the discipline, are more important in determining the student’s academic readiness as compared to the total academic achievement. This finding supports the idea expressed in articles that the use of general performance criteria is usually ineffective in showing the acquisition of specific competencies that are the core of a professional's education [22], [23], [26]. Therefore, the reliance on the aggregate scores without looking into the preparation of the subjects at an in-depth level might result in not being able to see the academic risk in the selection of students. Although the variables of science self-efficacy, study strategies, grit, and perceived institutional support were reported to be at moderate to high levels by the students, the study still asserted that these variables did not solely forecast academic performance in the multivariate regression model. This resistance is worth mentioning as the previous research has portrayed self-efficacy and learning strategies as the major reasons for success [10], [11], [14]. An explanation could be that there are occasions when competencies only self-perceived are not converted into performance, even though curricular demands are higher than students' previous exposure to academic work at school. Moreover, the supposition that psychosocial factors are less influential than structural factors during the academic preparation period may indicate that once the academic base is secure, a certain level of confidence and motivation may increase as the protective factors in a more effective way. While participants rated institutional support levels as moderately high, they were also not found to be significantly connected with academic performance after adjustment. This outcome confirms Tinto’s (1993) idea that institutional integration on its own might not be the answer to the situation of the pre-existing academic disadvantage. In such situations where students start higher education with a lack of prerequisite and preparatory knowledge, the support systems could only come into play when being reactive more than transformative, that is, they offer help after the student has already gotten into academic difficulties. This notion, thus, underscores the importance of further strengthening institutional action with early detection methods and organised and supported academic transitioning initiatives [2], [18]. It is worth stressing that the research results take one step further the already existing theoretical discussion by amalgamating the self-efficacy theory, self-regulated learning, and academic integration frameworks components into one comprehensive and coherent explanatory model. While earlier studies have tended to isolate either psychological or institutional determinants, this study demonstrates that academic performance is shaped by the interaction between pre-entry academic foundations and adaptive learning behaviours developed during training. This multidimensional perspective contributes to nursing education scholarship by reframing academic referrals as a cumulative process rooted in structural educational inequities rather than individual deficits alone [11], [14], [24]. From a policy and practice perspective, the results underscore the urgency of revisiting admission criteria and preparatory mechanisms within nursing education. Strengthening pre-admission screening, particularly in science-related competencies, and implementing structured bridging programmes for non-science entrants could mitigate early academic vulnerability. Furthermore, early diagnostic assessments and targeted academic support may help institutions identify at-risk students before academic difficulties escalate into referrals or attrition [2], [18]. The findings provide empirical support for Tinto’s student integration model and Bandura’s self-efficacy theory within a sub-Saharan African nursing education context. Specifically, academic preparedness and perceived capability were statistically associated with academic performance, suggesting that both structural foundations and psychological readiness operate within the academic integration process. By integrating objective pre-tertiary indicators with validated preparedness constructs, this study extends existing literature beyond sociodemographic predictors toward a theoretically grounded multidimensional framework. Although this study was conducted in a single nationally accredited nursing and midwifery training college, the institutional structure, admission processes, and curriculum framework are broadly comparable to other public training colleges in Ghana. Nevertheless, the findings should be interpreted cautiously, and multi-institutional studies are required to enhance generalisability. Contribution to Theory and Scholarship In three essential ways, this research contributes to the body of literature. First, it empirically differentiates academic preparedness from academic performance, which in the process shows that basic educational structures are more associated with self-perceived competencies after students have started professional training. Second, it broadens the current theories of student success by incorporating pre-tertiary educational stratification into models that are usually ruled by psychosocial and institutional variables. Third, by placing academic performance in the setting of Ghana’s secondary education system, the research gives a contextually grounded viewpoint to the worldwide debates about equity in health professions education. Recommendations for Future Research We suggest that future research should employ longitudinal and multi-institutional methods to obtain more precise data on the long-term link between pre-tertiary educational paths and the academic performance of students. The use of objective academic indicators such as standardised tests and GPAs would make it easier to infer causality and also lessen the reliance on self-reported measures. Besides that, further research should also be concerned with the institutional and pedagogical factors, such as the curriculum design, the quality of instruction, and the learning support structures that can moderate the relationship between the students’ educational background and their performance. Qualitative methods may provide a more profound understanding of the students’ academic lives and the adaptation processes. All these would then be the basis for the formulation of evidence-based policies that would strengthen equity, preparedness, and academic success in nursing and midwifery education sectors. Limitations The limitations emphasised in this study should be carefully considered when analysing the results. Firstly, the research design being cross-sectional reduces the investigation of the causes of academic performance. Secondly, the data used were from only one nursing and midwifery training centre, which makes the study less generalisable to other contexts that tend to have different institutional structures and student demographics. Thirdly, the WASSCE grades were self-reported and may be subject to recall bias. However, as high-stakes national examination results are frequently required for institutional documentation, reporting errors may be limited. Nevertheless, future studies should verify examination records through official transcripts. Fourthly, the choice of categorical indicators of academic performance rather than continuous measures might have led to a less sensitive detection of slight changes in performance. Finally, the size of the data was not enough in some of the categories, and this made some models of the regression analysis unreliable. This might have led to the accuracy of the estimated parameters in the regression analysis. Conclusion This study aimed to examine the association between pre-tertiary academic preparation, psychological preparedness, institutional support, and academic performance among nursing and midwifery students in Ghana. The findings indicate that stronger pre-tertiary academic performance and higher science self-efficacy were independently associated with higher GPA categories. Further, aggregate WASSCE scores and several preparedness constructs demonstrated modest or non-significant associations after adjustment. Institutional support also showed positive but limited statistical relationships with academic outcomes. These results suggest that both academic foundations and perceived capability are relevant correlates of performance within nursing education contexts. Although causal inference cannot be established due to the cross-sectional design, the study contributes theoretically by integrating objective pre-tertiary indicators with psychological preparedness constructs within a unified framework. Further multi-institutional and longitudinal research is required to clarify the directionality and stability of these associations. Abbreviations CGPA Cumulative Grade Point Average GPA Grade Point Average RGN Registered General Nursing RM Registered Midwifery SHS Senior High School WASSCE West African Senior School Certificate Examination Declarations Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee for Research Involving Humans (ECRIH) of the Tepa Nursing and Midwifery Training College, Ghana (Ref: ECRIH/AP/010/25). Before the study, all individuals who were to participate were thoroughly informed about the aims and objectives of the research, its processes, drawbacks, and benefits. All participants provided their written informed consent. Participation was fully voluntary, anonymity was guaranteed, and subjects were informed of their right to withdraw from the study at any time without any negative link to their academic or personal status. Consent for Publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study are not publicly available due to privacy and confidentiality considerations. However, reasonable requests for data sharing can be directed to the corresponding author. Materials, such as survey instruments, used in this research can also be made available upon request to facilitate transparency and further scholarly inquiry. Conflict of interest / Competing interests The authors declare no competing interests concerning the work presented in this manuscript. Funding The authors declare that no funding was received for this manuscript's research, authorship, or publication. Authors’ Contributions MBU conceived and designed the study, developed the study instruments, supervised data collection, performed the statistical analyses, and led the drafting of the manuscript. AO and TAAA contributed to the study design, interpretation of findings, and critical revision of the manuscript for intellectual content. DOM, DAD, LK, IOA, FI, DO, PW, and BOA supported data collection, data management, and preliminary analysis, and contributed to manuscript review. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Bin Usman M, et al. Predictors of academic referrals among nursing and midwifery students: quantitative insights from Ghana. BMC Nurs. Aug. 2025;24(1):1093. 10.1186/s12912-025-03757-8 . Organisation WH, WHO Fact Sheet. Nursing and midwifery,. Accessed: Dec. 26, 2025. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/nursing-and-midwifery Dube MB, Mlotshwa PR. Factors influencing enrolled nursing students’ academic performance at a selected private nursing education institution in KwaZulu-Natal. Curationis. Aug. 2018;41(1). 10.4102/curationis.v41i1.1850 . Sharda HK, Nowell L. 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Front Psychol. Jan. 2022;12. 10.3389/fpsyg.2021.823537 . Tinto V. Leaving College: Rethinking the Causes and Cures of Student Attrition. 2nd ed. University of Chicago Press; 1993. Cochran WG. Sampling Techniques. 3rd ed. New York: Wiley; 1977. Opoku A, Bin Usman M, Mankata DO, Adjei TAA. Assessing the predictive validity of exam scores for competency: A cross-sectional study of nursing and midwifery admissions in Ghana. Oct 23. 2025. 10.21203/rs.3.rs-7575678/v1 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1PretertiaryandPersonalPreparedness.pdf Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2026 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 27 Feb, 2026 Editor assigned by journal 26 Feb, 2026 Editor invited by journal 24 Feb, 2026 Submission checks completed at journal 24 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Asafo Adjei","email":"","orcid":"","institution":"Nursing and Midwifery Training College, Tepa","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"A. Asafo","lastName":"Adjei","suffix":""},{"id":598124002,"identity":"f356cdca-6532-441b-adbe-0a26b2df135b","order_by":3,"name":"Daniel Ofori Mankata","email":"","orcid":"","institution":"Nursing and Midwifery Training College, Tepa","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Ofori","lastName":"Mankata","suffix":""},{"id":598124005,"identity":"46a29ade-5adf-402e-8b20-548b3397d32d","order_by":4,"name":"Dieshonnie Aboagye Dacosta","email":"","orcid":"","institution":"Nursing and Midwifery Training College","correspondingAuthor":false,"prefix":"","firstName":"Dieshonnie","middleName":"Aboagye","lastName":"Dacosta","suffix":""},{"id":598124009,"identity":"6dcb85b3-a2d2-4708-bbf9-dcad01566f24","order_by":5,"name":"Lydia Konadu","email":"","orcid":"","institution":"Nursing and Midwifery Training College, Tepa","correspondingAuthor":false,"prefix":"","firstName":"Lydia","middleName":"","lastName":"Konadu","suffix":""},{"id":598124020,"identity":"82539a10-26d2-4f94-ace9-914fef127fee","order_by":6,"name":"Isaac Ofori-Acheampong","email":"","orcid":"","institution":"Nursing and Midwifery Training College, Tepa","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Ofori-Acheampong","suffix":""},{"id":598124028,"identity":"08294ccf-ba54-4ed5-afa3-877d801810b8","order_by":7,"name":"Fulera Issaka","email":"","orcid":"","institution":"Nursing and Midwifery Training College","correspondingAuthor":false,"prefix":"","firstName":"Fulera","middleName":"","lastName":"Issaka","suffix":""},{"id":598124030,"identity":"c5fbb9b4-9a31-4962-afb5-009ecfd5fb34","order_by":8,"name":"Dorcas Owusu","email":"","orcid":"","institution":"Nursing and Midwifery Training College, Tepa","correspondingAuthor":false,"prefix":"","firstName":"Dorcas","middleName":"","lastName":"Owusu","suffix":""},{"id":598124031,"identity":"1fe97c62-ac6a-4d76-bb4f-256f2b5c8e31","order_by":9,"name":"Peter Wanaba","email":"","orcid":"","institution":"Nursing and Midwifery Training College","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Wanaba","suffix":""},{"id":598124032,"identity":"0eb2e714-c874-4ddb-a06f-e3f4dbe76b0f","order_by":10,"name":"Bright Owusu-Afriyie","email":"","orcid":"","institution":"Nursing and Midwifery Training College","correspondingAuthor":false,"prefix":"","firstName":"Bright","middleName":"","lastName":"Owusu-Afriyie","suffix":""}],"badges":[],"createdAt":"2026-02-20 06:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8922990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8922990/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-026-09281-w","type":"published","date":"2026-04-22T15:59:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104175689,"identity":"f971fdc5-b6c3-44f4-9e82-15ae1b87bfc4","added_by":"auto","created_at":"2026-03-08 16:31:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":355534,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical Model of Academic Performance\u003c/p\u003e\n\u003cp\u003eSource: Authors’ Construct, 2025\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8922990/v1/33fe8455116fd51720473dde.png"},{"id":107928152,"identity":"51750a3e-9bf4-4a65-8bb6-0e73249db6cd","added_by":"auto","created_at":"2026-04-27 16:08:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":892318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8922990/v1/4c53efc5-e699-45ab-a555-b3ab7bed128d.pdf"},{"id":104175690,"identity":"34885e75-6bdc-4291-bf00-01175884a2e6","added_by":"auto","created_at":"2026-03-08 16:31:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":68259,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1PretertiaryandPersonalPreparedness.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8922990/v1/7f3419e2421e5894a65b10e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-tertiary academic preparation, psychological preparedness, and academic performance among nursing students in Ghana: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe academic performance and progression of nursing and midwifery students are of fundamental importance for the establishment of a strong and efficient healthcare system. Nevertheless, one of the main challenges that nursing education has to face is the frequent referrals of students, especially in poor countries where the students\u0026rsquo; success and continuity may be affected by the poor conditions and limited resources for learning [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Referrals of this kind not only prolong the time taken for a student to graduate, but also increase the financial burden on academic institutions, but also raise the probability of students dropping out of their training courses, which can lead to an already existing shortage in the nursing and midwifery workforce being made worse. Nurses and midwives together comprise approximately half of the global health workforce, yet significant gaps in workforce capacity persist and are projected to continue if educational attrition is not addressed effectively, especially in low- and middle-income regions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Poor academic outcomes among health professional students have been linked to an array of individual, instructional, and environmental factors, including learning preparedness, instructional quality, and institutional support, all of which have implications for both patient care quality and long-term workforce stability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent global policy and workforce discussions continue to emphasise strengthening nursing and midwifery education and protecting the training pipeline as part of wider workforce strategies, making the problem of academic underperformance in training institutions a practical workforce issue rather than only an academic one [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGhana's nursing education vulnerability predictors have started getting empirical attention. Post-enrollment factors like academic level, student age, language-related issues, and institutional support have been highlighted as significant correlates of referral risk in empirical and theoretical literature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings, while supporting the understanding of the in-program (proximal) mechanisms affecting academic outcomes, do not clarify the point of referral risk being, at least partly, \"imported\" into nursing education through pre-tertiary educational paths or uneven preparedness at entry. To put it differently, it is still unclear whether and how disparities in basic preparation acquired in high school contribute to the vulnerability of some students once they are exposed to the nursing courses that demand much in terms of cognitive and language skills.\u003c/p\u003e \u003cp\u003eIt is becoming increasingly apparent that there is a body of work which suggests that the pre-tertiary academic background, especially a Senior High School (SHS) grounding and performance in the key subjects like English, Maths, and Integrated Science, can have a major effect on the students\u0026rsquo; ability to deal with the demands of the professional nursing curricula [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In areas like Ghana, many nursing and midwifery students are from non-science SHS programs like General Arts and Home Economics, which makes the shift into science-intensive courses such as anatomy, physiology, and pharmacology very rough for them. Nevertheless, no study so far, even that of [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], has investigated how early academic routes, competence in core subjects and self-confidence in academics might combine to jointly predict academic referrals before the manifestation of difficulties at the tertiary level.\u003c/p\u003e \u003cp\u003eStudents may respond to academic demands differently depending on the level of their self-belief and cognitive preparedness. According to [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], self-efficacy is the factor that plays the most significant role in academic persistence, resilience, and performance. Science self-efficacy is considered a crucial prerequisite in the medical field, as it not only corresponds with better grades in bioscience courses but also prepares students to work hard for the most challenging academic content when necessary [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Besides, the self-regulated learning model of [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] places great emphasis on the use of study strategies, metacognitive planning and effort management, while [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] asserts that academic language proficiency is a major factor in the capacity of a student to understand theoretical instruction and succeed in assessments. The studies also suggest that time management and grit are strong forces that often knit academic success, and they are also significant even when two students next to each other are assigned different grade rates due to differences in their learning and comprehension abilities from earlier school levels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, very few studies have investigated the relationship of these factors with pre-tertiary academic background. Hence, academic preparedness in nursing education remains poorly studied theoretically.\u003c/p\u003e \u003cp\u003eEven though it has been appreciated in previous research that stress, institutional support, and language barriers play a vital role in the academic performance of students, researchers, including [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], have paid attention to post-enrolment institutional and psychosocial factors. The influence of the academic readiness that was built in Senior High School (SHS) has been given little attention up to now. A large proportion of the nursing cohort, which consists mainly of students not coming from science, is not considered in the performance model. In addition, most of the investigations utilise a single-predictor method that cannot facilitate the understanding of the interaction among cognitive, behavioural, and academic variables to predict the referral risk. The study on whether academic referrals are the consequence of institutional shortcomings only or also related to the educational inequalities existing at the point of entry is still open.\u003c/p\u003e \u003cp\u003eThis study builds upon and extends earlier scholarship by reframing academic referral not only as a post-enrolment phenomenon, as shown by [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], but as a potential outcome of academic preparedness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] established before entry into nursing education. While prior Ghanaian research [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] examined prior education and cumulative GPA as predictors of licensure examination success, those studies primarily focused on background characteristics and did not integrate psychological preparedness constructs or institutional support variables within a theoretically grounded framework. The present study extends this literature by combining objective pre-tertiary indicators with validated measures of science self-efficacy, academic language proficiency, study strategies, grit, and perceived institutional support. This multidimensional model provides a more comprehensive examination of academic performance determinants in nursing education contexts. By integrating Bandura\u0026rsquo;s self-efficacy theory, Pintrich\u0026rsquo;s self-regulated learning constructs and Tinto\u0026rsquo;s theory of academic integration, the present research proposes a multidimensional academic readiness framework. This theoretical contribution shifts attention from reactive institutional interventions to proactive assessment of academic risk, offering implications for admission policy, bridging programmes and evidence-based student support.\u003c/p\u003e\n\u003ch3\u003eAim of the study\u003c/h3\u003e\n\u003cp\u003eThe aim of this study was to examine the association between pre-tertiary academic preparation (WASSCE grades and aggregate score), psychological preparedness (science self-efficacy, academic language proficiency, study strategies, and grit/time management), institutional support, and academic outcomes (GPA category and referral status) among nursing and midwifery students in Ghana.\u003c/p\u003e "},{"header":"Literature Review","content":"\u003cp\u003eNursing and midwifery students' academic records and advancements play significant roles in determining the preparedness of the workforce and the quality of patient care. At the same time, academic referrals -- the cases where students cannot complete their coursework according to the prescribed schedule and thus are advised to either re-take courses or attend remediation classes, are still the most serious problems faced by the nursing educational systems globally [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Such referrals extend programme duration, increase financial strain, and heighten the risk of attrition, thereby undermining national efforts to strengthen the nursing workforce [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the significance of academic failure in health professions education is well acknowledged, scholarly discourse has long prioritised factors encountered \u003cem\u003eafter\u003c/em\u003e entry into nursing programmes, to the relative neglect of the academic conditions under which students begin their training.\u003c/p\u003e\u003cp\u003eAcademic stress, clinical workload, the experience of examinations, and institution-based support systems have been profoundly studied as the main causes of academic difficulties in studies of more recent years. A study by [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] found acute emotional exhaustion and clinical anxiety among the South African nursing students as major mediating factors to their poor academic performance. One other report by [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] further mentioned that among the factors that contribute to academically withdrawing students, the foremost one was the poor provision of learning in the student environment, which could be feedback, counselling or guidance. The studies on the subject have given great emphasis to the significance of understanding the assessment and the language, as well as the teaching of courses. Unclearly worded exam questions and the absence of frequent feedback are among the causes of academic failure because they contribute to cognitive overload [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this way, they have contributed to a deep understanding of the variables within a program that correlates with academic outcomes. The very initial state when all students have fairly the same academic background is what was not directly addressed but rather assumed by the studies. [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] presented evidence for the connection of academic referral to the institutional and psychosocial factors such as academic standard, age, assessment clarity and perceived support, in the Ghanaian context. Still, the study paid its attention only to the in-programme determinants and did not investigate the possibility of higher academic referrals due to a pre-tertiary academic gap.\u003c/p\u003e\u003cp\u003eNew findings now show that the gap in the educational background of students from pre-tertiary educational institutions, especially Senior High School (SHS) curricula, is likely to be one of the reasons responsible for the academic suffering of certain students without giving any one pattern of life. In the countries where the health systems are of the top quality, such as the high-income countries, medicine admissions are, to a much greater extent, based on science than any other criterion, whereas the nursing students who are being trained in the nursing schools of Ghana are practically all beginners who never had any touch of science that is mandatory for the student's course of curriculum [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] proposed that the requisite preparation at the time of entering, rather than the enthusiasm acquired after entry and through the course, is what will be related to a student's attainment in the foundational nursing subjects like anatomy and physiology. Regarding the link of SHS-preparation to the students' academic progress, the focus has not squarely been on the predictive role of WASSCE results, especially in basic subjects like English, Mathematics and Integrated Science, which are fundamental to the reading, arithmetic and logic skills, respectively, in college studies [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to the previous academic background, psychological and behavioural aspects such as self-efficacy, study skills, and language skills are strongly associated with academic success. The study by [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] showed that self-efficacy, defined as a sense of one's ability to perform necessary behaviours for a particular performance, is a trait that is rapidly related to professionals’ learning in health education (Usher \u0026amp; Pajares, 2008). Also, the learning model based on self-regulated learning and the study skills that are included in it, such as spaced repetition, retrieval practice, and metacognitive planning, have shown a strikingly high correlation with exam performance, according to the research of [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], and Credé \u0026amp; Kuncel, 2008. Apart from this, achieving academic proficiency in the English language has been quite a task, especially in multilingual settings, whereby [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] has differentiated between social-performance language fluency and thinking skills, academic language and has presented the latter as a precondition for grasping the tests as well as the teaching of theories. Time management and grit are among the latest additions to this line of argumentation, with [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] maintaining that persistence is frequently more important than raw intelligence in terms of forecasting long-run achievements.\u003c/p\u003e\u003cp\u003eNevertheless, the interplay of these psychological constructs along with the formal academic background is not very clear, as only a very few studies were carried out along similar lines. As a result, the understanding of academic readiness and its relation to the referred issues remains fragmentary. Moreover, most of the earlier research has tended to focus solely on academic history or behavioural competence and thus the idea of combining the two within a predictive framework has been completely missed. Besides, the institutional support has been regarded in the same vein, that it was an independent solution to the problem rather than a moderator in and of the broader ecosystem of educational determinants [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Consequently, the synthesis of current studies does not clearly show whether academic referrals were due to shortcomings encountered during training or were the result of more profound, systemic, and unequal academic advantages, and these were all ready-at-entry issues.\u003c/p\u003e\u003cp\u003eAlthough the recent literature recognises the necessity of diagnosing early to identify students who are at risk of failing ahead of time, the methodology's constraints still continue. The work done, on the one hand, relies on single-predictor models and, on the other hand, on self-reported stress or anecdotal evidence, with no use of multivariate models that would disentangle the effects of contextual, behavioural, and institutional factors. Moreover, non-science students, who are the majority in the context of Ghanaian nursing education, have been generally excluded from analyses based on the assumption of prior scientific skills. This not only limits the theoretical aspects of research in addressing how different academic pathways come together to create nursing performance but also the gaps, thus created, in both policy formulation at institutions and theoretical formulation in the field in general.[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] also point out that if there is no set model for academic readiness at the time of program entrance, the interventions that will be carried out might be more reactive rather than being preventive. The work of[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] contributed significantly to institutional and psychosocial perspectives but did not interrogate how academic referrals may stem from cognitive and academic foundations established before entry into nursing programmes.\u003c/p\u003e\u003cp\u003eThe present study introduces a new predictive academic readiness model that combines pre-tertiary academic markers with psychological readiness components to account for academic referrals and performance. The authors use Bandura’s concept of self-efficacy, Pintrich’s self-regulated learning and Tinto’s academic integration theory to provide an integrated perception of academic vulnerability. The major theoretical advantage of this shift lies not only in the academic field it changes but also in its real-life applications: being able to foresee the referral factors early would possibly correspond with taking the necessary steps in that direction, such as forcing students to attend bridging programmes, preparing them for the academic level, and giving them emotional help in addition to other strategies.\u003c/p\u003e\n\u003ch3\u003eConceptual and Theoretical Foundation\u003c/h3\u003e\n\u003cp\u003eThe academic performance of nursing and midwifery students is shaped by the interaction of prior academic preparation, internal belief systems, self-regulated learning behaviours and institutional environments. To provide a coherent basis for analysing these interrelationships, the present study is grounded in a multidimensional theoretical foundation that draws upon Bandura\u0026rsquo;s self-efficacy theory, Pintrich\u0026rsquo;s model of self-regulated learning and Tinto\u0026rsquo;s theory of academic integration. This integrated framework supports the prediction that academic referrals are not exclusively a result of institutional shortcomings but may reflect deeper disparities in academic readiness at the point of entry into professional training.\u003c/p\u003e \u003cp\u003eThe theory of self-efficacy as proposed by [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] forms the basic ground on which we can explain the association of students' beliefs in their academic abilities with their persistence, resilience, and performance in academics (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It has been proven by research that high self-efficacy is correlated with an increase of motivation, the improvement of the ability to cope with difficulties, and the readiness to deal with difficult academic tasks (Usher \u0026amp; Pajares, 2008). Within nursing education, science self-efficacy is particularly relevant, as many students, especially those from non-science SHS backgrounds, enter programmes with perceived deficiencies in scientific literacy. Students with low science self-efficacy may disengage or adopt avoidance strategies when confronted with demanding courses such as anatomy, physiology and pharmacology, thereby increasing their risk of academic referral.\u003c/p\u003e \u003cp\u003eIn addition to this internal belief system, the model of self-regulated learning (SRL) developed by [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] illustrates how students can plan, monitor, and proactively modify their learning strategies for the sake of their academic success (see\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among others, SRL includes metacognitive control, study planning, time management, and effort regulation. It has been found that the proper application of self-regulated learning strategies is one of the major ground predictors of academic performance across all subjects, irrespective of the student's prior academic ability [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The present study views constructs like study strategies, language proficiency in academia, and grit as behavioural manifestations of SRL and thus reflects students\u0026rsquo; capability to meet the demands of the higher education environment.\u003c/p\u003e \u003cp\u003eWhile self-efficacy and SRL focus on the initiative of the person, academic achievement also depends on the educational atmosphere. According to [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] theory of academic integration, students\u0026rsquo; staying at a college is the function of being both academically engaged and institutionally supported (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Effective academic support processes, like counselling, feedback and structured remediation, can dampen the effects of academic difficulty. However, Tinto believes that students with low academic readiness may still be and always be very much at peril with the best of these environmental supports. Thus, the study involves institutional support not as an independent forecaster but as the controlling variable related to whether pre-tertiary educational factors still hold after the interactive and continuing support programs.\u003c/p\u003e \u003cp\u003eThese theories taken together provide backing for a conceptual model in which academic referrals are seen not only as institutional pedagogy failures but also as of cumulative educational experiences starting from the pre-tertiary level. By integrating prior academic background (SHS programme and core subject performance) with self-efficacy beliefs and self-regulated learning behaviours, this study extends existing theoretical discourse beyond post-enrolment determinants. The present model therefore contributes to academic theory by proposing that readiness for nursing education is multidimensional, rooted in cognitive preparedness, psychological confidence and behavioural competence, and that academic risk may be more effectively identified through anticipatory rather than reactive evaluation.\u003c/p\u003e\n\u003ch3\u003eStudy hypotheses\u003c/h3\u003e\n\u003cp\u003eGuided by Tinto\u0026rsquo;s student integration model and Bandura\u0026rsquo;s self-efficacy framework, we hypothesised that:\u003c/p\u003e \u003cp\u003e1. Stronger pre-tertiary academic performance would be associated with higher GPA and lower referral risk.\u003c/p\u003e \u003cp\u003e2. Higher science self-efficacy and effective study strategies would be independently associated with better academic outcomes.\u003c/p\u003e \u003cp\u003e3. Institutional support would be positively associated with academic performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\n\u003cp\u003eA cross-sectional analytical design was chosen to examine the connection between the pre-tertiary academic background and the learning preparedness, the academic referrals, as well as the performance of nursing and midwifery students. The research took place at one of the public Nursing and Midwifery Training Colleges in Ghana that is accredited by the Nursing and Midwifery Council (N\u0026amp;MC). The college was purposively selected since its admissions comprise students from various Senior High School (SHS) programmes, mostly General Arts and Home Economics, with a minority of Science students. The college, therefore, provides a realistic representation of the Ghanaian nursing and midwifery education system.\u003c/p\u003e\n\u003ch2\u003eStudy Population and Eligibility Criteria\u003c/h2\u003e\n\u003cp\u003eThe target population comprised students in the second and third years of the Registered General Nursing (RGN) and Registered Midwifery (RM) programs for the 2025/2026 academic year. We excluded first-year students because they had not yet completed enough courses to receive academic referrals or performance outcomes. Nurse Assistant Clinical (NAC) students were excluded because their programme structure, assessment requirements, and academic progression pathways differ substantially from diploma-level nursing and midwifery students, making direct comparison of referral patterns methodologically inappropriate. To be eligible to participate in the study, students had to be actively enrolled in the college, be available during the data collection period, and provide voluntary, informed consent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSample Size Determination and Sampling Procedure\u003c/h2\u003e\n\u003cp\u003eThe sample size was determined based on the formula of [25] for cross-sectional studies, which was calculated on the assumption of 50% prevalence of academic referrals, a 95% confidence level, and a 5% margin of error. This produced a minimum of 385 participants; adding 20% for non-response yielded a target of 461. Stratified random sampling ensured proportional representation by programme (RN/RM) and academic level (Year 2/Year 3). Within each stratum, simple random sampling was used.\u003c/p\u003e\n\u003cp\u003eWe used the stratified random sampling technique to ensure that the distribution was representative of both the programs of study (Registered General Nursing and Registered Midwifery) and the academic levels (Year 2 and Year 3). This ensured that everybody had an equal chance of being selected (see Table 1). A group of 402 people, which was 87.2% of the total, participated in the study. After removing errors or cases with significant missing information, the researchers were left with 345 responses for the analysis.\u003c/p\u003e\n\u003cp\u003eTable 1: Sampling Frame and Sample Distribution\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProgram and Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eClass Code\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePopulation Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSample Size Calculated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSample Distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiploma in Nursing, Year 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDN10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e171 students were randomly selected from a population of 522.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiploma in Nursing, Year 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDN11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129 students were randomly selected from a population of 387.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegistered Midwifery, Year 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71 students were randomly selected from a population of 212.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegistered Midwifery, Year 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRM14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 students were randomly selected from a population of 207.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e440 students\u0026nbsp;were selected via simple random sampling from the total population of 1,328.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Collection Instrument and Procedures\u003c/h2\u003e\n\u003cp\u003eData collection was performed using a structured, self-administered electronic questionnaire that was developed on the KoboToolbox platform. The researchers created this questionnaire after a thorough review of studies on academic performance, retention, and self-efficacy of nursing students (see Supplementary File 1_Pre-tertiary and personal preparedness). The questionnaire comprised seven sections:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eDemographic Characteristics\u0026nbsp;included age, gender, level, and programme.\u003c/li\u003e\n \u003cli\u003eThe Pre-Tertiary Background section involved\u0026nbsp;SHS programme, WASSCE aggregate, and grades in\u0026nbsp;Core English, Core Mathematics, and Integrated Science. These core subjects are foundational for tertiary education.\u003c/li\u003e\n \u003cli\u003eLearning Preparedness: \u003cem\u003eScience Self-Efficacy Scale (SSE-8),\u0026nbsp;\u003c/em\u003ewhich was adapted from Bandura\u0026rsquo;s [10] concept of self-efficacy and scales that have been used in the past for testing the confidence in learning anatomy, physiology, and pharmacology.\u003cem\u003e\u0026nbsp;Study Strategies Scale (SS-10),\u0026nbsp;\u003c/em\u003ewhich was constructed upon the self-regulated learning model of [12].\u003cem\u003e\u0026nbsp;Academic Language Proficiency (ALP-6)\u003c/em\u003e, adapted from Cummins\u0026rsquo; [13] model of cognitive academic language proficiency.\u003cem\u003e\u0026nbsp;Grit/Time-Management Scale (GTM-6)\u003c/em\u003e, adapted from [15].\u003c/li\u003e\n \u003cli\u003eCurrent Life Constraints:\u0026nbsp;workload, caregiving duties, sleep hours, and food security.\u003c/li\u003e\n \u003cli\u003eCourse-Domain Difficulty:\u0026nbsp;perceived difficulty of science, professional, and general courses.\u003c/li\u003e\n \u003cli\u003eAcademic Outcomes include referral history and the previous semester\u0026rsquo;s GPA.\u003c/li\u003e\n \u003cli\u003eThe \u003cem\u003eInstitutional Support Scale (ISS-6)\u0026nbsp;\u003c/em\u003eincludes items that aim at measuring the perceived availability and adequacy of resources that foster or guide students\u0026rsquo; affinity to the college, including academic guidance, feedback, counselling, and peer support [24].\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe instrument was piloted with a sample of 30 students from a similar institution to evaluate the clarity and internal consistency. Cronbach\u0026rsquo;s alpha values ranged from .78 to .86, indicating acceptable coefficients.\u003c/p\u003e\n\u003ch2\u003eVariables and Operational Definitions\u003c/h2\u003e\n\u003cp\u003eIn this study, variables were organised in alignment with the study\u0026rsquo;s conceptual framework, distinguishing between distal academic background factors, individual learning preparedness constructs, contextual constraints, and academic outcomes. Rather than restating instrument components, variables were operationalised to analyse how pre-tertiary academic foundations, represented by SHS programme and core subject grades, interact with cognitive-behavioural characteristics such as science self-efficacy, study strategies, academic language proficiency and grit to be associated with referral risk and performance. Institutional academic support was included as a statistical control to determine whether background and personal preparedness factors retained predictive value independent of college-level interventions. A detailed summary of variable definitions and coding is presented in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2: Operation Definitions of Study Variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable Category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOperational Definition / Coding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOutcome Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Referral: Self-reported history of receiving one or more academic referrals (0 = No referral; 1 = \u0026ge; 1 referral). \u0026nbsp;Academic Performance: Self-reported GPA/CGPA categorised as \u0026lt; 2.50, 2.50\u0026ndash;2.99, 3.00\u0026ndash;3.49, or \u0026ge; 3.50.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-Tertiary Academic Background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Programme: General Arts, Home Economics, Business, Science, Technical, or Vocational. Core Subject Index: WASSCE grades in English, Core Mathematics, and Integrated Science (A1\u0026ndash;F9 coded numerically as 1\u0026ndash;9 and averaged). \u0026nbsp;WASSCE Aggregate: Total aggregate score (6\u0026ndash;54).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLearning Preparedness Constructs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eScience Self-Efficacy (SSE-8): Mean score (1\u0026ndash;5) reflecting confidence in handling science-based nursing coursework. \u0026nbsp;Academic Language Proficiency (ALP-6): Mean score (1\u0026ndash;5) assessing clarity in understanding academic language and assessment instructions. \u0026nbsp;Study Strategies (SS-10): Mean score (1\u0026ndash;5) evaluating use of metacognitive and self-regulated learning techniques. \u0026nbsp;Grit / Time Management (GTM-6): Mean score (1\u0026ndash;5) measuring persistence and academic responsibility.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent Life Constraints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorkload: Weekly hours of paid employment (numeric). \u0026nbsp;Caregiving Responsibility: Weekly hours spent on caregiving tasks (numeric). \u0026nbsp;Sleep Duration: Average nightly hours of sleep (numeric). Food Security: Frequency of worrying about or reducing meals (Never, Sometimes, Often).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInstitutional Academic Support (ISS-6):\u0026nbsp;Mean score (1\u0026ndash;5) covering access to academic guidance, feedback, counselling, and peer study opportunities.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSociodemographic Covariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years), Gender (Male/Female), Programme (RN/RM), Academic Level (Year 2/Year 3).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Authors\u0026rsquo; Construct, 2025\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese variables were then progressively entered into the regression models to check their individual and joint linked to academic referral status and GPA outcomes. The institutional academic assistance was used as a control, considering the stability of pre-tertiary and readiness factors of an individual.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eBias and Quality Control\u003c/h2\u003e\n\u003cp\u003eTo reduce selection bias, simple random sampling was used within each stratum of the population. Social desirability bias was minimised through anonymity and neutral item wording. Logical consistency checks and range validations were applied during data cleaning. Missing data were \u0026lt; 2% and managed using pairwise deletion.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eData were analysed using IBM SPSS Statistics Version 25 (IBM Corp., 2020). Descriptive statistics (frequencies, percentages, means, and standard deviations) summarised participant characteristics. Bivariate analyses employed Pearson\u0026rsquo;s chi-square tests for categorical variables and independent-samples \u003cem\u003et\u003c/em\u003e-tests or Mann\u0026ndash;Whitney U tests for continuous variables where normality was violated.\u003c/p\u003e\n\u003cp\u003eThe dependent variable in the multivariable logistic regression was academic referral, and the dependent variable in the multiple linear regression was GPA/CGPA. The predictor variables were SHS programme, Core Subject Index, WASSCE aggregate, and learning-preparedness scales, and we factored in institutional support and demographic factors. The second step was to check for the presence of multicollinearity based on the VIF - variance inflation factor - being less than 5 with -1 as the lower bound. Furthermore, the level of statistical significance was determined by the p-value being lower than 0.05.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee for Research Involving Humans (ECRIH) of the Tepa Nursing and Midwifery Training College, Ghana (Ref: ECRIH/AP/010/25). Before the study, all individuals who were to participate were thoroughly informed about the aims and objectives of the research, its processes, drawbacks, and benefits. All participants provided their written informed consent. Participation was fully voluntary, anonymity was guaranteed, and subjects were informed of their right to withdraw from the study at any time without any negative link to their academic or personal status.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eReliability of study scales\u003c/h2\u003e\n\u003cp\u003eThe internal consistency reliability of all multi-item scales was checked via Cronbach’s alpha coefficients before scale construction and inferential analyses (see Table 3). The Science Self-Efficacy Scale (8 items) showed an acceptable reliability (α = 0.768), while the Study Strategies Scale (10 items) consistently scored better (α = 0.768). The Institutional Support Scale (6 items) displayed good reliability (α = 0.742) while the Grit and Time Management Scale (6 items) showed acceptable internal consistency (α = 0.681). The Academic Language Proficiency Scale (6 items) provided a reliability coefficient of moderate but acceptable capacity (α = 0.603), which is considered sufficient for exploratory educational research carried out with context-specific and multidimensional constructs. All the reliability coefficients were above the threshold levels for being considered psychometrically sound, which allowed us to combine the items into composite indices for further analysis.\u003c/p\u003e\n\u003cp\u003eTable 3: Internal Consistency Reliability of Study Scales\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of Items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCronbach’s α\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScience Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Language Proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStudy Strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrit \u0026amp; Time Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInstitutional Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003ch2\u003eParticipant Characteristics and Response Rate\u003c/h2\u003e\n\u003cp\u003eThe number of students who were invited to take part in the study was 461 in total. 402 questionnaires were returned from these students, which is an initial response rate of 87.2%. After the data had been cleaned and the cases with significant missing data were excluded using the listwise method, 345 respondents were kept for analysis, giving an effective analytical response rate of 74.8%.\u003c/p\u003e\n\u003ch3\u003eSociodemographic and Programme Characteristics\u003c/h3\u003e\n\u003cp\u003eAs shown in\u0026nbsp;Table\u0026nbsp;4, the majority of respondents were\u0026nbsp;female (82.6%), reflecting the gender composition typical of nursing and midwifery programmes. Most students were enrolled in the\u0026nbsp;Registered General Nursing programme (75.1%), with approximately one-quarter pursuing\u0026nbsp;Registered Midwifery (24.9%). With respect to academic level,\u0026nbsp;59.7%\u0026nbsp;of participants were in\u0026nbsp;Year 3, while\u0026nbsp;40.3%\u0026nbsp;were in\u0026nbsp;Year 2, indicating greater representation of students in advanced stages of training.\u003c/p\u003e\n\u003cp\u003eTable 4: Sociodemographic and Programme Characteristics of Respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProgramme of Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRegistered General Nursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRegistered Midwifery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYear 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYear 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003ch3\u003eAdmission and Pre-Tertiary (SHS) Background Characteristics\u003c/h3\u003e\n\u003cp\u003eAdmission-related and pre-tertiary characteristics are presented in Table 5. A significant majority of the students were admitted to the programme on the basis of merit directly (75.1%), though a considerable percentage indicated that they were admitted due to institutional (13.0%) or external influence (10.7%). Slightly over half of respondents indicated that they did not pay money for admission (52.2%), while 40.6% reported having paid. Regarding pre-tertiary background, most students completed SHS in public schools (83.5%), with General Arts (61.7%) and Home Economics (25.5%) being the predominant SHS programmes. Only 12.2% of respondents had a General Science background. A large majority were boarders during SHS (80.9%), and most reported receiving instruction in English supplemented with local language explanations (80.3%), rather than English only.\u003c/p\u003e\n\u003cp\u003eTable 5: Admission Pathway and Pre-Tertiary Educational Background of Respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdmission Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDirect (merit-based)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInfluence (inside institution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInfluence (outside institution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePaid for Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrefer not to say\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Programme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral Arts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHome Economics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAgriculture Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Boarding Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBoarding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLanguage of Instruction (SHS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEnglish only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEnglish + local language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2024\u003c/p\u003e\n\u003ch2\u003eDescriptive Statistics of Composite Study Scales\u003c/h2\u003e\n\u003cp\u003eTable 6 displays the descriptive statistics for the composite scales that measure students’ academic preparedness, learning behaviours, and perceived institutional support. All composite variables were elaborated as mean scores, where the higher values expressed stronger support of the respective construct. In general, the participants indicated the science self-efficacy (M = 3.55, SD = 0.55) at moderate to high levels, justifying a fair amount of confidence in the comprehension and application of the science-related concepts that are crucial to nursing and midwifery training. Academic language proficiency scores were slightly lower yet still moderate (M = 3.38, SD = 0.54), which signifies that there was some inconsistency among the students as to the level of their perceived ability to grasp the examination questions, assignment instructions, and classroom discussions.\u003c/p\u003e\n\u003cp\u003eTable 6: Descriptive Statistics of Composite Study Scales\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScience Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Language Proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStudy Strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrit \u0026amp; Time Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInstitutional Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e \u003cem\u003eComposite scores represent mean values of scale items. Higher scores indicate higher levels of the respective construct.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eBivariate Associations with Academic Referral Status\u003c/h2\u003e\n\u003cp\u003eSeveral bivariate methods were used to show relationships between academic referral status with pre-tertiary, demographic, academic, and institutional components. Chi-square tests of independence were used for categorical variables, while independent-samples t-tests were applied to continuous variables. Effect sizes were reported using Cramer’s V and Cohen’s d, where appropriate.\u003c/p\u003e\n\u003ch3\u003eCategorical Variables and Academic Referral\u003c/h3\u003e\n\u003cp\u003eAs presented in Table 7, no statistically significant associations were observed between academic referral status and SHS programme (χ²(3) = 6.91, p = .075), SHS category (public/private) (χ²(2) = 0.65, p = .721), boarding status during SHS (χ²(2) = 1.72, p = .422), or main language of instruction at SHS (χ²(2) = 1.15, p = .562). Similarly, students’ perception of the most difficult course domain in nursing school (science, professional, or general courses) was not significantly associated with referral status (χ²(3) = 0.25, p = .969).\u003c/p\u003e\n\u003cp\u003eTable 7: Bivariate Associations Between Categorical Variables and Academic Referral Status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredictor Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eχ² (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect Size (Cramer’s V)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Programme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.91 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Category (Public/Private)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.65 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Boarding Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Language of Instruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.15 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCourse Domain Found Most Difficult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.89 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdmission Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.41 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePaid for Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003cp\u003eAlthough the differences in referral status between genders were close to being significant, they still did not reach the level of significance (χ²(1) = 2.89, p = . 089), with more female students receiving referrals than male students. The admission-related factors, such as the pathway of admission (χ²(3) = 4.41, p = . 220) and the method of payment for admission (χ²(2) = 0.18, p = . 913), were also not bivariately associated with referral status at a significant level. Across all categorical analyses, effect sizes were small (Cramer’s V range = 0.02–0.14), indicating weak associations.\u003c/p\u003e\n\u003ch3\u003eContinuous Variables and Academic Referral\u003c/h3\u003e\n\u003cp\u003eThe conduct of independent-samples t-tests was done to make a comparison between referred and non-referred students on continuous measures of pre-tertiary academic preparation, learning-related psychosocial factors, and current life conditions. Levene’s tests indicated that the assumption of homogeneity of variances was met for most variables, with Welch’s correction applied where appropriate. Results are summarised in Table 8.\u003c/p\u003e\n\u003cp\u003eTable 8: Comparison of Continuous Variables by Academic Referral Status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo Referral (Mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReferral (Mean ± SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScience Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.60 ± 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.53 ± 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Language Proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.39 ± 0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.38 ± 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22 (343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStudy Strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.61 ± 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.64 ± 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−0.57 (343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrit \u0026amp; Time Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.76 ± 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.70 ± 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98 (343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInstitutional Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.64 ± 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.70 ± 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−0.92 (343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWASSCE Aggregate Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.54 ± 4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.48 ± 4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−1.73 (337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommute Time (minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.86 ± 6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.04 ± 8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−2.27 (299)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage Sleep (hours)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.31 ± 1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.67 ± 1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−2.06 (314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;No statistically significant differences were observed between referred and non-referred students in science self-efficacy, academic language proficiency, study strategies, grit and time management, or perceived institutional support (p \u0026gt; .05). In the same manner, the WASSCE aggregate score showed no major difference between the two groups, nevertheless, a tendency for higher aggregate scores among referred students was seen (p = . 086).\u003c/p\u003e\n\u003cp\u003eIn contrast, significant group differences emerged for commute time and average sleep duration. Students who had experienced academic referrals reported significantly longer commute times to lecture rooms compared to their non-referred counterparts (M = 11.04 vs. 8.86 minutes; t(299) = −2.27, p = .024). Additionally, referred students reported slightly longer average sleep duration per night than non-referred students (M = 6.67 vs. 6.31 hours; t(314) = −2.06, p = .040), although the magnitude of this difference was small.\u003c/p\u003e\n\u003ch3\u003eAssociation Between WASSCE Core Subject Grades and Acaemic Referral Status\u003c/h3\u003e\n\u003cp\u003eThe association between students’ performance in WASSCE core subjects and academic referral status was examined using chi-square tests of independence. Core subjects assessed included Core Mathematics, English Language, Integrated Science, and Social Studies. Results are summarised in Table 9.\u003c/p\u003e\n\u003cp\u003eTable 9: Association Between WASSCE Core Subject Grades and Academic Referral Status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWASSCE Core Subject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eχ² (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLinear-by-Linear \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCramer’s V\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCore Mathematics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.64 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEnglish Language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.81 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntegrated Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.03 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.52 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003cp\u003eThere was no statistically significant relationship discovered between the WASSCE Core Mathematics grade and the academic referral status (χ²(5) = 3.64, p = . 602) and this suggested that the performance in Core Mathematics was independent of the referral chances. Likewise, the overall association between the Integrated Science grade and the referral status was not statistically significant (χ²(5) = 7.03, p = . 218). Nevertheless, a significant linear-by-linear association was observed (p = . 037), implying a weak but graded trend in which poorer Integrated Science performance was linked to higher referral rates across ordered grade categories.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;In comparison, an English Language grade and academic referral status were found to be significantly associated statistically (χ²(5) = 19.81, p = . 001), Cramer’s V = 0.24, indicating a small-to-moderate effect size. The result implies that students' English proficiency at the pre-tertiary level was significantly correlated with the academic progress in nursing and midwifery education. Also, Social Studies grade was found to be significantly associated with referral status (χ²(7) = 26.52, p \u0026lt; . 001), Cramer’s V = 0.28, showing a moderate effect size, which means that academic competencies assessed through Social Studies might be very important for academic success.\u003c/p\u003e\n\u003cp\u003eThe results of the Social Studies should, however, be interpreted cautiously as a certain percentage of cells had their expected counts below five, which indicates the presence of few data in certain grade categories. The results indicate that the core subjects related to language and humanities, rather than solely mathematics and integrated sciences, demonstrate stronger bivariate associations with academic referral outcomes.\u003c/p\u003e\n\u003ch2\u003eAssociation Between WASSCE Core Subject Grades and Last-Semester GPA\u003c/h2\u003e\n\u003cp\u003eThe relationship between students’ performance in WASSCE core subjects and\u0026nbsp;last-semester GPA (ordinal categories)was examined using Pearson’s chi-square tests of independence. Core subjects assessed included\u0026nbsp;Core Mathematics, English Language, Integrated Science, and Social Studies. Results are summarised in\u0026nbsp;Table 10.\u003c/p\u003e\n\u003cp\u003eTable 10: Association Between WASSCE Core Subject Grades and Last-Semester GPA\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWASSCE Core Subject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eχ² (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCramer’s V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCells with Expected Count \u0026lt; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCore Mathematics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.90 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEnglish Language\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.47 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntegrated Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.29 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.57 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003cp\u003eOverall, no statistically significant associations were observed between last-semester GPA and grades in Core Mathematics (χ²(20) = 18.90, \u003cem\u003ep\u003c/em\u003e = .528), Integrated Science (χ²(20) = 23.29, \u003cem\u003ep\u003c/em\u003e = .275), or Social Studies (χ²(28) = 32.57, \u003cem\u003ep\u003c/em\u003e = .252). Similarly, the association between English Language grade and GPA did not reach conventional statistical significance (χ²(20) = 29.47, \u003cem\u003ep\u003c/em\u003e = .079), although the result approached the threshold for significance.\u003c/p\u003e\n\u003cp\u003eAcross all subjects, effect sizes were\u0026nbsp;small\u0026nbsp;(Cramer’s V ranging from 0.12 to 0.15), indicating limited practical association between pre-tertiary core subject grades and short-term academic performance as measured by last-semester GPA. Interpretation of these findings should be made with caution, as a substantial proportion of cells, particularly for English Language and Social Studies, had\u0026nbsp;expected counts below five, reflecting sparse data across some grade–GPA combinations. These findings suggest that while WASSCE grades may be relevant for broad academic preparedness, they show\u0026nbsp;weak bivariate associations\u0026nbsp;with contemporaneous GPA outcomes in nursing and midwifery training.\u003c/p\u003e\n\u003ch2\u003eMultivariate Predictors of Academic Referral\u003c/h2\u003e\n\u003cp\u003eA binary logistic regression analysis was conducted to examine whether\u0026nbsp;pre-tertiary background (SHS programme, WASSCE aggregate score)\u0026nbsp;and\u0026nbsp;personal preparedness factors\u0026nbsp;(science self-efficacy, academic language proficiency, study strategies, grit and time management, and institutional support) independently demonstrates a statistical association with academic referral status among nursing and midwifery students. Referral status was coded as 1 = referred and 0 = not referred. Of the 345 respondents,\u0026nbsp;339 cases (98.3%)\u0026nbsp;were included in the analysis following listwise deletion of missing data. The baseline (null) model correctly classified\u0026nbsp;64.0%\u0026nbsp;of cases, reflecting the underlying imbalance between referred and non-referred students.\u003c/p\u003e\n\u003cp\u003eTable 11: Binary Logistic Regression Predicting Academic Referral Status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWald χ²\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio (Exp(B))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Programme (overall)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral Arts vs reference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHome Economics vs reference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28396.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWASSCE Aggregate Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScience self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic language proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStudy strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrit \u0026amp; time management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e−0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInstitutional support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003ch3\u003eModel Fit\u003c/h3\u003e\n\u003cp\u003eThe complete model did not bring about a statistically significant change in the prediction of academic referral status when compared to the null model (Omnibus χ²(8) = 13.39, p = . 099). The variance explained was very small (Cox \u0026amp; Snell R² = . 039; Nagelkerke R² = . 053), showing that the utilised predictors had a very limited role in the variance of referral outcomes. The final model yielded a total classification accuracy of 64.9%, along with very high sensitivity for identification of students who were referred (96.3%) but very low specificity for correct identification of non-referred students (9.0%).\u003c/p\u003e\n\u003ch3\u003eIndividual Predictors\u003c/h3\u003e\n\u003cp\u003eNone of the predictors entered into the model reached conventional statistical significance at \u003cem\u003ep\u003c/em\u003e \u0026lt; .05 (Table 11). SHS programme type was not a significant predictor overall (Wald χ² = 3.31, \u003cem\u003ep\u003c/em\u003e = .191). Compared with students from the reference SHS programme category, neither general arts nor home economics backgrounds significantly altered the odds of being referred. WASSCE aggregate score also showed no independent association with referral status (OR = 1.04, \u003cem\u003ep\u003c/em\u003e = .134).\u003c/p\u003e\n\u003cp\u003eSimilarly, none of the preparedness-related constructs (science self-efficacy,\u0026nbsp;academic language proficiency,\u0026nbsp;study strategies,\u0026nbsp;grit and time management, or\u0026nbsp;institutional support) were independently associated with referral status in the multivariable model (\u003cem\u003ep\u003c/em\u003e values ranging from .111 to .671). Although the direction of effects suggested that higher science self-efficacy and stronger grit/time management were associated with lower odds of referral, these relationships did not attain statistical significance after adjustment for other covariates.\u003c/p\u003e\n\u003ch3\u003eModel Diagnostics and Estimation Considerations\u003c/h3\u003e\n\u003cp\u003eThe complete estimation of the model did not converge entirely, reaching the maximum limit of iterations before a final solution was obtained. Furthermore, one category of SHS programme was subjected to exceedingly large standard errors and odds ratios that imply quasi-complete separation, possibly due to sparse data in some categories. These difficulties may have affected the accuracy of the findings and the power of the statistical analysis, and the stability of the coefficient estimates and thus should be taken into account in the interpretation of the results.\u003c/p\u003e\n\u003ch2\u003ePredictors of Last-Semester Academic Performance: Ordinal Logistic Regression\u003c/h2\u003e\n\u003cp\u003eAn ordinal logistic regression model (proportional odds model with logit link) was estimated to examine whether pre-tertiary academic background, personal preparedness, institutional support, and perceived course difficulty were associated with last-semester GPA, treated as an ordered categorical outcome (\u0026lt;2.50, 2.50–2.99, 3.00–3.49, ≥3.50). The analysis included 320 students with complete data across all variables.\u003c/p\u003e\n\u003cp\u003eTable 12: Ordinal Logistic Regression Predicting Last-Semester GPA\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWald χ²\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSHS Programme (Overall)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistically significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e├── General Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigher odds of higher GPA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e├── Agricultural Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigher odds of higher GPA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e└── General Arts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWASSCE Aggregate Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScience Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Language Proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStudy Strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGrit \u0026amp; Time Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInstitutional Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Field Survey, 2025\u003c/p\u003e\n\u003ch3\u003eModel Fit and Estimation Diagnostics\u003c/h3\u003e\n\u003cp\u003eThe final model (see Table 12) showed a statistically significant improvement in comparison with the intercept-only model (Likelihood Ratio χ²(163) = 374.64, p \u0026lt; . 001), which suggests that the group of predictors together played a role in the variation of GPA categories being explained. Pseudo-R² statistics suggested substantial explanatory power (Cox \u0026amp; Snell R² = .690; Nagelkerke R² = .765; McFadden R² = .505).\u003c/p\u003e\n\u003cp\u003eHowever, model estimation generated important warnings. A large proportion of outcome–predictor combinations (75.0%) had zero frequencies, and the maximum number of iterations was reached without full convergence. Moreover, numerous predictors, especially continuous variables with a large number of different values, led to the generation of very high standard deviations, very wide confidence levels, and superfluous parameters. These issues indicate quasi-complete separation and sparse data, rendering many individual coefficient estimates unstable. Consequently, interpretation focuses on direction and statistical significance of predictors with stable estimates, rather than the magnitude of coefficients.\u003c/p\u003e\n\u003ch3\u003eIndependent Predictors of GPA\u003c/h3\u003e\n\u003cp\u003eAfter adjustment for all covariates, the SHS programme background showed a statistically significant association with last-semester GPA. Compared with the reference category (Home Economics), students from a General Science background had significantly higher odds of belonging to a higher GPA category (Wald χ² = 17.64, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), while those from an Agricultural Science background also demonstrated higher odds of improved GPA (Wald χ² = 6.64, \u003cem\u003ep\u003c/em\u003e = .010). In contrast, a General Arts background was not significantly associated with GPA category (\u003cem\u003ep\u003c/em\u003e = .737).\u003c/p\u003e\n\u003cp\u003eNeither\u0026nbsp;WASSCE aggregate score\u0026nbsp;nor any of the\u0026nbsp;personal preparedness constructs—including science self-efficacy, academic language proficiency, study strategies, grit and time management, and institutional support—retained statistically significant independent associations with GPA in the fully adjusted model (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05 for all). Similarly, the perceived domain of course difficulty (science, professional, or general courses) was not independently associated with GPA category.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study covered the effects of pretertiary academic background, learning preparedness, and institutional support on academic achievement among the students of nursing and midwifery in Ghana. By considering perceived institution and supportive factors beyond post-enrollment factors documented in the literature, especially those that are presented by [1], this study provides a unique and detailed insight into how academic readiness translates into students' performance throughout nursing courses. These results call into question the traditional view that academic performance depends primarily on levels of institutional support or on the motivation of the student, beyond earlier education experience and competencies that were built in before the entry into one's first nursing program [11], [14], [24].\u003c/p\u003e\n\u003cp\u003eA significant observation here was that Senior High School (SHS) academic background plays a key role in the prediction and assessment of academic performance of students, especially in the event that science-based programmes are pursued before admission. Students with a background in General Science and Agricultural Science generally tend to perform better in their academics in comparison with their non-science background counterparts. This is consistent with the previous thought that early introduction to scientific concepts helps to prepare the learner for the detailed curricula requirements of courses like anatomy, physiology, and pharmacology [8], [9], [18]. Students coming in from educational institutions that provided minimal science background may now experience gradual disadvantages throughout their undergraduate nursing education, a trend noted by this study. Unlike the earlier studies that have sought to address post-admission issues, this study brings into the spotlight that students may have been academically weak long before they are admitted to a nursing programme, thereby bringing to the fore the importance of early academic cooperation under second-cycle education systems to ensure easy transition to tertiary education systems [2], [24].\u003c/p\u003e\n\u003cp\u003eOn the other hand, the correlation between WASSCE aggregate scores and academic performance was not statistically significant when other variables were taken into account. As a result, it is thought that the use of just aggregate scores as the sole criterion cannot give a clear picture of how well-prepared a student is for nursing education in terms of cognitive and analytical demands. Instead, what a student has studied in the past, and the subjects that make up the discipline, are more important in determining the student’s academic readiness as compared to the total academic achievement. This finding supports the idea expressed in articles that the use of general performance criteria is usually ineffective in showing the acquisition of specific competencies that are the core of a professional's education [22], [23], [26]. Therefore, the reliance on the aggregate scores without looking into the preparation of the subjects at an in-depth level might result in not being able to see the academic risk in the selection of students.\u003c/p\u003e\n\u003cp\u003eAlthough the variables of science self-efficacy, study strategies, grit, and perceived institutional support were reported to be at moderate to high levels by the students, the study still asserted that these variables did not solely forecast academic performance in the multivariate regression model. This resistance is worth mentioning as the previous research has portrayed self-efficacy and learning strategies as the major reasons for success [10], [11], [14]. An explanation could be that there are occasions when competencies only self-perceived are not converted into performance, even though curricular demands are higher than students' previous exposure to academic work at school. Moreover, the supposition that psychosocial factors are less influential than structural factors during the academic preparation period may indicate that once the academic base is secure, a certain level of confidence and motivation may increase as the protective factors in a more effective way.\u003c/p\u003e\n\u003cp\u003eWhile participants rated institutional support levels as moderately high, they were also not found to be significantly connected with academic performance after adjustment. This outcome confirms Tinto’s (1993) idea that institutional integration on its own might not be the answer to the situation of the pre-existing academic disadvantage. In such situations where students start higher education with a lack of prerequisite and preparatory knowledge, the support systems could only come into play when being reactive more than transformative, that is, they offer help after the student has already gotten into academic difficulties. This notion, thus, underscores the importance of further strengthening institutional action with early detection methods and organised and supported academic transitioning initiatives [2], [18].\u003c/p\u003e\n\u003cp\u003eIt is worth stressing that the research results take one step further the already existing theoretical discussion by amalgamating the self-efficacy theory, self-regulated learning, and academic integration frameworks components into one comprehensive and coherent explanatory model. While earlier studies have tended to isolate either psychological or institutional determinants, this study demonstrates that academic performance is shaped by the interaction between pre-entry academic foundations and adaptive learning behaviours developed during training. This multidimensional perspective contributes to nursing education scholarship by reframing academic referrals as a cumulative process rooted in structural educational inequities rather than individual deficits alone [11], [14], [24].\u003c/p\u003e\n\u003cp\u003eFrom a policy and practice perspective, the results underscore the urgency of revisiting admission criteria and preparatory mechanisms within nursing education. Strengthening pre-admission screening, particularly in science-related competencies, and implementing structured bridging programmes for non-science entrants could mitigate early academic vulnerability. Furthermore, early diagnostic assessments and targeted academic support may help institutions identify at-risk students before academic difficulties escalate into referrals or attrition [2], [18]. The findings provide empirical support for Tinto’s student integration model and Bandura’s self-efficacy theory within a sub-Saharan African nursing education context. Specifically, academic preparedness and perceived capability were statistically associated with academic performance, suggesting that both structural foundations and psychological readiness operate within the academic integration process. By integrating objective pre-tertiary indicators with validated preparedness constructs, this study extends existing literature beyond sociodemographic predictors toward a theoretically grounded multidimensional framework.\u003c/p\u003e\n\u003cp\u003eAlthough this study was conducted in a single nationally accredited nursing and midwifery training college, the institutional structure, admission processes, and curriculum framework are broadly comparable to other public training colleges in Ghana. Nevertheless, the findings should be interpreted cautiously, and multi-institutional studies are required to enhance generalisability.\u003c/p\u003e\n\u003ch1\u003eContribution to Theory and Scholarship\u003c/h1\u003e\n\u003cp\u003eIn three essential ways, this research contributes to the body of literature. First, it empirically differentiates academic preparedness from academic performance, which in the process shows that basic educational structures are more associated with self-perceived competencies after students have started professional training. Second, it broadens the current theories of student success by incorporating pre-tertiary educational stratification into models that are usually ruled by psychosocial and institutional variables. Third, by placing academic performance in the setting of Ghana’s secondary education system, the research gives a contextually grounded viewpoint to the worldwide debates about equity in health professions education.\u003c/p\u003e\n\u003ch1\u003eRecommendations for Future Research\u003c/h1\u003e\n\u003cp\u003eWe suggest that future research should employ longitudinal and multi-institutional methods to obtain more precise data on the long-term link between pre-tertiary educational paths and the academic performance of students. The use of objective academic indicators such as standardised tests and GPAs would make it easier to infer causality and also lessen the reliance on self-reported measures. Besides that, further research should also be concerned with the institutional and pedagogical factors, such as the curriculum design, the quality of instruction, and the learning support structures that can moderate the relationship between the students’ educational background and their performance. Qualitative methods may provide a more profound understanding of the students’ academic lives and the adaptation processes. All these would then be the basis for the formulation of evidence-based policies that would strengthen equity, preparedness, and academic success in nursing and midwifery education sectors.\u003c/p\u003e\n\u003ch1\u003eLimitations\u003c/h1\u003e\n\u003cp\u003eThe limitations emphasised in this study should be carefully considered when analysing the results. Firstly, the research design being cross-sectional reduces the investigation of the causes of academic performance. Secondly, the data used were from only one nursing and midwifery training centre, which makes the study less generalisable to other contexts that tend to have different institutional structures and student demographics. Thirdly, the WASSCE grades were self-reported and may be subject to recall bias. However, as high-stakes national examination results are frequently required for institutional documentation, reporting errors may be limited. Nevertheless, future studies should verify examination records through official transcripts. Fourthly, the choice of categorical indicators of academic performance rather than continuous measures might have led to a less sensitive detection of slight changes in performance. Finally, the size of the data was not enough in some of the categories, and this made some models of the regression analysis unreliable. This might have led to the accuracy of the estimated parameters in the regression analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to examine the association between pre-tertiary academic preparation, psychological preparedness, institutional support, and academic performance among nursing and midwifery students in Ghana. The findings indicate that stronger pre-tertiary academic performance and higher science self-efficacy were independently associated with higher GPA categories. Further, aggregate WASSCE scores and several preparedness constructs demonstrated modest or non-significant associations after adjustment. Institutional support also showed positive but limited statistical relationships with academic outcomes. These results suggest that both academic foundations and perceived capability are relevant correlates of performance within nursing education contexts. Although causal inference cannot be established due to the cross-sectional design, the study contributes theoretically by integrating objective pre-tertiary indicators with psychological preparedness constructs within a unified framework. Further multi-institutional and longitudinal research is required to clarify the directionality and stability of these associations.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCGPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCumulative Grade Point Average\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrade Point Average\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRGN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegistered General Nursing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegistered Midwifery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSenior High School\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWASSCE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWest African Senior School Certificate Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee for Research Involving Humans (ECRIH) of the Tepa Nursing and Midwifery Training College, Ghana (Ref: ECRIH/AP/010/25). Before the study, all individuals who were to participate were thoroughly informed about the aims and objectives of the research, its processes, drawbacks, and benefits. All participants provided their written informed consent. Participation was fully voluntary, anonymity was guaranteed, and subjects were informed of their right to withdraw from the study at any time without any negative link to their academic or personal status.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to privacy and confidentiality considerations. However, reasonable requests for data sharing can be directed to the corresponding author. Materials, such as survey instruments, used in this research can also be made available upon request to facilitate transparency and further scholarly inquiry.\u003c/p\u003e\n\u003ch2\u003eConflict of interest / Competing interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests concerning the work presented in this manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no funding was received for this manuscript's research, authorship, or publication.\u003c/p\u003e\n\u003ch2\u003eAuthors’ Contributions\u003c/h2\u003e\n\u003cp\u003eMBU\u0026nbsp;conceived and designed the study, developed the study instruments, supervised data collection, performed the statistical analyses, and led the drafting of the manuscript.\u0026nbsp;AO\u0026nbsp;and\u0026nbsp;TAAA\u0026nbsp;contributed to the study design, interpretation of findings, and critical revision of the manuscript for intellectual content.\u0026nbsp;DOM,\u0026nbsp;DAD,\u0026nbsp;LK,\u0026nbsp;IOA,\u0026nbsp;FI,\u0026nbsp;DO, PW, and BOA supported data collection, data management, and preliminary analysis, and contributed to manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBin Usman M, et al. Predictors of academic referrals among nursing and midwifery students: quantitative insights from Ghana. BMC Nurs. Aug. 2025;24(1):1093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12912-025-03757-8\u003c/span\u003e\u003cspan address=\"10.1186/s12912-025-03757-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation WH, WHO Fact Sheet. Nursing and midwifery,. Accessed: Dec. 26, 2025. [Online]. 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Oct 23. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21203/rs.3.rs-7575678/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-7575678/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Academic performance, nursing education, academic preparedness, pre-tertiary education","lastPublishedDoi":"10.21203/rs.3.rs-8922990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8922990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcademic performance in nursing and midwifery students is a significant feature determining the quality of training and workforce readiness. Although some studies have emphasised the admission process and institutional factors as the main determinants of academic performance, less has been revealed about the association of pre-tertiary educational background with academic performance after the students\u0026rsquo; enrolment. Few studies in sub-Saharan Africa have integrated objective pre-tertiary academic indicators with psychological preparedness constructs within a theoretically grounded framework.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study assessed the link between senior high school (SHS) background and academic readiness and the academic performance of nursing and midwifery students in Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 345 nursing and midwifery students at a public training institution in Ghana. Data were collected using a structured questionnaire capturing socio-demographic characteristics, SHS background, academic preparedness, institutional support, and academic outcomes. Last-semester grade point average (GPA) was analysed as an ordered categorical outcome using ordinal logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eParticipants reported moderate levels of academic preparedness, with mean scores of 3.55 (SD\u0026thinsp;=\u0026thinsp;0.55) for science self-efficacy, 3.38 (SD\u0026thinsp;=\u0026thinsp;0.54) for language proficiency, 3.63 (SD\u0026thinsp;=\u0026thinsp;0.49) for study strategies, 3.73 (SD\u0026thinsp;=\u0026thinsp;0.54) for grit and time management, and 3.68 (SD\u0026thinsp;=\u0026thinsp;0.58) for institutional support. The ordinal regression model indicated a good general fit to the data (Likelihood Ratio χ\u0026sup2; = 374.64, p \u0026lt; .001). When compared to the other two circumstances, the student\u0026rsquo;s background was found to be a predictor of their academic performance, as the General Science (p \u0026lt; .001) and Agricultural Science (p = .010) students\u0026rsquo; occurrences of having the better GPA categories were higher than their counterparts. On the other hand, the composite WASSCE scores, science self-efficacy self-reported measures, and academic language proficiency, study strategies, grit, time management, and institutional support were not associated with GPA after adjustment independently.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNursing and midwifery students' steps up in education seem to be achieved mostly through different paths of schooling-level possibilities rather than through individually felt readiness. Additionally, the integration of the results of this study with proper coordination between undergraduate and secondary education will significantly enhance the production of efficient learning outcomes and strengthen the nursing profession.\u003c/p\u003e","manuscriptTitle":"Pre-tertiary academic preparation, psychological preparedness, and academic performance among nursing students in Ghana: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 16:31:39","doi":"10.21203/rs.3.rs-8922990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T04:41:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T15:50:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T11:57:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312062419854637391219268258223585786931","date":"2026-03-17T18:09:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196660562967823001084928774687726757084","date":"2026-03-10T03:25:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T11:09:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T20:31:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T04:55:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-24T13:05:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-02-24T12:59:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48ae7086-9f2a-42be-909f-676484ab92c9","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:05:57+00:00","versionOfRecord":{"articleIdentity":"rs-8922990","link":"https://doi.org/10.1186/s12909-026-09281-w","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2026-04-22 15:59:58","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2026-03-08 16:31:39","video":"","vorDoi":"10.1186/s12909-026-09281-w","vorDoiUrl":"https://doi.org/10.1186/s12909-026-09281-w","workflowStages":[]},"version":"v1","identity":"rs-8922990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8922990","identity":"rs-8922990","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}