Get Permission Kotgirwar, Lalwani, and Athavale: Preadmission profile and academic performance: Are we selecting the best students for medicine?


Introduction

Selection of appropriate candidates for medical education is a challenging task. There is a continuous search for appropriate factors that are valid, reliable, cost effective and less time consuming. The most important being, what criterion if applied, can select the best candidates as future doctors.1

Many studies across the world have tried to explore various cognitive and non-cognitive predictors which influence academic performance of medical students.1, 2, 3, 4

In Indian medical education scenario, admission to Medical colleges across India is by any one of the following three methods i) Via All India institute of medical sciences entrance for admission to seven (AIIMS) (conducted by AIIMS New Delhi), ii) Jawaharlal Nehru Institute of Medical sciences Pondicherry entrance for admission to JIMPER(conducted by the same institute) and iii) Via National eligibility cum entrance test (NEET) for admission to around 450 medical colleges across India conducted by Central board of Secondary Education (CBSE). All these examinations prescribe syllabi that do not conform to any particular school board and utilize the sole criteria (score in entrance test) for admissions to medical colleges.

There has been no research or experimentation regarding the ability of this entrance examinations to choose appropriate candidates who shall achieve the competencies of all domains (that is cognitive, psychomotor and affective) and shall serve the community at large.

The present study was aimed at exploring various cognitive and non-cognitive variables that can predict performance of medical students studying in tertiary care teaching and research medical institute of Government of India, Bhopal. This institute has the status of Institute of National importance. The institute draws students by a national eligibility cum entrance test conducted by AIIMS New Delhi. These admissions are considered very coveted as around 4 lac students compete for some 700 seats every year.5 These students had cleared AIIMS entrance to secure admission to the institute.

The study was aimed at exploring such criterion which may have sufficient predictive strength so as to consider them for intake of medical students, or may show a window of opportunity in predicting potential under achievers so as to initiate a counselling or intervention programme early.

Methodology

This observational cross-sectional study was carried out on students who appeared for the university examination for first year MBBS at Tertiary care centre Bhopal. 147 students participated in this pilot study. The study was granted permission by institutional ethical committee and informed consent was obtained from the study A prevalidated, pretested, structured questionnaire was used to collect information from the students (Annexure I). The questionnaire contained questions seeking information on predictor variables (preadmission factors) that may be related to academic performance of the MBBS students.

The questionnaire also enquired about the dependent variables that were the percentile of marks scored in university examination of first year MBBS. The responses of the study participants to the entire questionnaire were recorded. Socioeconomic status of student’s family was assessed by modified Kuppuswamyscale6based on parent’s occupation, income and number of family members.

Statistical analysis was performed using statistical package SPSS 20. Standard tests for descriptive statistics were applied.

The relationship of study variables whose responses were on continuous scale, with academic performance were analysed by Pearson’s correlation followed by regression analysis. Independent sample T test and one way ANOVA was applied for analysing the relationship of study variables whose responses were categorised in two groups and three or more groups respectively. Value of less than 0.05 was considered to be statistically significant.

Results

Table 1 Sshows frequency distribution of various study variables. Of the 147 study participants, about two-thirds were male. About two-thirds were more than 18 yrs of age at the time of their entry into the medical college. Two third students were selected in first attempt in entrance examination. While two thirds of the students studied in Central Board of Secondary Education (CBSE) in their tenth standard, this proportion increased to three fourth in twelfth standard. The increase was at the cost of decline in students choosing to study ICSE board in favour of CBSE. Majority of the students hailed from schools with English as medium of instruction. This proportion increased from primary to higher secondary schools. Majority of the students belonged to urban areas. A gradual shift of students from rural to urban settings was observed as they progressed from primary, secondary and higher secondary schooling. Majority of the students studied in private schools. Almost 85 percent students hailed from upper or upper middle class socioeconomically. The All India ranks of students in AIIMS entrance examination ranged from 58- 8829 (median of rank 486.5). Of the study participants 54.4% were selected via general category and remaining were selected from reserved category for socially backwards (An affirmative scheme for up liftment of socially backwards) (OBC- 31.3%, SC -10.2% & ST- 4.1)

The mean marks scored by students in X standard were 90.84% ±7.057 and in XII standard marks scored were 90.36%±6.88.

About 2/3 students had entered the medical profession by their own choice. Almost all students attended coaching for entrance examination for at least one year besides their routine schooling. Eighty three per cent students attended regular school and 16.2% attended a dummy school. (A dummy school is a term used for such schools which though are registered as regular schools but permit absenteeism to facilitate the student to attend coaching/ study for entrance examination).

Table 2 shows correlation of study parameters on continuous scale with that of academic performance of students. Significant positive correlation was observed between marks obtained in tenth and twelfth standard with academic performance. The strength of correlation was more with class XII marks. However significant negative correlation was observed with number of attempts taken to succeed in entrance examination. The performance of students did not significantly correlate with their respective ranks in entrance examination and duration of coaching.

Table 1

Showing frequency of distribution of various study parameters

S.No

Parameters

Groups

Frequency

Valid Percentage

1

Sex

Female

101

68.7

Male

46

31.3

2

Age

18 years

53

36.1

>18 years

94

63.9

3

Attempts

97

66

46

31.3

03

2.0

01

0.7

4

10th Board

State

33

22.4

CBSC

100

68.0

ICSC

14

9.5

5

12th Board

State

33

22.4

CBSC

112

76.2

ICSC

02

1.4

6

Reservation

General

77

52.4

OBC

48

32.7

SC

16

10.9

ST

06

4.1

7

Medium in school (Primary)

English

122

83.0

Vernacular

25

17

8

Medium in school (Secondary)

English

131

89.1

Vernacular

16

10.9

9

Medium in school (Higher Secondary)

English

136

92.5

Vernacular

11

7.5

10

Place of stay (Primary)

Metro

17

11.6

District

62

42.2

Tehsil

22

15.0

Village

46

31.3

11

Place of stay(Secondary)

Metro

20

13.6

District

73

49.7

Tehsil

17

11.6

Village

37

25.2

12

Place of stay(Higher Secondary)

Metro

21

14.3

District

85

57.8

Tehsil

13

8.8

Village

28

19

13

Schooling (Primary)

Government Non Residential

32

21.8

Government Residential

0

00

Private Non Residential

109

74.1

Private Residential

06

4.1

14

Schooling (Secondary)

Government Non Residential

31

21.1

Government Residential

9

6.1

Private Non Residential

99

67.3

Private Residential

08

5.4

15

Schooling (Higher Secondary)

Government Non Residential

36

24.5

Government Residential

7

4.8

Private Non Residential

86

58.5

Private Residential

8

12.2

16

Kuppuswamy Scale *4.1% of students did not mention the household income, hence could not calculate.

1-Upper class

61

41.5

Upper middle class

63

42.9

Lower middle class

16

10.9

Upper lower

0.1

0.7

17

Duration of coaching

None

03

2.0

One year

52

35.4

One and half year

02

1.4

Two years

68

46.3

Three years

18

12.2

Four years

04

2.7

18

Sports and extracurricular

Basic level

54

9.5

Advance level

79

53.7

Not attempted

14

36.7

19

Joining of profession

Own choice

96

65.3

Others choice

04

2.7

Mixed choice

47

32.0

Table 2

Showing correlation of study parameters on continuous scale with that of academic performance of students

Total Theory Percentile

Total Practical Percentile

Rank

Pearson Correlation

-.116

-.136

Sig.(2 tailed)

.195

.132

N

126

124

Attempts

Pearson Correlation

-.330

-.294

Sig.(2 tailed)

.000*

.001*

N

129

127

Marks (Tenth)

Pearson Correlation

.351

.335

Sig.(2 tailed)

.000*

.000*

N

129

127

Marks(Twelfth)

Pearson Correlation

.438

.403

Sig.(2 tailed)

.000*

.000*

N

129

127

Duration of coaching

Pearson Correlation

-.015

-.213

Sig.(2 tailed)

.864

.016

N

129

127

[i] *Statistically significant

Table 3

Showing multiple linear regressions of predictor variables with respect to dependent variable for total theory and practical percentile

Model

Unstandardized Coefficients

Standardized Coefficients

t

Significance

B

Standard Error

Beta

T

P

T

P

T

P

T

P

T

P

(Constant)

-76.143

-54.403

38.475

34.315

-

-

-1.979

-1.585

.050

.115

Rank

.000

-.001

.001

.001

-.026

-.053

-.327

-.666

.744

.506

Attempts

-7.326

-6.335

3.965

3.537

-.163

-.160

-1.847

-1.791

.067

.075

Tenth

.257

.198

.372

.332

.068

.059

.691

.596

.490

.552

Twelth

1.208

1.074

.461

.411

.307

.309

2.619

2.611

.010*

.010*

Duration of coaching

1.980

3.664

2.645

2.359

.058

.122

.748

1.553

.455

.123

@12E

.021

-.086

.226

.202

.010

-.044

.094

-.428

.925

.669

[i] * Statically Significant; T- Theory; P- Practical

Table 4

Showing relationship between study variables (which showed normal distribution in two groups) total theory and practical percentile as assessed by independent sample t test

Factors

Groups

Mean Marks and Standard Deviation

T test of equality of means

df value

95% Confidence level

P value

T

P

T

P

T

P

T

P

T

P

Sex

Male

101

47.56 ± 28.05

47.72±25.61

- 2.37

- 2.14

145

145

-20.49 to – 1.86

-17.50 to – .70

.019

.034

Female

46

58.74±22.67

56.82±19.54

Age

18 yrs

53

51.98 ±27.21

52.94 ±25.09

.310

.892

145

145

-7.72 to10.60

-4.50 to 11.92

.757

.374

> 18 yrs

94

50.54±26.88

49.23 ±23.69

Type of school attended

Regular

124

51.05± 26.63

51.18 ±24.21

-.002

.712

145

145

-12.13 to 12.10

-6.95 to 14.79

.998

.477

Dummy

23

51.07±29.01

47.26 ±24.32

Coaching institute attended

Yes

142

51.87±26.42

51.22±23.59

3.126

3.315

143

143

17.59 to 78.11

18.30 to 72.35

0.002*

0.001*

No

03

4.02±2.92

5.90 ±4.65

Not responded

02

Sports and extracurricular activity

Basic

79

49.80 ±27.57

49.11 ±24.77

- . 896

- 1.32

131

131

-13.36 to 5.03

-13.69 to 2.69

.372

.186

Advance

54

53.97 ±24.40

56.61 ±22.35

Not responded

14

Medium in school (Primary)

English

122

52.78 ± 26.24

50.83 ±23.46

1.724

.288

145

145

-1.48 to 21.71

-8.99 to 12.06

.087

.773

Vernacular

25

42.66 ±29.08

49.29 ±27.95

Medium in school (Secondary)

English

131

52.09 ±26.48

50.50 ±23.32

1.331

-.094

145

145

-4.58 to 23.51

-13.31to 12.09

.185

.925

Vernacular

16

42.62 ±29.80

51.11 ±31.29

Medium in school (Higher Secondary)

English

137

52.56 ±26.54

51.40 ±24.13

2.552

1.561

145

145

4.97 to 39.18

-3.27 to 27.89

0.12*

.121*

Vernacular

10

30.47 ±24.47

39.10 ±23.13

[i] *Statistically significant; T- Theory; P- Practical

Table 5

Showing relationship between study variables (distributed in more than two groups), total theory and practical percentile as assessed by one way ANNOVA test

Sum of Squares

df

Mean Square

F

Significance

T

P

T

P

T

P

T

P

T

P

Board X

Between Groups

28.628

24.344

90

86

.318

.283

1.119

.841

.328

.772

Within Groups

15.917

20.200

56

60

.284

.337

Total

44.544

44.544

146

146

Board XII

Between Groups

16.329

18.163

90

86

.181

.211

.837

1.230

.776

.198

Within Groups

12.133

10.300

56

60

.217

.172

Total

28.463

28.463

146

146

Reservation

Between Groups

69.467

64.917

90

86

.772

.755

1.385

1.267

.095

.166

Within Groups

31.200

35.750

56

60

.557

.596

Total

100.667

100.667

146

146

Place of stay (Primary)

Between Groups

101.877

84.327

90

86

1.132

.981

1.110

.788

.341

.846

Within Groups

57.117

74.667

56

60

1.020

1.244

Total

158.993

158.993

146

146

Place of stay (Secondary)

Between Groups

89.991

80.207

90

86

1.000

.933

.922

.794

.639

.838

Within Groups

60.717

70.500

56

60

1.084

1.175

Total

150.707

150.707

146

146

Place of stay Higher (Secondary)

Between Groups

78.610

74.627

90

86

.873

.868

.946

.935

.599

.617

Within Groups

51.717

55.700

56

60

.924

.928

Total

130.327

130.327

146

146

Schooling (Primary)

Between Groups

67.366

69.899

90

86

.749

.813

.958

1.183

.578

.246

Within Groups

43.750

41.217

56

60

.781

.687

Total

111.116

111.116

146

146

Schooling (Secondary)

Between Groups

67.333

69.117

90

86

.748

.804

.898

1.074

.680

.388

Within Groups

46.667

44.883

56

60

.833

.748

Total

114.000

114.000

146

146

Schooling (Higher Secondary)

Between Groups

76.304

105.137

90

86

.848

1.223

.705

1.903

.931

.005

Within Groups

67.383

38.550

56

60

1.203

.643

Total

143.687

143.687

146

146

Joining of Profession

Between Groups

71.800

73.133

90

86

.798

.850

.814

.953

.809

.585

Within Groups

54.867

53.533

56

60

.980

.892

Total

126.667

126.667

146

146

[i] T- Theory; P- Practical

Table 3 show multiple linear regression analysis of predictor variables (on continuous scale) with respect to dependent variable i.e. theory and practical percentile. Relationship between predictor variables with academic performance was analyzed by independent sample T Test for the variables which showed normal distribution in two groups (Table 4).

To explore relationship of predictor variables grouped in more than two categories, one way ANNOVA was applied. The results showed that these variables did not have any significant relationship with academic scores of students except with type of school attended in Higher Secondary (Table 5).

Discussion

Academic performance in medical schools may be influenced by large number of factors. The present study chose to explore the relationship of preadmission factors like sociodemography, prior academic performance, schooling and related issues with the performance of students in Medical college. There is a debate worldwide, whether or not preadmission factors affect student’s performance.6, 7, 8 Also there is an on-going debate as to what might be the best method to select best talents for medical training.9, 10

Prior academic performance

There are many studies which endorse that the prior academic performance of the student strongly and positively influences the performance in universities.2, 11, 12, 13, 14, 15, 16 Some studies however claim that no such relationship exists.17 The present study found significant positive correlation between academic scores of students in class X and XII. Stronger correlation was observed with scores obtained in class XII. Authors are of the opinion that scores of class XII should be given weightage for admission to medical institutes. This would avoid overemphasis on a single entrance examination, as is now the case, and shall also reinforce the importance of well-established school examination system. This would also check the coaching institutes which have become informal parallel teaching machinery, with no checks and balances, created just to crack an entrance examination. The challenge in doing this is to equate scores of different school boards, across the country.

Socio economic status

Many studies report a strong influence of socioeconomic status of parents on educational outcomes of students. It is a common belief that low social economic status negatively affects academic achievement because low social economic status prevents access to vital resources.18, 19, 20 Considine and Zappala state that, in families where the parents are advantaged socially, educationally and economically foster a higher level of achievement in their children.21 They also found that these parents provide higher levels of psychological support for their children through environments that encourage the development of skills necessary for success at school. On the contrary Pedrosa et al. and Mohammad et al, in their study on educational and socio-economic background, found that students coming from disadvantaged socioeconomic and educational homes perform relatively better than those coming from higher socioeconomic and educational strata.22, 23 Lumb and Vali and Mohammad et al have also reported no relationship of students performance in medical course to socioeconomic status.2, 23

Interestingly the descriptive statistics revealed that almost 85% students came from high socioeconomic strata (upper and upper middle class). In the present study the performance of students did not show any relationship with the socioeconomic status as calculated by Kuppuswamyscale. This may be because the college provides an equal opportunity for learning to all students which create an insulated facilitatory environment for all students equally. However, the entrance examination is highly skewed in favour of students from high socioeconomic status.

The socially backward

Government of India reserves up to 50% (recently increased to 60% to include economically backwards also) of the total seats in state run Medical colleges for socially backward class (scheduled class, scheduled tribes and other backward classes).24 This is a form of affirmative action that attempts to compensate for the social inequality once prevalent in the form of caste system in India. However, as was observed in the study, the performance of socially backward students selected utilizing the facility of reservation, generally on scores lower than the unreserved group, did not affect the academic performance in medical college. Ironically almost 85% students from the reserved category (socially backward as per Government guidelines) came from higher socioeconomic status (upper and upper middle class) as per Kuppuswamy scale. This contradiction indicates that the facility of reservation is availed more often by the better offs in their respective category than the real needy ones.

Rural urban divide

Most of the studies conducted around the world confirm that students coming from rural background underperform compared to their urban counterparts. This is primarily because of lack of instructional resources.25, 26, 27 However a counterview is that it does not make any difference.28 The present study observed that though the entrance test was very heavily skewed towards students from urban areas, there was no relationship between the residence of students and their academic performance in Medical College, meaning thereby that students from both backgrounds performed equally. As per the census of India 2011, the rural –urban proportion of population is 68.84% & 31.16%.29 It is indeed appalling to note that he entrance examination leaves out a large section of population as ‘not fit’ to be doctors.

Language barrier

India being a multi-cultural society has many regional languages and no particular language is considered as National language. While at the school level students have an option of studying in different vernacular languages whereas the admission test for admission to AIIMS is conducted in only two languages i.e. English and Hindi. As per census of India 2011, there are only 0.02% citizens speaking English as their first language and 12.18% as second or third language.30 The medical education in India is primarily in English language, might be as a vestige of British colonial rule. A study by Moulsey et al observed that English language competence has a significant correlation with academic performance in Saudi Arabia, as the professional course is taught in English language.31 Whereas Mohammad M et al. did not find any correlation in medical students of UAE.23 Similar observation was reported from Gautam et al in a study of Medical students at Nepal.1 The present study found no relationship between the performance of students in medical college vis a vis their language of instruction during schooling. This implies that the students from any vernacular medium cope reasonably to instructions in English and that it does not affect their performance. The reason might be that in a professional course there is less emphasis on correctness of language than the technical component. However, as is evident from the descriptive statistics of the students, the exam seems to favour students from English medium as compared to vernacular background.

Background of school

Different types of schooling systems operate in India. These include government run schools, which are mostly poorly equipped and financed but the education is subsidized. Another group is private run schools which are believed to impart better education at higher costs and hence are preferred by socioeconomically well of sections of society.

These schools operate under different school boards namely Central board of Secondary education (CBSE), Indian certificate of secondary education (ICSE), and boards of different states of India (e.g. Maharashtra state board, Tamilnadu state board). The norms of CBSE and ICSE are more stringent and hence only better financed and better equipped schools can affiliate to them.32, 33 Most other schools affiliate with respective state boards. These boards have different curricula and assessment patterns and hence equating scores of different boards is debatable. Although lakhs of students take entrance examination from different schools and boards the selected candidates, as is evident from descriptive statistics, majority of the students selected, and come from CBSE board. This is also evident from shift of students from ICSE board and vernacular boards to CBSE board during higher secondary.

Also, the number of students selected from private schools is disproportionately more.

While many studies claim that type of schools attended did not affect the performance of students34, 11 while other’s claim the contrary.2 The present study did not show any relationship between school boards and academic performance. The significant values as obtained between schooling in Higher secondary and performance in practical examination is to be taken with caution due to chances of error due to small numbers in different groups.

The best predictor

In pursuit of selecting the best talent for medical course many universities use multiple predictors. There is overwhelming evidence that use of combination of predictors is better indicator of student’s performance in medical school/ university.

In a socio-demographically unequal, culturally diverse, country with gross educational inequality like India, it is indeed challenging to pick such predictors which are significant, practical, uniform and objective at the same time and are representative of the society at large. Authors feel that overreliance on one entrance examination, which seems to be primarily choosing urban, English speaking students from CBSE board belonging to high socioeconomic backgrounds, should be avoided.

The entrance test should be tailored to have representation of larger population of the country. This is partly addressed by reservation policy for socially backwards; however very large representation of socioeconomically high strata in this category also seems to be defeating the purpose.

Conclusion

  1. The entrance examination was found to be highly skewed towards urban, english medium students, coming from high socioeconomic strata studying in CBSC board schools, although these factors did not show any relationship to academic performance in medical college.

  2. An admission index/score can be prepared which takes into account class XII scores and attempts taken to clear entrance examination. As these factors have significant relationship with academic performance.

  3. Policy makers need to seriously consider to make the entrance examination more inclusive for students of various linguistic backgrounds, different socioeconomic strata, and different geographic backgrounds and across all school boards.

Source of Funding

Nil.

Conflict of Interest

None.

Acknowledgement

None.

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Received : 03-08-2022

Accepted : 14-08-2022


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https://doi.org/10.18231/j.ijcap.2022.042


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