Predicting Academic Performance Among Female Undergraduate Medical and Health Sciences Students Through Sociodemographic, Socioeconomic, and Nutritional Factors
Sahiba Hakro, Mehrun Nisa Soomro, Fiza Keerio, Sher M Chandio, Fareeha Hassan
TL;DR
This study explores how sociodemographic, socioeconomic, and nutritional factors influence academic performance among female undergraduate medical and health sciences students.
Contribution
The study introduces a predictive model for academic performance among female university students in medical and health sciences, a group previously understudied.
Findings
Academic performance was significantly higher among MBBS and BSPH students.
Fourth and fifth-year students showed better academic performance.
The predictive model achieved 79.3% accuracy in predicting academic performance.
Abstract
Introduction Education plays a crucial role in improving individual well-being and socioeconomic development. Poor academic performance is often associated with adverse socioeconomic and psychosocial outcomes. Numerous studies have highlighted the effects of sociodemographic, socioeconomic, and nutritional factors on academic performance. However, these studies have been limited to school students only, leaving university students understudied. Objective To predict academic performance through sociodemographic, socioeconomic, and nutritional factors among female undergraduate medical and health sciences students. Materials and methods This cross-sectional study was conducted on 400 female undergraduate medical and health sciences students selected using stratified systematic random sampling. Data were collected through a self-administered questionnaire. Nutritional status was…
| Variables | Categories | n (%) |
| Age | ≤19 years | 41 (10.3) |
| 20 years | 65 (16.3) | |
| 21 years | 90 (22.5) | |
| 22 years | 95 (23.8) | |
| ≥23 years | 109 (27.3) | |
| Residence | Rural | 150 (37.5) |
| Urban | 250 (62.5) | |
| Accommodation | Hostler | 96 (24.0) |
| Day scholar | 304 (76.0) | |
| Field of study | MBBS | 202 (50.5) |
| DPT | 54 (13.5) | |
| BSPH | 51 (12.8) | |
| BSN | 48 (12.0) | |
| Pharm-D | 45 (11.3) | |
| Nutritional status | Underweight | 117 (29.3) |
| Normal weight | 200 (50.0) | |
| Overweight | 83 (20.8) | |
| Academic performance | Good | 307 (76.8) |
| Bad | 93 (23.3) |
| Variables | Categories | Good academic performance, n (%) | χ2 (p-value) | OR (95% CI) | p-value |
| Age | ≤19 years | 31 (75.6) | 5.16 (0.271) | Ref | - |
| 20 years | 44 (67.7) | 0.68 (0.27-1.61) | 0.384 | ||
| 21 years | 73 (81.3) | 1.39 (0.56-3.33) | 0.471 | ||
| 22 years | 71 (74.7) | 0.95 (0.39-2.19) | 0.914 | ||
| ≥23 years | 88 (80.7) | 1.35 (0.56-3.13) | 0.491 | ||
| Residence | Rural | 108 (72.0) | 3.04 (0.82) | Ref | - |
| Urban | 199 (79.6) | 1.52 (0.95-2.43) | 0.083 | ||
| Accommodation | Hostler | 71 (74.0) | 0.55 (0.458) | Ref | - |
| Day Scholar | 236 (77.6) | 1.22 (0.71-2.06) | 0.458 | ||
| Field of study | Pharm-D | 25 (55.6) | 41.1 (< 0.05*) | Ref | - |
| BSN | 27 (56.3) | 1.03 (0.45-2.34) | 0.947 | ||
| DPT | 36 (66.7) | 1.6 (0.71-3.7) | 0.259 | ||
| BSPH | 40 (78.4) | 2.91 (1.21-7.27) | <0.05* | ||
| MBBS | 179 (88.6) | 6.23 (3-13.03) | <0.001** | ||
| Year of study | Second year | 74 (63.8) | 23.91 (<0.05*) | Ref | - |
| Third year | 75 (73.5) | 1.58 (0.89-2.84) | 0.124 | ||
| Fourth year | 95 (83.3) | 2.84 (1.54-5.37) | <0.05* | ||
| Fifth year | 63 (92.6) | 7.15 (2.9-21.7) | <0.001** |
| Variables | Categories | Good academic performance, n (%) | χ2 (p-value) | OR (95% CI) | p-value |
| Father’s occupation | Private employee | 34 (69.4) | 2.29 (0.807) | Ref | - |
| Doctor | 22 (75.9) | 1.39 (0.5-4.13) | 0.540 | ||
| Government employee | 89 (76.1) | 1.40 (0.66-2.92) | 0.371 | ||
| Businessman | 67 (79.8) | 1.74 (0.77-3.91) | 0.179 | ||
| Teacher | 44 (80.0) | 1.77 (0.72-4.42) | 0.215 | ||
| Others | 51 (77.3) | 1.5 (0.65-3.49) | 0.342 | ||
| Mother’s occupation | Unemployed | 252 (75.9) | 0.78 (0.376) | Ref | - |
| Employed | 55 (80.9) | 1.34 (0.72-2.68) | 0.377 | ||
| Father’s education | Illiterate | 2 (40.0) | 4.92 (0.178) | Ref | - |
| Primary | 10 (66.7) | 3 (0.38-29.16) | 0.302 | ||
| Secondary & higher secondary | 88 (76.5) | 4.89 (0.77-38.6) | 0.091 | ||
| Graduation | 207 (78.1) | 5.35 (0.87-41.36) | 0.069 | ||
| Mother’s education | Illiterate | 19 (67.9) | 14.21 (0.136) | Ref | - |
| Primary | 40 (67.8) | 1 (0.37-2.58) | 0.995 | ||
| Secondary & higher secondary | 147 (77.8) | 1.66 (0.67-3.85) | 0.251 | ||
| Graduation | 101 (81.5) | 2.08 (0.81-5.11) | 0.116 | ||
| Parental income | Lower class | 35 (64.8) | 23.66 (<0.05) | Ref | - |
| Lower middle class | 130 (76.5) | 1.76 (0.90-3.40) | 0.092 | ||
| Upper middle class | 116 (80.0) | 2.17 (1.08-4.33) | <0.05* | ||
| Upper class | 26 (83.9) | 2.82 (0.99-9.4) | 0.066 |
| Variables | Categories | Good academic performance, n (%) | χ2 (p-value) | OR (95% CI) | p-value |
| Nutritional status | Overweight | 60 (72.3) | 1.27 (0.530) | Ref | - |
| Normal weight | 157 (78.5) | 1.4 (0.77-2.50) | 0.262 | ||
| Underweight | 90 (76.9) | 1.28 (0.67-2.44) | 0.457 | ||
| Diet type | Veg | 69 (62.2) | 18.18 (<0.05*) | Ref | - |
| Non-veg | 12 (75.0) | 1.83 (0.59-6.86) | 0.323 | ||
| Mixed | 226 (82.8) | 2.93 (1.78-4.81) | <0.001** | ||
| Meals per day | One | 9 (56.3) | 9.07 (<0.05*) | Ref | - |
| Two | 92 (70.8) | 1.88 (0.63-5.42) | 0.240 | ||
| Three or more | 206 (81.1) | 3.34 (1.14-9.41) | <0.05* | ||
| Breakfast frequency | Often | 31 (57.4) | 18.85 (<0.05*) | Ref | - |
| Seldom | 93 (72.7) | 1.97 (1.01-3.84) | <0.05* | ||
| Daily | 183 (83.9) | 3.88 (2.02-7.44) | <0.001** | ||
| Lunch frequency | Often | 16 (66.7) | 1.68 (0.431) | Ref | - |
| Seldom | 45 (75.0) | 1.5 (0.51-4.17) | 0.440 | ||
| Daily | 246 (77.8) | 1.76 (0.69-4.17) | 0.214 | ||
| Dinner frequency | Often | 4 (66.7) | 0.39 (0.823) | Ref | - |
| Seldom | 15 (75.0) | 1.5 (0.17-10.5) | 0.688 | ||
| Daily | 288 (77.0) | 1.67 (0.23-8.72) | 0.555 |
| Variables | Categories | AOR (95% CI) | p-value |
| Field of study | Pharm-D | Ref | - |
| BSN | 1.51 (0.6-3.84) | 3.380 | |
| DPT | 1.41 (0.42-4.89) | 0.582 | |
| BSPH | 3.01 (0.8-12.07) | 0.110 | |
| MBBS | 6.08 (1.9-20.9) | <0.05* | |
| Parental income | Lower class | Ref | - |
| Lower middle class | 0.94 (0.42-2.05) | 0.886 | |
| Upper middle class | 0.79 (0.33-1.83) | 0.591 | |
| Upper class | 1.31 (0.38-5.03) | 0.675 | |
| Meals per day | One | Ref | - |
| Two | 1.59 (0.37-6.19) | 0.513 | |
| Three or more | 2.28 (0.63-7.65) | 0.191 | |
| Year of study | Second year | Ref | - |
| Third year | 1.96 (1.03-3.81) | 0.0527 | |
| Fourth year | 3.59 (1.81-7.37) | <0.05* | |
| Fifth year | 7.93 (2.9-26.11) | <0.05** | |
| Diet type | Veg | Ref | - |
| Non-veg | 1.02 (0.24-4.91) | 0.977 | |
| Mixed | 0.86 (0.31-2.18) | 0.759 | |
| Breakfast frequency | Often | Ref | - |
| Seldom | 1.37 (0.59-3.16) | 0.458 | |
| Daily | 2 (0.7-5.47) | 0.184 |
| Metric | Value |
| AICc | 397.3 |
| McFadden’s R2 | 0.17 |
| Hosmer-Lemeshow test | χ2 = 4.07, p = 0.851 |
| ROC AUC | 0.77 |
| Correctly classified | 79.3% |
| Positive predictive value | 80.8% |
| Negative predictive value | 63.9% |
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Taxonomy
TopicsMedical Education and Admissions · Healthcare professionals’ stress and burnout · Health and Well-being Studies
Introduction
Education enhances livelihoods, health, and promotes social stability. At the individual level, it is associated with a better quality of life through enhanced productivity, as educated individuals have greater social and economic opportunities. On a broader scale, it builds talented and well-informed human capital, which is considered a driving force for economic development [1]. Academic performance refers to the extent to which an institution, student, or teacher has achieved their goals [2].
Academically strong students tend to have higher self-confidence and self-esteem, lower anxiety levels and depression, better social orientation, and a lower likelihood of being involved in substance use such as khat and alcohol [1]. Poor academic performance often leads to poverty, unemployment, promiscuity, drug use, illegal activities, homelessness, social isolation, dependency, and inadequate health insurance [3]. Academic performance is a multifaceted outcome affected by various interlinked factors, including psychological traits, study settings and environment, extracurricular participation, communication skills, and even physical health [4].
Sociodemographic characteristics significantly shape the academic outcome of the students by providing a supportive environment [5]. On the other hand, socioeconomic status is a key measure of an individual’s overall prestige in society. It is an extensively researched construct in social sciences and encompasses dimensions like parental occupation, education, income, access to opportunities and resources [6,7]. Research has consistently demonstrated a strong association between socioeconomic status and academic success [7].
Medical and health sciences students frequently experience hectic schedules and high academic demands, which leave little room for prioritizing their own nutrition and health. This contributes to unhealthy eating behaviours such as skipping meals and eating unhealthy snacks, leading to elevated stress levels [8]. Medical students often under-eat or overeat, leading to nutrient deficiencies that can affect their educational performance [9]. Given the well-documented influence of nutritional status on students' cognitive function, focus, and general academic accomplishment, the relationship between academic performance and nutrition has received more attention recently [10].
Most studies on academic performance are concentrated on primary and secondary level students leaving gap for female university students. The objective of this study is to predict academic performance through sociodemographic, socioeconomic, and nutritional factors among female undergraduate medical and health sciences students.
Materials and methods
Study design, duration, and settings
This cross-sectional study was conducted from March 21, 2023, to August 16, 2023, on female undergraduate medical and health sciences students at Peoples University of Medical and Health Sciences for Women (PUMHSW), Shaheed Benazirabad, Sindh, Pakistan.
Sample size, technique and selection
The sample size was calculated using Cochran's formula n=z^2^ x p (1-p)/d^2^ [11], assuming a 70.5% prevalence (p=0.705) for good academic performance among health sciences students, as reported in a previous Ethiopian study, with a z-value of 1.96 corresponding to a 95% confidence level and a 5% margin of error (d=0.05) [12]. Although the minimum required sample size was 321, we recruited 400 participants using stratified systematic random sampling to ensure proportional representation across fields. Consenting female undergraduate medical and health sciences students aged 18 years and above who appeared in their last regular annual or semester exams were included in the study. Nonconsenting students, first-year students, and those who didn’t appear in their last regular or semester examinations were excluded from the study.
Study instrument and validation
The study instrument was a self-administered questionnaire consisting of 22 items divided into four sections (Appendix A). The first sections comprised five items assessing sociodemographic characteristics. The second section comprised five items assessing socioeconomic status. The third section comprised nine items related to nutritional information. The last section comprised three items to examine academic performance. The questionnaire was developed by the authors themselves after careful review of the relevant literature. Its content validity was assessed by six experts. The overall content validity index was 0.93, reflecting excellent validity. A pilot study was conducted on 20 students to assess the face validity, clarity, and administration.
Father's occupation was classified into six categories depending upon the nature of the profession: government employee, private employee, doctor, businessman, teacher, and others. Mother's occupation was classified into two categories: unemployed and employed. Parental education was classified based on years of formal education and stratified into four categories: illiterate (no formal education), primary (one to five years), secondary and higher secondary (six to 12 years), and graduation (≥14 years). Parental income was measured on a monthly basis and was classified into four categories: lower class (<50,000 Pakistani rupee or PKR), lower-middle class (50,000-100,000 PKR), upper-middle class (100,001-200,000 PKR), and upper class (>200,000 PKR).
Nutritional status was assessed with BMI and classified into three categories: underweight (<18.5 kg/m^2^, normal weight (18.5-24.9 kg/m^2^), and overweight (≥25.0 kg/m^2^). The frequency of breakfast, lunch, and dinner was assessed based on weekly consumption and classified into three categories: daily (six to seven times a week), often (three to five times a week), and seldom (zero to two times a week). Academic performance was divided into two outcomes based on mean aggregate marks: good (≥65%) and poor (<65%).
Data collection procedure
Data was collected by the researchers themselves using a finalized self-administered questionnaire. Students were approached in their respective departments, and the questionnaire’s content and the purpose of the study were explained to them. The questionnaire was administered in the supervised classrooms, ensuring independent completion and preventing discussion among participants. Height was measured using a wall-mounted scale with participants standing still, legs straight, arms at their sides, line of sight parallel to the floor, and heels touching the flat surface. Weight was measured with a calibrated digital weight machine. Participants were instructed to take off their shoes and place aside any other accessories (e.g., cell phone, wallet, purse). Data of the last exams were obtained from the university’s examination branch, and all the personal identifiers were removed before data analysis.
Statistical analysis
Data were entered and analyzed using GraphPad Prism, version 9.5 (GraphPad Software, San Diego, CA, USA). Chi-square (χ2) test was applied to assess the association between academic performance and sociodemographic, socioeconomic, and nutritional factors. Binary logistic regression was performed to examine predictors of academic performance, and odds ratios (OR) with their 95% confidence interval (CI) estimates were computed.
Variables with p-values <0.05 were included in the final multivariate model. Model fit for multiple logistic regression was assessed with the Hosmer-Lemeshow test, McFadden's R^2^, and receiver operating characteristic curve (ROC-AUC). Predictive accuracy was evaluated with classification rates and ROC-AUC. Adjusted odds ratios (AOR) with their 95% CI estimates were reported. A 95% confidence level was used for analysis, and the threshold of significance was set at p-value <0.05.
Ethical approval
This study was conducted following approval from the Ethical Review Committee, Institute of Public Health, PUMHSW (Reference No. PUMHSW/SBA/CHS/170 dated March 20, 2023). Students meeting the eligibility criteria were informed about the study's purpose, and written consent was obtained. Information provided by the participants was kept strictly confidential, and all personal identifiers were removed prior to analysis.
Results
Half of the sample comprised Bachelor of Medicine, Bachelor of Surgery (MBBS) students (50.5%). The mean age of participants was 21.5±1.5 years, with nearly half aged 22-23 years. Most students were day scholars (76.0%) and rural residents (62.5%). The mean BMI was 21.8±5.7 kg/m², and 50.0% students had a normal weight. Overall, 76.8% demonstrated good academic performance, with mean aggregate marks of 69.5±9.5 (Table 1).
Table 1: Baseline characteristics of the study participants (N=400)n: number of students; %: column percentage of grand total; MBBS: Bachelor of Medicine, Bachelor of Surgery; DPT: Doctor of Physical Therapy; BSPH: Bachelor of Science in Public Health; BSN: Bachelor of Science in Nursing; Pharm-D: Doctor of Pharmacy.Nutritional status assessed with body mass index (BMI) and classified as: underweight (<18.5 kg/m²), normal (18.5–24.9 kg/m²), and overweight (≥25.0 kg/m²). Good academic performance defined as mean aggregate marks ≥65% in the last exams.
Academic performance revealed significant association with field and year of the study. MBBS students (OR=6.23, 95% CI: 3-13.03, p<0.001) and Bachelor of Science in Public Health (BSPH) students (OR=2.91, 95% CI: 1.21-7.27, p<0.05) had higher odds of good academic performance. Fourth year students (OR=2.84, 95% CI: 1.54-5.37, p<0.05) and fifth year students (OR=7.15, 95% CI: 2.9-21.7, p<0.001) also performed significantly better. No significant associations were observed for age, residence, and accommodation (Table 2).
**Table 2: Sociodemographic predictors of academic performance (N=400)n: number of students; %: row percentage; χ2: chi-square test statistic; OR: odds ratio, calculated from binary logistic regression; CI: confidence interval; Ref: reference category; MBBS: Bachelor of Medicine, Bachelor of Surgery; DPT: Doctor of Physical Therapy; BSPH: Bachelor of Science in Public Health; BSN: Bachelor of Science in Nursing; Pharm-D: Doctor of Pharmacy; *p<0.05, p<0.001.
Parental income was significantly associated with academic performance. Upper middle-class students (OR=2.17, 95% CI: 1.08-4.32, p<0.05) had better odds than lower-class students. Parental education and occupation showed slight variations in performance but failed to achieve significance (Table 3).
**Table 3: Socioeconomic predictors of academic performance (N=400)n: number of students; %: row percentage; χ2: chi-square test statistic; OR: odds ratio, calculated from binary logistic regression; CI: confidence interval; Ref: reference category; *p<0.05, p<0.001.Parental education defined as years of formal education and categorized into: illiterate (no formal education), primary (one to five years), secondary and higher secondary (six to 12 years), and graduation (≥14 years). Parental income defined as monthly income and categorized into: lower class (<50,000 PKR), lower-middle class (50,000-100,000 PKR), upper-middle class (100,001-200,000 PKR), and upper class (>200,000 PKR).
Among the nutritional factors, academic performance was significantly associated with mixed diet (OR=2.93, 95% CI: 1.78-4.81, p<0.001), three or more meals per day (OR=3.34, 95% CI: 1.14-9.41, p<0.05) and daily breakfast (OR=3.88, 95% CI: 2.02-7.44, p<0.001). Students with normal weight performed slightly better, but the differences were not statistically significant (Table 4).
**Table 4: Nutritional predictors of academic performance (N=400)n: number of students; %: row percentage; χ2: chi-square test statistic; OR: odds ratio, calculated from binary logistic regression; CI: confidence interval; Ref: reference category; *p<0.05, p<0.001.Frequency of breakfast, lunch, and dinner defined as the number of times each meal consumed per week and classified into three categories: daily (six to seven times a week), often (three to five times a week), and seldom (zero to two times a week).
The multiple logistic regression model included the variables showing a significant association in binary logistic regression. The significant independent predictors of academic performance were being an MBBS student (AOR=6.08, 95% CI: 1.90-20.9, p<0.05), and fourth- (3.59, 95% CI: 1.81-7.37, p<0.05) and fifth-year student (7.93, 95% CI: 2.9-26.11, p<0.001). Other variables included in the model including, breakfast frequency, meals per day, diet type, and parental income were not statistically significant but showed detectable trends (Table 5).
**Table 5: Multiple logistic regression analysis of predictors of academic performance (N=400)MBBS: Bachelor of Medicine, Bachelor of Surgery; DPT: Doctor of Physical Therapy; BSPH: Bachelor of Science in Public Health; BSN: Bachelor of Science in Nursing; Pharm-D: Doctor of Pharmacy; AOR: adjusted odds ratio, calculated from binary logistic regression for variables with p<0.05 on binary logistic regression; CI: confidence interval; Ref: reference category; *p<0.05, p<0.001.
The model demonstrated a good fit and acceptable discrimination (AUC=0.77). The model correctly classified 79.3% of cases, with a positive predictive value of 80.8% and a negative predictive value of 63.9% (Table 6).
Discussion
This study analyzed the sociodemographic, socioeconomic, and nutritional predictors of academic performance among 400 female undergraduate medical and health sciences students. Field and year of study, parental income, diet type, meals per day, and daily breakfast significantly impacted the academic outcomes, whereas year and field of study remained independent predictors in the multivariate model.
Sociodemographic factors are well-known determinants of academic achievement [13]. MBBS students consistently secured higher marks compared to their allied counterparts. These results are aligned with the studies from Riyadh, Saudi Arabia, and Lahore, Pakistan, where MBBS students outperformed the allied students [13,14]. Generally, the MBBS discipline is well-structured, skill-demanding, with rigorous and competitive admission criteria, which may explain the academic superiority over allied fields. Similarly, performance gradually increased with advancing years, suggesting adaptation to workload, refined learning techniques, and improved ability to understand the course structure and examination pattern over time. Although age showed no significant impact, older students performed better, reflecting accumulated academic experience and curriculum adoption. Accommodation and residence did not demonstrate a statistical association in this study. Despite previous studies noting better performance in hostlers [15,16], day scholars performed slightly better in our study. This difference may be due to unequal sample distribution and external factors.
Parental income revealed a significant positive relationship with academic outcome; students from the upper and upper-middle classes are more likely to perform better, aligning with previous studies from Pakistan and Sudan [6,17]. Higher socioeconomic status is linked to a better learning environment, academic support, and access to resources. While students with professional fathers and employed mothers showed slightly better academic outcomes, these results failed to achieve statistical significance. This implies that parental occupation may provide better opportunities for learning and growth, but may not independently result in good academics. Parental education showed a clear but non-significant upward trend, with graduate parents linked to higher student performance. These results are consistent with reported literature from the United States of America (USA) [18]. The lack of significance in our study may be explained by varying definitions, assessment methods, and scales of education throughout the world.
Nutritional status is a fundamental predictor of human growth and efficiency. Studies conducted on medical and health sciences students from Saudi Arabia and Turkey have shown associations between nutritional status and higher grade point average (GPA) [14,19]. In our study, students with normal weight performed academically better than those who were overweight or underweight; however, the difference was not significant. These variations in results can be attributed to the difference in outcome assessment, methodologies, and sampling fluctuations. Further longitudinal studies are recommended to understand the potential relationship and implement the required nutritional policies for students. Among nutritional factors, mixed diet, three or more meals per day, and daily breakfast were significantly associated with academic performance. Data regarding these dietary factors is lacking in the reported literature for university students, but studies on school students in Iraq, Indonesia, and Malaysia have repeatedly recognized daily breakfast and a balanced diet as significant predictors of academic excellence [20-22].
In the multivariate analysis, year of study and field of study emerged as significant independent predictors of good academic performance. Nutritional factors, dietary patterns, and socioeconomic status showed a higher strength of association in binary logistic analysis, but did not retain statistical significance in the multivariate model. This suggests academic-related variables have a strong impact on academic performance, while nutritional and socioeconomic variables may affect it indirectly. The overall model demonstrated satisfactory predictive accuracy and acceptable discriminatory power and can be used to detect at-risk students.
Limitations of the study
This study did not use any popular nutritional and socioeconomic scale due to difficulty in its use and unfamiliarity with the study participants. Academic performance was only determined by mean aggregate marks, and it didn’t account for other assessment methods, including assignments, projects, comprehensive tests, and class performance. The use of self-reported data for selected variables may introduce minimal reporting bias; however, the key outcome variables were objectively measured. The model has limited explanatory power, a lower negative predictive value due to insufficient sample size in specific subgroups, and potential multicollinearity.
Conclusions
Academic performance is multifaceted and is influenced by academic-related factors, particularly academic progression and the field of study. Nutritional and socioeconomic factors had a limited independent effect. Structured curriculum, cumulative academic exposure, and intensive coursework play a central role in determining student achievement, while nutritional and socioeconomic factors may offer a supportive effect.
Targeted academic support and healthy lifestyle promotion, by incorporating nutritional awareness and student wellness initiatives, within university settings, may enhance student outcomes. Future research should employ standardized nutritional and socioeconomic assessment tools and integrate advanced analytical approaches, including machine learning algorithms and neural network models, to enhance predictive accuracy|.
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