ABSTRACT
Background:
The increasing emphasis on competitive entrance examinations has led many students to join professional coaching institutes, where rigorous academic schedules may increase psychological distress. This study compared anxiety, depression, and academic stress among coaching and noncoaching students.
Aim:
To assess and compare anxiety, depression, and academic stress between coaching and noncoaching students.
Methods:
This cross-sectional study included 300 students aged 13–17 years (150 coaching and 150 noncoaching). Participants were assessed using a semi-structured proforma, the Hamilton Anxiety Rating Scale (HAM-A), the Beck Depression Inventory (BDI), and the Scale of Academic Stress (SAS). Data were analyzed using IBM SPSS Statistics version 21.
Results:
Coaching students had higher mean anxiety (13.74 ± 11.47 vs. 10.89 ± 9.14), depression (14.25 ± 14.09 vs. 9.13 ± 8.46), and academic stress (293.91 ± 80.87 vs. 261.32 ± 65.35) scores than noncoaching students. After Bonferroni adjustment, differences in depression and academic stress remained statistically significant, whereas the difference in anxiety was no longer significant. Anxiety, depression, and academic stress were positively correlated (all P < 0.001).
Conclusion:
Psychological distress was common in both groups but was significantly greater among coaching students, particularly for depression and academic stress. These findings highlight the need for routine mental health screening and timely psychological interventions for students preparing for competitive examinations.
Keywords: Anxiety, depression, education, psychological stress, schools, students
INTRODUCTION
Increasing academic competition has placed considerable pressure on students to achieve high academic performance, with greater emphasis on goal-directed learning than holistic development. Although education offers significant opportunities for personal and professional growth, heightened academic expectations and perfectionism have been associated with increased psychological distress, including anxiety, depression, and stress.[1,2]
Professional coaching institutes provide structured preparation for competitive examinations such as engineering and medical entrance tests. Every year, a large number of students relocate to a prominent coaching city in northern India to pursue such coaching because of the institute’s reputation for producing successful examination outcomes.[3] However, intense competition for limited college seats results in demanding academic schedules, frequent assessments, performance-based batch segregation, and prolonged study hours.[4,5] These factors often reduce opportunities for leisure, physical activity, and social interaction, increasing students’ vulnerability to psychological distress.
Additional stressors include parental expectations, financial burden related to coaching fees and living expenses, separation from family, and suboptimal hostel or paying guest accommodation, all of which may contribute to anxiety, depression, and academic stress.[6,7,8,9] Furthermore, constant comparison with peers and fear of failure can adversely affect students’ mental wellbeing.[10,11,12]
Although several Indian studies have examined mental health among students, most are limited by small sample sizes, restricted geographical coverage, or assessment of only one or two psychological outcomes. A few studies have directly compared anxiety, depression, and academic stress between coaching and noncoaching students.[3,13,14,15,16] Consequently, evidence regarding the psychological impact of intensive coaching environments remains limited.
Therefore, the present study aimed to compare anxiety, depression, and academic stress among coaching and noncoaching students. Understanding these differences may help identify vulnerable groups and inform interventions to promote student mental wellbeing and a more supportive educational environment.
METHODOLOGY
This cross-sectional study was conducted from October 10, 2023 to December 10, 2023 after obtaining approval from the Institutional Ethics Committee (Letter No. F.3 ( ) Acad/Ethical Clearance/2023/49). Written permission was also obtained from the school principal, and informed consent was taken from the parents/guardians or local guardians of all participants.
Participant recruitment and sampling
After obtaining approval from the Institutional Ethics Committee and permission from the respective institutions, eligible participants were recruited using a consecutive sampling technique. A total of 3 coaching institutes (2 NEET-UG and 1 IIT-JEE) and 4 schools (2 government and 2 private) were approached during the study period. Students aged 13–17 years were screened for eligibility according to the predefined inclusion and exclusion criteria. The purpose and procedures of the study were explained to the students and their parents/guardians or local guardians. Written informed consent was obtained from parents/guardians or local guardians, and written assent was obtained from the students before participation.
A total of 342 students were screened for eligibility during the study period. Of these, 42 students were excluded: 17 were attending both regular school and professional coaching classes, 10 were receiving individual tuition/coaching, 4 were outside the eligible age range of 13–17 years, 2 had a history of serious medical, surgical, or psychiatric illness, 1 was receiving mood-altering medication, and 8 either declined participation or did not provide the required consent/assent. The remaining 300 eligible students were enrolled consecutively and included in the study, comprising 150 coaching students (defined as students exclusively enrolled in full-time coaching institutes preparing for IIT-JEE or NEET-UG entrance examinations) and 150 noncoaching students (defined as students attending regular schools who were not enrolled in any professional coaching institute).
Sample size estimation
The sample size was estimated using the formula for comparison of two proportions:
n = [(Zα/2 + Zβ)² {P₁(1 − P₁) + P₂(1 − P₂)}]/(P₁−P₂)²
where
P₁ (coaching students) = 41% (0.41)
P₂ (noncoaching students) = 25% (0.25)
Zα/2 = 1.96 at a 95% confidence level, and Zβ = 0.84 for 80% power.
Substituting these values:
n ≈ 132 participants per group
After adding approximately 10% for nonresponse:
132 × 1.10 ≈ 145 participants per group, rounded to 150 participants per group. So, Total sample size = 300 participants
Inclusion criteria
Professional coaching group: Students aged 13–17 years, of either gender, attending coaching for engineering or medical entrance examinations, and not attending regular school.
Noncoaching group: Students aged 13–17 years, of either gender, regularly attending school (government or private) and not enrolled in professional coaching classes.
Exclusion criteria
Students were excluded if they: (i) were younger than 13 or older than 17 years; (ii) were taking individual tuition or attending both school and professional coaching; (iii) were enrolled in open schooling; (iv) had serious medical, surgical, or psychiatric illnesses (other than stress, anxiety, or depression); (v) were taking mood-altering medication; or (vi) were unwilling to participate.
Data collection procedure
Data collection was carried out at the respective schools and coaching institutes during scheduled sessions in a quiet classroom or designated room ensuring privacy and minimal disturbance. Participants were approached individually or in small groups and were assured regarding the confidentiality and anonymity of the information provided. Sociodemographic details were recorded using a semi-structured proforma. Subsequently, the Beck Depression Inventory (BDI) and Scale of Academic Stress (SAS) were administered to the participants, who completed the questionnaires under the supervision of the investigators. The Hamilton Anxiety Rating Scale (HAM-A) was administered through a face-to-face interview conducted by the investigator, and each item was scored according to the participant’s responses and observed behavior. Completed questionnaires were checked for completeness at the time of collection, and all data were coded and entered into a secure database for statistical analysis.
Assessment tools
Semi-structured proforma: Designed for the study to collect sociodemographic details, academic background, and identification data.
-
Hamilton Anxiety Rating Scale (HAM-A): Developed by Max Hamilton, the HAM-A is a widely used clinician-rated scale comprising 14 items assessing anxiety symptoms, particularly somatic complaints.[17] Each item is rated on a 5-point scale ranging from 0 to 4, where 0 indicates absence of symptoms, 1 indicates mild symptoms, 2 indicates moderate symptoms, 3 indicates severe symptoms, and 4 indicates very severe symptoms. The total HAM-A score is obtained by summing the scores of all 14 items, yielding a possible range of 0 to 56.
For the present study, anxiety severity was categorized as follows: Scores of 0–13 were considered normal or no anxiety, 14–17 as mild anxiety, 18–24 as moderate anxiety, and scores of 25 or above as severe anxiety. Higher scores indicated greater severity of anxiety symptoms.
Beck Depression Inventory (BDI): A 21-item self-report scale assessing depressive symptoms, with each item scored 0–3.[18] The Hindi version by Lal et al. (1974) was used in this study. Total scores range from 0 to 63 and are interpreted as: 0–9 (minimal), 10–18 (mild), 19–29 (moderate), and 30–63 (severe).
Scale of Academic Stress (SAS): This 80-item instrument measures academic stress across four domains: frustration, conflict, pressure, and anxiety using a 5-point Likert format.[19] Positive and negative items are scored in opposite directions. The total score is the sum of frequency and quantity scores. Stress levels were categorized as low (≤P30), average (P31–P69), and high (≥P70), where P = percentile.
Statistical analysis
Data were coded, entered, and analyzed using IBM SPSS Statistics for Windows, Version 21.0 (IBM Corp., Armonk, NY, USA).[20] Descriptive statistics were used to summarize sociodemographic and clinical variables. Inferential statistics were applied to test associations between variables, with a significance threshold of P < 0.05. To reduce the risk of Type I error arising from multiple testing, the Bonferroni correction was applied to families of related comparisons.
RESULTS
The mean age of coaching students was significantly higher than that of noncoaching students (P < 0.0001). Significant differences were observed in domicile, parental occupation, annual family income, living status, family type, and subject selection [Table 1].
Table 1.
Distribution of coaching (n=150) and noncoaching students (n=150) according to sociodemographic variables
| Indicators | Subgroups | Coaching students n (%) | Noncoaching students n (%) | Chi-square |
|---|---|---|---|---|
| Domicile | Urban | 103 (68.7%) | 42 (28%) | 49.67* |
| Rural | 47 (31.3%) | 108 (72%) | ||
| Father’s occupation | Self-employed | 67 (44.7%) | 81 (54%) | 34.38* |
| Govt. job | 50 (33.3%) | 21 (14%) | ||
| Semi-Govt. job | 1 (0.7%) | 5 (3.3%) | ||
| Private job | 22 (14.7%) | 9 (6%) | ||
| Farmer | 10 (6.7%) | 34 (22.7%) | ||
| Mother’s occupation | Self-employed | 7 (4.7%) | 20 (13.3%) | 19.37* |
| Govt. job | 21 (14%) | 6 (4%) | ||
| Semi-Govt. job | 4 (2.7%) | 0 (0%) | ||
| Private job | 5 (3.3%) | 3 (2%) | ||
| House-wife | 113 (75.3%) | 121 (80.7%) | ||
| Annual family Income | <1 Lac | 35 (23.3%) | 100 (66.7%) | 63.83* |
| 1 Lac - 5 Lac | 60 (40%) | 38 (25.3%) | ||
| >5 Lac | 55 (36.7%) | 12 (8%) | ||
| Living status | With family member | 74 (49.3%) | 139 (92.7%) | 68.76* |
| Alone | 76 (50.7%) | 11 (7.3%) | ||
| Family type | Joint | 49 (32.7%) | 82 (54.7%) | 16.92* |
| Extended Nuclear | 11 (7.3%) | 3 (2%) | ||
| Nuclear | 90 (60%) | 65 (43.3%) | ||
| Subject selection | Self | 132 (88%) | 116 (77.3%) | 6.02* |
| Parents | 16 (10.7%) | 31 (20.7%) | ||
| Other | 2 (1.3%) | 3 (2%) |
*Significant after Bonferroni correction (α≈0.001)
Coaching students showed higher proportions of moderate-to-severe depression and high academic stress than noncoaching students [Table 2].
Table 2.
Distribution of anxiety, depression, and academic stress levels among coaching (n=150) and noncoaching students (n=150)
| Scale | Category | Coaching students n (%) | Noncoaching students n (%) | Total n (%) | Chi-square value |
|---|---|---|---|---|---|
| Anxiety (HAM-A) | Normal (0–13) | 87 (58%) | 103 (68.7%) | 190 (63.3%) | 6.195 |
| Mild (14–17) | 18 (12%) | 13 (8.7%) | 31 (10.3%) | ||
| Moderate (18–24) | 17 (11.3%) | 19 (12.7%) | 36 (12%) | ||
| Severe (≥25) | 28 (18.7%) | 15 (10%) | 43 (14.3%) | ||
| Depression (BDI) | Minimal (0–9) | 86 (57.3%) | 108 (72%) | 194 (64.7%) | 17.836* |
| Mild (10–18) | 20 (13.3%) | 18 (12%) | 38 (12.7%) | ||
| Moderate (19–29) | 17 (11.3%) | 19 (12.7%) | 36 (12%) | ||
| Severe (≥30) | 27 (18%) | 5 (3.3%) | 32 (10.7%) | ||
| Academic Stress (SAS) | Low Stress (≤30th percentile) | 35 (23.3%) | 51 (34%) | 86 (28.7%) | 14.873* |
| Average Stress (31st–69th percentile) | 45 (30%) | 61 (40.7%) | 106 (35.3%) | ||
| High Stress (≥70th percentile) | 70 (46.7%) | 38 (25.3%) | 108 (36%) |
*Significant after Bonferroni correction (adjusted α≈0.017)
Coaching students had significantly higher mean depression and academic stress scores than noncoaching students after Bonferroni adjustment. Although mean anxiety scores were higher among coaching students, this difference was no longer statistically significant after correction for multiple comparisons [Table 3].
Table 3.
Comparison of mean age and mental health scores (anxiety, depression, and academic stress) between coaching (n=150) and noncoaching students (n=150)
| Parameter | Coaching (Mean±SD) | Noncoaching (Mean±SD) | t | P |
|---|---|---|---|---|
| Age | 16.69±0.77 | 15.50±0.97 | 11.83 | <0.0001* |
| Anxiety Score (HAM-A) | 13.74±11.47 | 10.89±9.14 | 2.38 | 0.0178 |
| Depression Score (BDI) | 14.25±14.09 | 9.13±8.46 | 3.82 | 0.0002* |
| Academic Stress (SAS) | 293.91±80.87 | 261.32±65.35 | 3.84 | 0.0002* |
*Significant after Bonferroni adjustment (α=0.0125)
Correlation analysis demonstrated that, after Bonferroni correction, only age showed a significant inverse correlation with depression scores, whereas no other sociodemographic variable remained significantly associated with anxiety, depression, or academic stress [Table 4]. Anxiety was moderately positively correlated with depression (r = 0.586, P < 0.001), whereas weaker but statistically significant positive correlations were observed between anxiety and academic stress (r = 0.294, P < 0.001) and between depression and academic stress (r = 0.219, P < 0.001) [Table 5].
Table 4.
Correlation of sociodemographic variables with anxiety, depression, and academic stress among all participants (n=300)
| Indicators | Pearson’s r (P) | ||
|---|---|---|---|
|
| |||
| Anxiety | Depression | Stress | |
| Age | −0.065 [0.265] | −0.220 [<0.001]* | 0.064 [0.271] |
| Gender | 0.064 [0.269] | 0.149 [0.010] | −0.010 [0.864] |
| Religion | −0.105 [0.070] | −0.007 [0.906] | −0.007 [0.904] |
| Domicile | −0.102 [0.076] | −0.047 [0.414] | 0.018 [0.761] |
| Father’s education | 0.075 [0.196] | 0.119 [0.039] | 0.101 [0.082] |
| Mother’s education | 0.070 [0.226] | 0.054 [0.352] | 0.040 [0.486] |
| Father’s occupation | −0.036 [0.530] | −0.066 [0.257] | −0.047 [0.421] |
| Mother’s occupation | 0.054 [0.347] | −0.036 [0.540] | 0.054 [0.353] |
| Number of siblings | −0.020 [0.731] | −0.131 [0.023] | 0.065 [0.263] |
| Annual family income | 0.040 [0.486] | 0.108 [0.062] | 0.054 [0.347] |
| Family size | 0.009 [0.871] | −0.061 [0.295] | −0.038 [0.507] |
| Family type | 0.040 [0.485] | 0.067 [0.246] | −0.029 [0.614] |
| Living status | 0.126 [0.029] | 0.121 [0.036] | 0.011 [0.851] |
| Grade | −0.094 [0.105] | −0.138 [0.017] | −0.083 [0.150] |
| Stream | −0.038 [0.516] | −0.052 [0.371] | 0.044 [0.443] |
| Subject selection | 0.134 [0.020] | 0.043 [0.457] | 0.067 [0.251] |
| Type of school | 0.028 [0.635] | 0.064 [0.271] | −0.122 [0.035] |
*Significant after Bonferroni correction (α≈0.001)
Table 5.
Correlation between anxiety, depression, and academic stress among all participants (n=300)
| Variable | Test applied | Anxiety | Depression | Stress |
|---|---|---|---|---|
| Anxiety | Pearson Correlation | 1 | 0.586** | 0.294** |
| Sig. (2-tailed) | <0.001 | <0.001 | ||
| Depression | Pearson Correlation | 0.586** | 1 | 0.219** |
| Sig. (2-tailed) | <0.001 | <0.001 | ||
| Stress | Pearson Correlation | 0.294** | 0.219** | 1 |
| Sig. (2-tailed) | <0.001 | <0.001 |
**Correlation is significant at P<0.01 level (2-tailed)
DISCUSSION
The present study compared anxiety, depression, and academic stress among coaching and noncoaching students and found that coaching students experienced significantly greater psychological distress. After adjustment for multiple comparisons, coaching students had significantly higher depression and academic stress scores, whereas the difference in anxiety scores was no longer statistically significant. Significant positive correlations were also observed among anxiety, depression, and academic stress, suggesting that these psychological problems frequently coexist.
The two groups differed in several sociodemographic characteristics, including domicile, parental occupation, annual family income, living status, family type, and subject selection. These differences likely reflect disparities in access to coaching education. Students from families with better socioeconomic resources are more likely to afford professional coaching, while those attending coaching often relocate from their hometowns and live away from their families.[21,22] Such differences should be considered while interpreting the findings.
Our findings are consistent with those of Sharma et al.,[13] who reported significantly higher levels of depression and anxiety among students attending coaching classes for medical entrance examinations compared with noncoaching students. The greater psychological distress among coaching students may be explained by the intense academic competition, frequent examinations, performance pressure, and fear of failure associated with entrance examination preparation.[2] Students living away from their families may additionally experience loneliness and reduced social support, while the financial burden of coaching fees and related expenses may further contribute to psychological distress.[6,7,23] Limited opportunities for recreation and social interaction may also adversely affect overall wellbeing.[24]
Several previous studies have identified academic pressure, family-related stressors, adverse life events, and financial difficulties as important contributors to depression, anxiety, and stress among adolescents and young adults.[25,26,27,28] These findings support the present observation that coaching students, who are exposed to multiple academic and psychosocial stressors simultaneously, are at greater risk of psychological distress.
Although several sociodemographic variables showed nominal associations with anxiety, depression, and academic stress on unadjusted analyses, only the inverse correlation between age and depression remained statistically significant after Bonferroni correction. This finding suggests that many apparent associations may reflect chance findings resulting from multiple statistical comparisons and should therefore be interpreted cautiously.
A significant positive correlation was observed among anxiety, depression, and academic stress, indicating that students with higher academic stress also tended to report greater anxiety and depressive symptoms. Similar associations have been reported previously, with academic stress consistently identified as an important correlate of depression and anxiety among adolescents.[29,30,31] Experimental and clinical evidence also suggests that prolonged stress can adversely affect neurobiological mechanisms involved in mood regulation, thereby increasing vulnerability to depression and anxiety.[32] Furthermore, anxiety and depression frequently coexist and may mutually reinforce one another over time, contributing to poorer psychological wellbeing.[33,34,35,36,37] These findings underscore the importance of addressing psychological distress in a comprehensive rather than symptom-specific manner.
The findings of this study have important practical implications. Educational institutions and coaching centers should consider implementing routine mental health screening, accessible counseling services, stress management programs, and initiatives that encourage healthy coping strategies and social support. Early identification and timely intervention may help reduce psychological distress and improve students’ overall wellbeing.
The study has certain limitations. It was conducted in a single coaching city in northern India, which may limit the generalizability of the findings. The cross-sectional design precludes causal inferences. The sample size was modest relative to the student population, and reliance on self-report measures for depression and academic stress may have introduced reporting bias. In addition, students receiving individual tuition or those who had discontinued their studies were not included, and residual confounding due to baseline sociodemographic differences between the two groups cannot be excluded.
CONCLUSION
Both coaching and noncoaching students experience psychological distress; however, depression and academic stress were significantly higher among coaching students. Anxiety, depression, and academic stress were positively correlated, highlighting the need for comprehensive mental health strategies that promote early identification, timely intervention, and psychological wellbeing among students.
Implications
The findings provide evidence to help students, parents, and educators weigh the academic benefits against the potential psychological risks associated with professional coaching.
Routine mental health screening, counseling services, and stress management programs should be incorporated into schools and coaching institutes to promote student wellbeing.
Early identification and appropriate psychological interventions may help reduce psychological distress and prevent adverse outcomes, including self-harm and suicide.
Declaration regarding the use of generative AI
The authors attest that there was no use of generative artificial intelligence (AI) technology in the generation of text, figures, or other informational content of this manuscript.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
This study was conducted as part of the Indian Council of Medical Research, Short Term Studentship (ICMR-STS) 2023.
Funding Statement
Indian Council of Medical Research – Short Term Studentship (ICMR-STS) 2023 program, with a stipend of ₹50,000 awarded.
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