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The relationship between academic performance and severity of depressed mood during medical school
The Relationship Between Academic Performance and Severity of Depressed Mood During Medical School David C. Clark, Steven R. Daugherty, Peter B. Zeldow, Gerald S. Gotterer, and Donald Hedeker We employ a structural
equation
and depressed
over
mood
model to examine the relationship
4 years
measures
included
undergraduate
gradepoint
average,
full Medical
(NB)
scores.
Inventory Overall
Severity
for a single gradepoint
College Admissions
of depressed
mood
there
is little
reason
a non-significant
to
tendency
influence
Board scores,
averages
consistently
result directs
D 1988
which
reported
attention
to admit
subgroup
was
think
that
for mood fewer
depressive
distress
of medical
or symptoms.
The
class.
and
Test (MCAT)
academic
performance
Academic
second-year
performance
medical
and total National
by administering
school
Boards Part I
the Beck Depression
and once per year during the last 2 years. mood
states
compromise
school for the class as a whole.
academic
Medical
school
and mood had no direct impact on grades. There
in the
in turn influenced
to a subgroup
first-
depressive
mood,
between
school
assessed
during the first 2 years of medical
grades had no direct impact on depressed
prone
average,
two times per year during the first 2 years,
performance was
medical
months mood.
preceding
Students
symptoms
throughout
students
National
with
medical
less susceptible
non-susceptible
and/or
Boards
higher college
Part
school. The latter
to depression,
minimizing
I to
gradepoint
qualities
or less of this
merit further investigation. by Grune & Stratton,
Inc.
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HAT IS THE LINK between academic performance and mood for medical students? Do poor grades make students depressed? Does depression cause students to get lower grades? Intuitively, we might assume both are true. Medical students emerge from high school and college with gradepoint averages which place them in the top fractiles of their respective classes, so that most find themselves in the middle of a class distribution for the first time during medical school, where occasional poor grades or course failures are not uncommon. Most of us have felt the pangs of doubt or depressed mood which accompany substandard performance at one time or another. And certainly severe depression impairs cognitive performance.’ Yet beyond these intuitions, the link between grades and depression is far from clear. Evidence of a correlation between academic performance and mood does not explain how they are linked. If depressed mood and diminished academic performance are correlated, the correlation may be due to some causal relationship between them, or it may not. Correlation does not necessarily imply causality. The correlation may be due to some third, unknown causal agent, or be spurious.2
From the Department of Psychiatry, Rush Medical College, Chicago. This research is part of the Rush Longitudinal Study, supported by the National Fund for Mudicul Education (Burroughs Wellcome Fund and Ameritech Foundation). the Chicago Community Tru.st. and the American Medical Association Education and Research Foundation. Principal Investigators include Jefiey Salloway, Linda Leksas, Edward Eckenfels. and Phyllis Blumberg, who participated equally in study design. data collection, and the early stages ojanalysis. Address reprint requests to David C. Clark, Ph.D., Department of Psvchiatry, Rush Medical College, 1720 West Polk St., Chicago, IL 60612. 0 1988 by Grune & Stratton, Inc. OOIO-440.%‘/88/2904/0008$03.00/0 Comprehensive
Psychiatry,
Vol. 29,
No. 4 (July/August),
1988:
pp 409-420
409
CLARK ET AL
410
Previous studies in medical education have been influenced by a priori assumptions about the nature and sources of stress for medical students. Student distress has sometimes been viewed as the result of failings of the student, and sometimes as the consequence of the manner in which medical school functions as an institution. Notman et al.,3 for example, view medical school as a highly stress-laden, anxiety-provoking and depression-engendering experience for all students. Mitchell et a1.,4 by contrast, portray medical school as no more stressful or distressing than other graduate or professional training programs, where depressed mood is a natural consequence of the academic difficulties encountered by a subgroup of students. These academic difficulties may be due to a number of factors, including modest ability, poor academic preparation, and compromised functioning due to personal problems or psychiatric illness. On this note, a number of investigators536 argue that the most prevalent psychiatric illnesses in the general population (e.g., major depression) are the major causes of academic failure in medical school. Whatever their differences, these and other investigators share one characteristic in common: they have interpreted their findings to support predetermined assumptions about the etiology of student distress. They have assumed a particular causal relationship between academic performance and depressed mood, without subjecting that assumption to any adequate test. In this report, we will examine the relationship between academic performance and depressed mood in the context of a longitudinal study of a single medical school class. The longitudinal nature of the design allows us to evaluate the causal direction of observed links between academic performance and depressed mood. Our strategy is twofold. First, we will employ a series of structural equation models to test the fit of our data to two hypotheses: that poor grades lead to depression, and that depression leads to poor grades. In this way we will test several common assumptions rather than beginning from them. Second, we will examine the changing levels of depression for subgroups of the class as defined by academic rank to provide a practical context for interpreting the results of the structural equation model. SUBJECTS
AND METHODS
Subjects Subjects were one class of medical students at a Midwestern medical school who agreed to participate in a comprehensive 4-year longitudinal study of adaptation to medical school. One hundred sixteen students (96% of the freshman class) completed the initial battery of questionnaires during school orientation prior to starting the first year. On repeated assessment, 106 students completed questionnaires during April of the first year (88% of the original class); 89 during November of the second year (74%); 95 during April of the second year (79%); 85 during December of the third year (70%); and 91 during December of the fourth year (75%). The six consecutive assessment time points will be referred to as “A, B, C, D, E, and F.” All 121 original members of the class participated in the study at least once over 4 years. The confidentiality and anonymity of each student participant were protected by means of an elaborate coding scheme. Two thirds of the class were men, the mean age was 23.9 years (SD = 4.4), and 12% was married. At graduation, eight students had fallen back a year, seven had dropped out of medical school, and one had died. At this particular medical school, academic regulations require students to sit for the National Board of Medical Examiners (NBME) Part I examination in the spring of their second year. If they fail to pass that exam, their progress into the clinical clerkship year is arrested until the exam is passed. If they cannot pass the NBME-I in three sittings, they are discharged from medical school on academic grounds. For this analysis, we have confined our attention to a subgroup of 52 students who completed the
GRADES AND DEPRESSED MOOD IN MEDICAL
SCHOOL
411
questionnaires at each of four assessments (A. B, D, and E), who completed all their basic science coursework at the central medical center campus rather than at satellite campuses (so grades are directly comparable), and who sat for the NBME-I before assessment E. These four time points were selected for four reasons: (a) they span the first two years of classroom instruction which culminates with the NBME-I; (b) assessments B and D come closest to coinciding with year-end grading, and D comes closest to coinciding with sitting for the NBME-I; (c) assessment C was associated with relatively low participation rates, and thus was excluded; and (d) assessment E provides an index of mood in the months following notification of individual NBME-I scores.
Assessment Variables The Beck Depression Inventory (BDI) ’ is one of the most widely used self-report scales designed to assess the severity of depressed mood. The BDI consists of 21 items, each representing a symptom related to the diagnosis of depression (both cognitive-affective and vegetative symptoms). Subjects respond by rating each symptom item with a score ranging from 0 (absent) to 3 (severe or persistent presence of the symptom). The scale is scored by summing the 21 responses. Beck and Beamesderfe8 indicate that a score >21 identifies a relatively pure group of severely depressed patients, and that a score of 14 or greater should be considered significant for screening purposes. As alternative indicators of mood, we also administered the Rosenberg Self-Esteem Scale, ’ the Eysenck Brief Inventory’ for assessing neuroticism (i.e., liability to anxiety), and a situation-specific confidence scale.” We calculated an undergraduate gradepoint average (IJGPA), a first-year medical school gradepoint average (MGPAI). and a second-year medical school gradepoint average (MGPAZ) for each student from their academic transcripts, on a scale ranging from 0 to 4.0. For the yearly medical school gradepoint averages, each grade was weighted by the proportion of curricular hours devoted to that course. The full Medical College Admissions Test (MCAT) score (i.e., the standard summary score for the component MCAT subtests) accompanying the student’s medical school admission application and the total National Boards Part I score (NB) for each student were obtained from the Dean’s Office.
Structural Equation Analysis Structural equation models (e.g., LISREL)” can estimate causal relationships among variables, based on the observed intercorrelations between these variables. The hypothesized or causal relationships may be represented as a path diagram. A diagram of one hypothesized set relationships between severity of depression and academic performance appears as Fig I, The illustrates a set of linear structural equations represented by the following general equation:
a set of observed of causal diagram
y=By+yx+C where y is a vector of dependent variables, x is a vector of independent variables, @ and y are coefficient matrices, and { is a random vector of residuals.* Independent variables are those that influence the dependent variables (indicated in the diagram by an arrow from the independent to the dependent variable). Dependent variables may also influence one another. Notice that in Fig 1, all the variables are dependent variables except for UGPA. This follows from the fact that we have no data on mood or academic performance collected simultaneous with or earlier in time than the UGPA. The LISREL computer program estimates the model parameters, or pathway coefficients (y, ~~ yj, /3, ~ &, and cl - 1;). and computes a x2 test statistic representing the ability of the specified equations to account for the intercorrelations between the identified variables. The LISREL program also computes a r-statistic (parameter estimate/standard error) for each model parameter, which can be used to test whether the parameter is significantly different from zero. For parameters (or diagrammed pathways)
*More specifically, Fig I illustrates the following equations: BDI-A = y, UGPA + {i BDI-B = yi UGPA + 8, BDI-A + (2 BDI-D = y, UGPA + & BDI-B +
CLARK ET AL
412
Fig 1.
Hypothesized
Structural
Equation Model.
suggested by the data but not hypothesized by the model, the program computes the change in x2 (i.e., in model fit) that would result if each parameter were added to the model. Finally, parameters can be constrained to be equal, and the change x2 associated with this constraint constitutes a test of an equality assumption. Based on these statistics, a variety of hypotheses concerning the influence of one variable, or a group of variables, on others can be tested. The structural equation model we have depicted in Fig I hypothesizes that: (a) BDI scores are influenced directly by UGPA and the most recent BDI score, but not by MCAT, MGPAl, MGPA2, or NB; (b) the effect of UGPA on each of the serial BDI scores is equal or constant, as opposed to time-varying; (c) MGPAI and MGPAZ are influenced by UGPA, MGPAZ is influenced by MGPAI, but BDI scores do not influence MGPAI or MGPAZ; (d) NB scores are directly influenced by MGPAl and MGPA2, but not independently influenced by UGPA or BDI scores; and (e) MCAT scores are not related to any other academic performance or mood variables, and thus do not appear linked to any other variable in Fig 1. The serial influence of each variable on itself is the simplest possible case to assume. The invariant influence of UGPA on serial BDI scores is the simplest way to explain the correlation we (and the equality assumption of observed between UGPA and BDI scores. I2 These relationships UGPA-BDI
influence)
will be explicitly
tested by the model.
RESULTS
Mood Over Time for the Entire Class The mean BDI scores for the entire class at each of the six assessments are listed in Table 1. The lowest depression scores were manifest on the first day of medical school, when students were keen with anticipation. Mean scores ranging from five to six were manifest at the end of the first year, the beginning of the second year, and in the middle of the fourth year (assessments B, C, and F). The highest depression scores were manifest at the end of the second and in the middle of the third year (assessments D and E). Overall, it appears that the spring of the second year of medical school was the most distressing period for the class as a whole.
Fit of the Structural Equation Model Figure 2 illustrates the t-values (path coefficients divided by their standard error) computed by the LISREL program for the hypothesized structural equation model, as depicted in Fig 1. A t-value > 1.96 indicates that the corresponding path coefficients are significantly different from zero at the LY< .05 level for a two-sided test (or t > 1.65 for a one-sided test), and a t-value exceeding 2.58 indicates they are significantly different from zero at the cy < .Ol level for a two-sided test (or t > 2.33 for a one-sided test). Since, in the present case, the direction of any hypothesized
413
GRADES AND DEPRESSED MOOD IN MEDICAL SCHOOL
Table 1. Mean and Range of BDI Scores at Each Assessment Total Sample
Assessment
Total Sample (Mean + SD)
(N)
Subsample of 52 (Mean -r SD)
A: September, first yr
116
3.28
3 4.41
3.38
r 4.61
B: April, first yr
106
6.14
k 6.22
6.73
k 6.28
C: November, second yr
88
5.92
f 6.56
6.17
+ 6.27
D: April, second yr
96
7.86
i
7.43
7.96
+ 7.38
E: December, third yr
85
7.08
i
8.63
7.40
+ 7.84
F: December, fourth yr
91
4.78
+ 5.20
5.15
+ 5.11
effect is dictated by its position in a time sequence, the use of a one-sided test would seem reasonable. Notice that the direction of the effect is provided by the sign of the t-value: a negative sign indicates that higher levels of the independent measure cause lower levels of the dependent measure, and a positive sign indicates that the variables covary in the same direction. All the parameters in Fig 2 (except the path between MGPA2 and NB) differed significantly from zero at the a < .Ol level. The fit of this model represented by the x2 statistic is adequate (P < .239), indicating one cannot reject the hypothesized model, which accounts for the actual data relatively well. The change in xX computed for each possible pathway not hypothesized by the original model suggests that a better-fitting model would result from the addition of five select pathways. The additional parameters improve the already-adequate fit of the model significantly, since the change in x2 resulting from their addition is considerable (Ax’ = 15.08, Q” = 5, P < .Ol). These five additional pathways and their t-values have been added to the model solution in Fig 3. The additional pathways between
.5 05
Model Ftf: x2 = 22.97
Fig 2.
5 05
5 05
df = 19 (P < 0.239)
Fit of Hypothesized
Structural
Equation Model.
CLARK ET AL
414
Model Fit: x2 = 7.89
Fig 3.
df-
14 (f’<
0.995)
Fit of Final Structural
Equation Model.
Beck-A/Beck-E and Beck-B/Beck-E were significant; those between Beck-D/NB and NB/Beck-E approached significance. The interpretation of this final model will now be more fully presented. Serial Relation of Mood Assessments
Serial BDI scores were significantly intercorrelated at Pearson R values ranging from .63 to .77 after the first assessment (between assessment A and the other assessments, r = .26 to .41). The final structural equation model identified significant positive pathways between consecutive BDI assessments, with another between time points A-E and an almost significant one between time points B-E (Fig 3). Thus a student’s BDI score at any single assessment after the first exerted a strong influence on his/her BDI score at the subsequent assessment; and a student’s BDI score at time point E (December of the third year) was shaped by a confluence of all previous mood assessments. Serial Relation of Academic Performance
Measures
MGPAl, MGPA2, and NB scores were significantly intercorrelated at values ranging from .43 to .66. UGPA was significantly correlated with all three measures of academic achievement in medical school at about the same level (r = .40 to .42), but MCAT was not (r = .14 to .27). The final structural equation model identified significant positive pathways between UGPA-MGPAl, UGPA-MGPA2, MGPAlMGPA2, and MGPAl-NB (Fig 3). MGPAl exerted a strong influence on NB, but MGPA2 did not. Variation in UGPA (beyond that influencing MGPAl) did not contribute to NB score differences via a significant pathway; nor was MCAT score related by a significant pathway to any other academic performance measure. Thus a student’s undergraduate gradepoint average exerted a strong influence on his/her first-year medical school gradepoint average, which in turn influenced both second-year gradepoint average and National Boards Part I score. Relation Between Mood and Academic Performance
The final structural equation model suggested three different types of causal relationship between depressed mood and academic performance (Fig 3). First,
GRADES AND DEPRESSED MOOD IN MEDICAL
SCHOOL
415
UGPA had a significant influence on all four BDI assessments that was negative and time-invariant; thus the model affirmed that the assumption of an invariant influence of UGPA on BDI fit adequately, with no need for additional parameters. Second, depressed mood at time point D (i.e., before final exams at the end of the second year) tended to have a negative influence on National Boards Part I scores. Third, National Boards Part I scores tended toward a negative influence on depressed mood at time point E (in December of the third year, on core clerkships). These findings suggest that: (a) better undergraduate academic performance (or ability) contributed to fewer reported depressive symptoms throughout medical school in a manner that did not diminish from one year to the next; (b) a student’s depressed mood as assessed under the considerable pressure of impending secondyear final examinations and Boards Part I may have contributed to lower Boards scores (without affecting the student’s course grades to the same degree); and (c) students who performed less well on Boards tended to report more depressive symptoms in the months following receipt of their Boards scores. Sprci$cir?- of Depressed Mood as an Outcome/lnj?uence The possibility exists that dysphoric mood in general, and not depressed mood in particular, share the relationship with UGPA and NB just described. To examine this possibility, we substituted serial neuroticism, self-esteem, and academic confidence measures (one variable series at a time) for the BDI variable in the structural equation model. No significant relationships emerged between any of the substituted variables, on one hand, and any of the academic performance variables, on the other. Thus the results of the structural equation model analysis as portrayed in Fig 3 appear to apply specifically to depressed mood. Examination of Mean BDl Scores Although the structural equation model provides a clear picture of the covariation between variables, the mathematics of LISREL are abstract. When we turn our attention to the practical implications of these findings, the actual size of the effects is also important to consider. Therefore we examined the mean levels of depression scores over time for student subgroups defined in terms of their class rank on two of the academic performance variables. Figures 4 and 5 graphically display the mean
12 ,
I
Ranked by: IJGM Topthkd -Mtdhttkd Bottom t&d
-
2" 0
A
C
El
D
E
ASSESSMENT Fig 4.
Mean Depression
Score Over Time by UGPA.
1
F
416
CLARK ET AL
Q@
s-
8
7-
$
6-
s B
5-
g n
4-
ifj
2:
< I
3Ranked by: NB
l-
-
Top third
-
Bottom
Middlethkd thid
0 A
0
C
D
E
F
ASSESSMENT Fig 5.
Mean Depression
Score Over Time by NB.
BDI scores over time for the top, middle, and bottom third of the sample when sorted by UGPA and NB respectively. A close examination of these figures reveals several interesting details. First, the invariant causal effect of UGPA on mood seems to be a function of those students in the bottom third of the UGPA distribution (Fig 4). Although BDI scores increased over time for the class as a whole, those with UGPA scores in the bottom third of the sample began medical school more depressed than their peers, and remained more depressed over the entire course of medical school. The pattern for students ranked by MGPAl (not illustrated) is similar to that portrayed in Fig 4, insofar as the bottom third of the class was associated with the highest BDI scores. Interestingly, students in the middle third of the class by UGPA evidenced a pattern of mood change over time that parallels that for the bottom third (i.e., more depression as final exam periods approached), though they remained less depressed than students in the bottom third. It appears that both the bottom and middle UGPA groups respond to the same pressures and demands of medical school with changes in mood, although to different degrees. The top UGPA group appears much less sensitive to the same schedule of pressures and demands. Figure 5, where students are rank-ordered by their performance on NB, shows a different story. Students in the top third of the class maintained a relatively nonvarying mean BDI score over the 4 years of medical school. The middle and bottom groups, by contrast, showed marked elevations of BDI scores from the end of the first year through the third year. The middle group showed a higher depression score than the other two groups on the first day of medical school, and became progressively more depressed until the final year. The bottom third was indistinguishable from the top third on the first day of medical school, but then became progressively more depressed until the third year. These group differences might lead one to hypothesize that students with low BDI scores on the first day of medical school who evidence a sharp increase in depression during the latter half of the first year are at greatest risk for scoring in the lower third of the class on National Boards. Students with relatively high BDI scores on the first day of medical school appear to be at risk for average-range Board scores.
GRADES AND DEPRESSED MOOD IN MEDICAL
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Analvsis of Covariance To examine the relationship between mood and medical school grades from another vantage point, we entered serial BDI scores and medical school gradepoint scores into a multivariate repeated-measures analysis of variance. The analysis yielded an almost-significant relationship between depression scores and academic performance over the first 2 years of medical school (F = 3.03; P = ,057). When UGPA was included in the same analysis as a covariate, however, this association vanished (F = I .36; P = .26). Thus the results of the structural equation model and the analysis of covariance converge to suggest that the mild correlations observed between depressed mood and medical school grades are illusory. The strong influence of undergraduate gradepoint average on both mood and school grades explains the modest association between the latter two. Possible Sampling Bias Our reasons for limiting the sample to a subgroup of 52 students (43% of the class) for the structural equation model analysis were discussed in the Subjects section; but this sampling strategy introduces the possibility that our subgroup of 52 is not representative of the entire class in terms of the variables defining the structural equation model. Therefore we undertook a comparison of the 52 with the remainder of the class on all the variables of interest. The subgroup of 52 was not significantly different from the remainder with respect to mean BDI scores at the first (t = 0.33, 115 u!!, P = NS) or any subsequent assessment. The 52 were not significantly different from the remainder with respect to undergraduate gradepoint average (t = I .38, 92 df, P = NS) or first-year medical school gradepoint average (t = 0.01, 78 dJ P = NS). The 52 performed better than the remainder of the class with respect to full MCAT score (1 = 4.24, I 15 dJ P = .OOOl), second-year medical school gradepoint average (t = 2.04, 116 dJ P = .04), and total NB score (t = 2.17, 113 dj; P = .03). Differences between the subgroup of 52 and the remainder of the class on the academic performance measures are highlighted in Tables 2 and 3. DISCUSSION
Undergraduate gradepoint average exerted two independent but parallel effects on medical students. The first of these effects is not surprising: prior performance in college was a good predictor of performance in medical school, and a far better predictor than MCAT scores. The second effect was unexpected: students who earned higher UGPAs were less likely to report depressive symptoms over 3 years of medical school, and this effect did not diminish with time. Otherwise depressed mood had no significant impact on medical school grades, and an uncertain impact
Table 2.
Comparison
of Subgroups
of 52 Students
and the Remainder
Mean k Standard Deviation Score UGPA Subgroup of 52 Remaining 64
3.38 3.45
k .34 + .24
MCAT
42.6 39.8
k 3.5 f 3.8
MGPAl
2.61 2.61
2 .25 r .23
NB
MGPA2
3.09 3.00
t .26 +_ .23
521.7 478.3
+ 99.4 +_ 11 1.6
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CLARK ET AL
Table 3. Comparison
of Subgroups
of 52 Students
and the Remainder
Distribution of Students by Course Grade Pattern* Honors
Pass
Fail
First Year Subgroup of 52 Remaining 34t
18 (35%) 6 (18%)
21 (40%) 25 (74%)
13 (25%) 3 (9%)
Second Year Subgroup of 52 Remaining 64
13 (25%) 11 (17%)
22 (41%) 33 (52%)
17 (33%) 20 (31%)
*Mutually exclusive categories. Those earning two or more “honors” course grades during the Year, one or more “fail” course grades during the year, and the remainder (“pass”) are categorized into separate groups. TThirty students fulfilled requirements for their first preclinical year at one of two satellite college campuses in a special program, and thus their grades for that year are not comparable.
on National Boards performance, after UGPA was taken into account. MCAT scores correlated positively at low levels (r = .20) with other academic measures, but this did not translate into significant effects for a sample of 52 students. There is no reason to think that the Admissions Committee of the medical school in question relied on MCAT scores for making admissions decisions any less or more than other medical schools. The value of MCAT scores for predicting medical school performance at individual medical schools may be different from that suggested by national studies, as has been noted by others,‘3*‘4 due in part to the restricted variance in MCAT scores inherent to smaller samples. We are left to puzzle: Given the variation in academic standards and grading curves among U.S. colleges, why does an indicator as unstandardized and as remote in time as UGPA have such a persistent and pervasive influence on mood throughout medical school? We believe that the answer lies in the unusually high reliability and validity of UGPA as a measure of academic success, and in the implications of that success for psychological functioning in medical school. UGPA is not a pure measure of ability or drive, although it doubtlessly reflects both. In a narrow sense, it is simply a measure of academic success. It is, however, an unusually good measure of academic success, because it embodies a fundamental principle of psychometrics: the principle of aggregation. Low correlations in psychological research are often imposed by error of measurement resulting from inadequate data sampling. As the number of observations is increased, reliability coefficients also increase to the limit of the reliability of the measure under investigation.15 UGPA is a direct measure of academic success which summarizes numerous evaluations (varying in difficulty and incentive) as made by numerous evaluators (instructors). As such, it is an unusually solid measure, which probably accounts for its superiority over the MCATs in predicting medical school performance.16 An accumulation of successful experiences has subjective consequences for the student as well. A modicum of psychological well-being is necessary to achieve academic success. Those who achieve and who know that they are doing well tend to develop a strong and abiding sense of academic self-confidence which motivates them to work harder and perform better in school.” This sense of self-confidence may also serve as a buffer against depressive reactions to uneven medical school performance.
GRADES AND DEPRESSED MOOD IN MEDICAL
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Students who did less well in college, by contrast, have two obstacles to overcome when they need to reassure themselves during medical school. First, they must develop the requisite skills to succeed academically. Second, they must ward off depressive affects and thoughts, and cannot draw on the recollection of such a successful academic history for comfort. Several authors have demonstrated that the most effective interventions to improve academic performance are those which address these two issues simultaneously.‘7~‘8 In the absence of undergraduate BDI scores, a high UGPA serves as an indirect measure of resistance to depression in our subjects, as well as a predictor of future academic performance. This interpretation is supported by the observation, illustrated in Fig 4, that students in the bottom two thirds of the class as ranked by UGPA show a mood pattern that rises and falls in direct response to the episodic stresses of medical school. Students in the top third appear relatively nonreactive to the same schedule of pressures. The resiliency of medical students with a high UGPA merits further investigation. This interpretation would be bolstered if the structural equation model could be replicated using self-esteem and academic confidence measures in place of the BDI. We attempted, but were unable to replicate our findings in this manner. However, both measures are rather crude indices of subtle psychological states, and we arc unwilling to reject our interpretation of the data on their account at this point in time. The subset of 52 subjects selected for this analysis were representative of the class as a whole by virtue of their BDI scores, undergraduate gradepoint averages, and first-year medical school gradepoint averages. Since the UGPA and MGPAI measures were the major predictors of subsequent academic performance for this sample and the entire class of 121 students, it seems fair to conclude that the sample of 52 (albeit restricted) reflected the mood responses and academic abilities of the class as a whole. Nevertheless, it must be noted that the sample of 52 performed better than the rest of the class on MCATs and NBS, raising a concern that capable students who do not perform well on formal standardized examinations (as opposed to course exams) were underrepresented in our sample. Medical educators, advisers, and medical students should be alert to the possibility that student demoralization or depression during the months preceding Boards exerts a small but detrimental influence on Boards performance. Of equal concern is the possibility that lower Boards scores augment depressed mood. The two effects did not quite reach levels of significance in the context of the structural equation model. We highlight them to draw attention to a potential problem most likely to influence students already mildly depressed at the end of their second year. in a manner calculated to color their third-year learning experiences on core clerkships. Since students who do not perform well on formal standardized exams may have been underrepresented in our analysis, as previously mentioned. it is possible that we have underestimated the magnitude of this problem. REFERENCES I. Clark DC. Clayton PJ, Andreasen NC, et al: Intellectual functioning and abstraction ability m major affective disorders. Compr Psychiatry 26:313-325, 1985 2. Gibbons RD, Davis JM: The price of beer and the salaries of priests: Analysis and display of longitudinal psychiatric data. Arch Gen Psychiatry 4l:l 182-l 184. 1984 3. Notman MT. Salt P, Nadelson CC: Stress and adaptation in medical students: Who is most vulnerable’! Compr Psychiatry 25:355-366. 19X4
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4. Mitchell RE, Matthews JR, Grandy TG, et al: The question of stress among first-year medical students. J Med Educ 58:367-372, 1983 5. Kilpatrick DG, Dublin W, Marcotte DB: Personality, stress of the medical education process, and changes in affective mood state. Psycho1 Rep 34:1215-1223, 1974 6. Waring EM: Psychiatric illness in physicians: A review. Compr Psychiatry 15:519-530, 1974 7. Beck A, Beamesderfer A: Assessment of depression: The Depression Inventory. Mod Probl Pharmacopsychiatry 7:151-169, 1974 8. Rosenberg M: Society and Adolescent Self Image. Princeton, NJ, Princeton University Press, 1965 9. Eysenck S, Eysenck H: An improved short questionnaire for the measurement of extraversion and neuroticism. Life Sci 3:1103-l 109, 1964 10. Zeldow PB, Clark DC, Daugherty SR: Masculinity, femininity, type A behavior and psychosocial adjustment in medical students. J Pers Sot Psycho1 48:481-492, 1985 Il. Joreskog KG, Sorbom D: LISREL-V Users’ Guide. Chicago, National Educational Resources, 1981 12. Shingles RD: Causal inference in cross-lagged panel analysis, in Blalock HM (ed): Causal Models in Panel and Experimental Designs. New York, Aldine, 1985, pp 219-249 13. Erdmann JB: The Medical College Admission Test and the selection of medical students. N Engl J Med 310:386-389, 1984 14. Nowacek GA, Pullen E, Short J, et al: Validity of MCAT scores as predictors of preclinical grades and NBME Part I examination scores. J Med Educ 62:989-991, 1987 15. Epstein S: The stability of behavior: I. On predicting most of the people much of the time. J Pers Sot Psycho1 37:1097-l 126, 1979 16. Grover PL, Smith DU: Academic anxiety, locus of control, and achievement in medical school. J Med Educ 561727-736, 1981 17. Felson RB: The effect of self-appraisals of ability on academic performance. J Pers Sot Psycho1 47~944-952, 1984 18. Wilson TD, Linville PW: Improving the performance of college freshmen with attributional techniques. J Pers Sot Psycho1 49:287-293, 1985
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HAT IS THE LINK between academic performance and mood for medical students? Do poor grades make students depressed? Does depression cause students to get lower grades? Intuitively, we might assume both are true. Medical students emerge from high school and college with gradepoint averages which place them in the top fractiles of their respective classes, so that most find themselves in the middle of a class distribution for the first time during medical school, where occasional poor grades or course failures are not uncommon. Most of us have felt the pangs of doubt or depressed mood which accompany substandard performance at one time or another. And certainly severe depression impairs cognitive performance.’ Yet beyond these intuitions, the link between grades and depression is far from clear. Evidence of a correlation between academic performance and mood does not explain how they are linked. If depressed mood and diminished academic performance are correlated, the correlation may be due to some causal relationship between them, or it may not. Correlation does not necessarily imply causality. The correlation may be due to some third, unknown causal agent, or be spurious.2
From the Department of Psychiatry, Rush Medical College, Chicago. This research is part of the Rush Longitudinal Study, supported by the National Fund for Mudicul Education (Burroughs Wellcome Fund and Ameritech Foundation). the Chicago Community Tru.st. and the American Medical Association Education and Research Foundation. Principal Investigators include Jefiey Salloway, Linda Leksas, Edward Eckenfels. and Phyllis Blumberg, who participated equally in study design. data collection, and the early stages ojanalysis. Address reprint requests to David C. Clark, Ph.D., Department of Psvchiatry, Rush Medical College, 1720 West Polk St., Chicago, IL 60612. 0 1988 by Grune & Stratton, Inc. OOIO-440.%‘/88/2904/0008$03.00/0 Comprehensive
Psychiatry,
Vol. 29,
No. 4 (July/August),
1988:
pp 409-420
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Previous studies in medical education have been influenced by a priori assumptions about the nature and sources of stress for medical students. Student distress has sometimes been viewed as the result of failings of the student, and sometimes as the consequence of the manner in which medical school functions as an institution. Notman et al.,3 for example, view medical school as a highly stress-laden, anxiety-provoking and depression-engendering experience for all students. Mitchell et a1.,4 by contrast, portray medical school as no more stressful or distressing than other graduate or professional training programs, where depressed mood is a natural consequence of the academic difficulties encountered by a subgroup of students. These academic difficulties may be due to a number of factors, including modest ability, poor academic preparation, and compromised functioning due to personal problems or psychiatric illness. On this note, a number of investigators536 argue that the most prevalent psychiatric illnesses in the general population (e.g., major depression) are the major causes of academic failure in medical school. Whatever their differences, these and other investigators share one characteristic in common: they have interpreted their findings to support predetermined assumptions about the etiology of student distress. They have assumed a particular causal relationship between academic performance and depressed mood, without subjecting that assumption to any adequate test. In this report, we will examine the relationship between academic performance and depressed mood in the context of a longitudinal study of a single medical school class. The longitudinal nature of the design allows us to evaluate the causal direction of observed links between academic performance and depressed mood. Our strategy is twofold. First, we will employ a series of structural equation models to test the fit of our data to two hypotheses: that poor grades lead to depression, and that depression leads to poor grades. In this way we will test several common assumptions rather than beginning from them. Second, we will examine the changing levels of depression for subgroups of the class as defined by academic rank to provide a practical context for interpreting the results of the structural equation model. SUBJECTS
AND METHODS
Subjects Subjects were one class of medical students at a Midwestern medical school who agreed to participate in a comprehensive 4-year longitudinal study of adaptation to medical school. One hundred sixteen students (96% of the freshman class) completed the initial battery of questionnaires during school orientation prior to starting the first year. On repeated assessment, 106 students completed questionnaires during April of the first year (88% of the original class); 89 during November of the second year (74%); 95 during April of the second year (79%); 85 during December of the third year (70%); and 91 during December of the fourth year (75%). The six consecutive assessment time points will be referred to as “A, B, C, D, E, and F.” All 121 original members of the class participated in the study at least once over 4 years. The confidentiality and anonymity of each student participant were protected by means of an elaborate coding scheme. Two thirds of the class were men, the mean age was 23.9 years (SD = 4.4), and 12% was married. At graduation, eight students had fallen back a year, seven had dropped out of medical school, and one had died. At this particular medical school, academic regulations require students to sit for the National Board of Medical Examiners (NBME) Part I examination in the spring of their second year. If they fail to pass that exam, their progress into the clinical clerkship year is arrested until the exam is passed. If they cannot pass the NBME-I in three sittings, they are discharged from medical school on academic grounds. For this analysis, we have confined our attention to a subgroup of 52 students who completed the
GRADES AND DEPRESSED MOOD IN MEDICAL
SCHOOL
411
questionnaires at each of four assessments (A. B, D, and E), who completed all their basic science coursework at the central medical center campus rather than at satellite campuses (so grades are directly comparable), and who sat for the NBME-I before assessment E. These four time points were selected for four reasons: (a) they span the first two years of classroom instruction which culminates with the NBME-I; (b) assessments B and D come closest to coinciding with year-end grading, and D comes closest to coinciding with sitting for the NBME-I; (c) assessment C was associated with relatively low participation rates, and thus was excluded; and (d) assessment E provides an index of mood in the months following notification of individual NBME-I scores.
Assessment Variables The Beck Depression Inventory (BDI) ’ is one of the most widely used self-report scales designed to assess the severity of depressed mood. The BDI consists of 21 items, each representing a symptom related to the diagnosis of depression (both cognitive-affective and vegetative symptoms). Subjects respond by rating each symptom item with a score ranging from 0 (absent) to 3 (severe or persistent presence of the symptom). The scale is scored by summing the 21 responses. Beck and Beamesderfe8 indicate that a score >21 identifies a relatively pure group of severely depressed patients, and that a score of 14 or greater should be considered significant for screening purposes. As alternative indicators of mood, we also administered the Rosenberg Self-Esteem Scale, ’ the Eysenck Brief Inventory’ for assessing neuroticism (i.e., liability to anxiety), and a situation-specific confidence scale.” We calculated an undergraduate gradepoint average (IJGPA), a first-year medical school gradepoint average (MGPAI). and a second-year medical school gradepoint average (MGPAZ) for each student from their academic transcripts, on a scale ranging from 0 to 4.0. For the yearly medical school gradepoint averages, each grade was weighted by the proportion of curricular hours devoted to that course. The full Medical College Admissions Test (MCAT) score (i.e., the standard summary score for the component MCAT subtests) accompanying the student’s medical school admission application and the total National Boards Part I score (NB) for each student were obtained from the Dean’s Office.
Structural Equation Analysis Structural equation models (e.g., LISREL)” can estimate causal relationships among variables, based on the observed intercorrelations between these variables. The hypothesized or causal relationships may be represented as a path diagram. A diagram of one hypothesized set relationships between severity of depression and academic performance appears as Fig I, The illustrates a set of linear structural equations represented by the following general equation:
a set of observed of causal diagram
y=By+yx+C where y is a vector of dependent variables, x is a vector of independent variables, @ and y are coefficient matrices, and { is a random vector of residuals.* Independent variables are those that influence the dependent variables (indicated in the diagram by an arrow from the independent to the dependent variable). Dependent variables may also influence one another. Notice that in Fig 1, all the variables are dependent variables except for UGPA. This follows from the fact that we have no data on mood or academic performance collected simultaneous with or earlier in time than the UGPA. The LISREL computer program estimates the model parameters, or pathway coefficients (y, ~~ yj, /3, ~ &, and cl - 1;). and computes a x2 test statistic representing the ability of the specified equations to account for the intercorrelations between the identified variables. The LISREL program also computes a r-statistic (parameter estimate/standard error) for each model parameter, which can be used to test whether the parameter is significantly different from zero. For parameters (or diagrammed pathways)
*More specifically, Fig I illustrates the following equations: BDI-A = y, UGPA + {i BDI-B = yi UGPA + 8, BDI-A + (2 BDI-D = y, UGPA + & BDI-B +
CLARK ET AL
412
Fig 1.
Hypothesized
Structural
Equation Model.
suggested by the data but not hypothesized by the model, the program computes the change in x2 (i.e., in model fit) that would result if each parameter were added to the model. Finally, parameters can be constrained to be equal, and the change x2 associated with this constraint constitutes a test of an equality assumption. Based on these statistics, a variety of hypotheses concerning the influence of one variable, or a group of variables, on others can be tested. The structural equation model we have depicted in Fig I hypothesizes that: (a) BDI scores are influenced directly by UGPA and the most recent BDI score, but not by MCAT, MGPAl, MGPA2, or NB; (b) the effect of UGPA on each of the serial BDI scores is equal or constant, as opposed to time-varying; (c) MGPAI and MGPAZ are influenced by UGPA, MGPAZ is influenced by MGPAI, but BDI scores do not influence MGPAI or MGPAZ; (d) NB scores are directly influenced by MGPAl and MGPA2, but not independently influenced by UGPA or BDI scores; and (e) MCAT scores are not related to any other academic performance or mood variables, and thus do not appear linked to any other variable in Fig 1. The serial influence of each variable on itself is the simplest possible case to assume. The invariant influence of UGPA on serial BDI scores is the simplest way to explain the correlation we (and the equality assumption of observed between UGPA and BDI scores. I2 These relationships UGPA-BDI
influence)
will be explicitly
tested by the model.
RESULTS
Mood Over Time for the Entire Class The mean BDI scores for the entire class at each of the six assessments are listed in Table 1. The lowest depression scores were manifest on the first day of medical school, when students were keen with anticipation. Mean scores ranging from five to six were manifest at the end of the first year, the beginning of the second year, and in the middle of the fourth year (assessments B, C, and F). The highest depression scores were manifest at the end of the second and in the middle of the third year (assessments D and E). Overall, it appears that the spring of the second year of medical school was the most distressing period for the class as a whole.
Fit of the Structural Equation Model Figure 2 illustrates the t-values (path coefficients divided by their standard error) computed by the LISREL program for the hypothesized structural equation model, as depicted in Fig 1. A t-value > 1.96 indicates that the corresponding path coefficients are significantly different from zero at the LY< .05 level for a two-sided test (or t > 1.65 for a one-sided test), and a t-value exceeding 2.58 indicates they are significantly different from zero at the cy < .Ol level for a two-sided test (or t > 2.33 for a one-sided test). Since, in the present case, the direction of any hypothesized
413
GRADES AND DEPRESSED MOOD IN MEDICAL SCHOOL
Table 1. Mean and Range of BDI Scores at Each Assessment Total Sample
Assessment
Total Sample (Mean + SD)
(N)
Subsample of 52 (Mean -r SD)
A: September, first yr
116
3.28
3 4.41
3.38
r 4.61
B: April, first yr
106
6.14
k 6.22
6.73
k 6.28
C: November, second yr
88
5.92
f 6.56
6.17
+ 6.27
D: April, second yr
96
7.86
i
7.43
7.96
+ 7.38
E: December, third yr
85
7.08
i
8.63
7.40
+ 7.84
F: December, fourth yr
91
4.78
+ 5.20
5.15
+ 5.11
effect is dictated by its position in a time sequence, the use of a one-sided test would seem reasonable. Notice that the direction of the effect is provided by the sign of the t-value: a negative sign indicates that higher levels of the independent measure cause lower levels of the dependent measure, and a positive sign indicates that the variables covary in the same direction. All the parameters in Fig 2 (except the path between MGPA2 and NB) differed significantly from zero at the a < .Ol level. The fit of this model represented by the x2 statistic is adequate (P < .239), indicating one cannot reject the hypothesized model, which accounts for the actual data relatively well. The change in xX computed for each possible pathway not hypothesized by the original model suggests that a better-fitting model would result from the addition of five select pathways. The additional parameters improve the already-adequate fit of the model significantly, since the change in x2 resulting from their addition is considerable (Ax’ = 15.08, Q” = 5, P < .Ol). These five additional pathways and their t-values have been added to the model solution in Fig 3. The additional pathways between
.5 05
Model Ftf: x2 = 22.97
Fig 2.
5 05
5 05
df = 19 (P < 0.239)
Fit of Hypothesized
Structural
Equation Model.
CLARK ET AL
414
Model Fit: x2 = 7.89
Fig 3.
df-
14 (f’<
0.995)
Fit of Final Structural
Equation Model.
Beck-A/Beck-E and Beck-B/Beck-E were significant; those between Beck-D/NB and NB/Beck-E approached significance. The interpretation of this final model will now be more fully presented. Serial Relation of Mood Assessments
Serial BDI scores were significantly intercorrelated at Pearson R values ranging from .63 to .77 after the first assessment (between assessment A and the other assessments, r = .26 to .41). The final structural equation model identified significant positive pathways between consecutive BDI assessments, with another between time points A-E and an almost significant one between time points B-E (Fig 3). Thus a student’s BDI score at any single assessment after the first exerted a strong influence on his/her BDI score at the subsequent assessment; and a student’s BDI score at time point E (December of the third year) was shaped by a confluence of all previous mood assessments. Serial Relation of Academic Performance
Measures
MGPAl, MGPA2, and NB scores were significantly intercorrelated at values ranging from .43 to .66. UGPA was significantly correlated with all three measures of academic achievement in medical school at about the same level (r = .40 to .42), but MCAT was not (r = .14 to .27). The final structural equation model identified significant positive pathways between UGPA-MGPAl, UGPA-MGPA2, MGPAlMGPA2, and MGPAl-NB (Fig 3). MGPAl exerted a strong influence on NB, but MGPA2 did not. Variation in UGPA (beyond that influencing MGPAl) did not contribute to NB score differences via a significant pathway; nor was MCAT score related by a significant pathway to any other academic performance measure. Thus a student’s undergraduate gradepoint average exerted a strong influence on his/her first-year medical school gradepoint average, which in turn influenced both second-year gradepoint average and National Boards Part I score. Relation Between Mood and Academic Performance
The final structural equation model suggested three different types of causal relationship between depressed mood and academic performance (Fig 3). First,
GRADES AND DEPRESSED MOOD IN MEDICAL
SCHOOL
415
UGPA had a significant influence on all four BDI assessments that was negative and time-invariant; thus the model affirmed that the assumption of an invariant influence of UGPA on BDI fit adequately, with no need for additional parameters. Second, depressed mood at time point D (i.e., before final exams at the end of the second year) tended to have a negative influence on National Boards Part I scores. Third, National Boards Part I scores tended toward a negative influence on depressed mood at time point E (in December of the third year, on core clerkships). These findings suggest that: (a) better undergraduate academic performance (or ability) contributed to fewer reported depressive symptoms throughout medical school in a manner that did not diminish from one year to the next; (b) a student’s depressed mood as assessed under the considerable pressure of impending secondyear final examinations and Boards Part I may have contributed to lower Boards scores (without affecting the student’s course grades to the same degree); and (c) students who performed less well on Boards tended to report more depressive symptoms in the months following receipt of their Boards scores. Sprci$cir?- of Depressed Mood as an Outcome/lnj?uence The possibility exists that dysphoric mood in general, and not depressed mood in particular, share the relationship with UGPA and NB just described. To examine this possibility, we substituted serial neuroticism, self-esteem, and academic confidence measures (one variable series at a time) for the BDI variable in the structural equation model. No significant relationships emerged between any of the substituted variables, on one hand, and any of the academic performance variables, on the other. Thus the results of the structural equation model analysis as portrayed in Fig 3 appear to apply specifically to depressed mood. Examination of Mean BDl Scores Although the structural equation model provides a clear picture of the covariation between variables, the mathematics of LISREL are abstract. When we turn our attention to the practical implications of these findings, the actual size of the effects is also important to consider. Therefore we examined the mean levels of depression scores over time for student subgroups defined in terms of their class rank on two of the academic performance variables. Figures 4 and 5 graphically display the mean
12 ,
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Ranked by: IJGM Topthkd -Mtdhttkd Bottom t&d
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ASSESSMENT Fig 4.
Mean Depression
Score Over Time by UGPA.
1
F
416
CLARK ET AL
Q@
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8
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ASSESSMENT Fig 5.
Mean Depression
Score Over Time by NB.
BDI scores over time for the top, middle, and bottom third of the sample when sorted by UGPA and NB respectively. A close examination of these figures reveals several interesting details. First, the invariant causal effect of UGPA on mood seems to be a function of those students in the bottom third of the UGPA distribution (Fig 4). Although BDI scores increased over time for the class as a whole, those with UGPA scores in the bottom third of the sample began medical school more depressed than their peers, and remained more depressed over the entire course of medical school. The pattern for students ranked by MGPAl (not illustrated) is similar to that portrayed in Fig 4, insofar as the bottom third of the class was associated with the highest BDI scores. Interestingly, students in the middle third of the class by UGPA evidenced a pattern of mood change over time that parallels that for the bottom third (i.e., more depression as final exam periods approached), though they remained less depressed than students in the bottom third. It appears that both the bottom and middle UGPA groups respond to the same pressures and demands of medical school with changes in mood, although to different degrees. The top UGPA group appears much less sensitive to the same schedule of pressures and demands. Figure 5, where students are rank-ordered by their performance on NB, shows a different story. Students in the top third of the class maintained a relatively nonvarying mean BDI score over the 4 years of medical school. The middle and bottom groups, by contrast, showed marked elevations of BDI scores from the end of the first year through the third year. The middle group showed a higher depression score than the other two groups on the first day of medical school, and became progressively more depressed until the final year. The bottom third was indistinguishable from the top third on the first day of medical school, but then became progressively more depressed until the third year. These group differences might lead one to hypothesize that students with low BDI scores on the first day of medical school who evidence a sharp increase in depression during the latter half of the first year are at greatest risk for scoring in the lower third of the class on National Boards. Students with relatively high BDI scores on the first day of medical school appear to be at risk for average-range Board scores.
GRADES AND DEPRESSED MOOD IN MEDICAL
SCHOOL
417
Analvsis of Covariance To examine the relationship between mood and medical school grades from another vantage point, we entered serial BDI scores and medical school gradepoint scores into a multivariate repeated-measures analysis of variance. The analysis yielded an almost-significant relationship between depression scores and academic performance over the first 2 years of medical school (F = 3.03; P = ,057). When UGPA was included in the same analysis as a covariate, however, this association vanished (F = I .36; P = .26). Thus the results of the structural equation model and the analysis of covariance converge to suggest that the mild correlations observed between depressed mood and medical school grades are illusory. The strong influence of undergraduate gradepoint average on both mood and school grades explains the modest association between the latter two. Possible Sampling Bias Our reasons for limiting the sample to a subgroup of 52 students (43% of the class) for the structural equation model analysis were discussed in the Subjects section; but this sampling strategy introduces the possibility that our subgroup of 52 is not representative of the entire class in terms of the variables defining the structural equation model. Therefore we undertook a comparison of the 52 with the remainder of the class on all the variables of interest. The subgroup of 52 was not significantly different from the remainder with respect to mean BDI scores at the first (t = 0.33, 115 u!!, P = NS) or any subsequent assessment. The 52 were not significantly different from the remainder with respect to undergraduate gradepoint average (t = I .38, 92 df, P = NS) or first-year medical school gradepoint average (t = 0.01, 78 dJ P = NS). The 52 performed better than the remainder of the class with respect to full MCAT score (1 = 4.24, I 15 dJ P = .OOOl), second-year medical school gradepoint average (t = 2.04, 116 dJ P = .04), and total NB score (t = 2.17, 113 dj; P = .03). Differences between the subgroup of 52 and the remainder of the class on the academic performance measures are highlighted in Tables 2 and 3. DISCUSSION
Undergraduate gradepoint average exerted two independent but parallel effects on medical students. The first of these effects is not surprising: prior performance in college was a good predictor of performance in medical school, and a far better predictor than MCAT scores. The second effect was unexpected: students who earned higher UGPAs were less likely to report depressive symptoms over 3 years of medical school, and this effect did not diminish with time. Otherwise depressed mood had no significant impact on medical school grades, and an uncertain impact
Table 2.
Comparison
of Subgroups
of 52 Students
and the Remainder
Mean k Standard Deviation Score UGPA Subgroup of 52 Remaining 64
3.38 3.45
k .34 + .24
MCAT
42.6 39.8
k 3.5 f 3.8
MGPAl
2.61 2.61
2 .25 r .23
NB
MGPA2
3.09 3.00
t .26 +_ .23
521.7 478.3
+ 99.4 +_ 11 1.6
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CLARK ET AL
Table 3. Comparison
of Subgroups
of 52 Students
and the Remainder
Distribution of Students by Course Grade Pattern* Honors
Pass
Fail
First Year Subgroup of 52 Remaining 34t
18 (35%) 6 (18%)
21 (40%) 25 (74%)
13 (25%) 3 (9%)
Second Year Subgroup of 52 Remaining 64
13 (25%) 11 (17%)
22 (41%) 33 (52%)
17 (33%) 20 (31%)
*Mutually exclusive categories. Those earning two or more “honors” course grades during the Year, one or more “fail” course grades during the year, and the remainder (“pass”) are categorized into separate groups. TThirty students fulfilled requirements for their first preclinical year at one of two satellite college campuses in a special program, and thus their grades for that year are not comparable.
on National Boards performance, after UGPA was taken into account. MCAT scores correlated positively at low levels (r = .20) with other academic measures, but this did not translate into significant effects for a sample of 52 students. There is no reason to think that the Admissions Committee of the medical school in question relied on MCAT scores for making admissions decisions any less or more than other medical schools. The value of MCAT scores for predicting medical school performance at individual medical schools may be different from that suggested by national studies, as has been noted by others,‘3*‘4 due in part to the restricted variance in MCAT scores inherent to smaller samples. We are left to puzzle: Given the variation in academic standards and grading curves among U.S. colleges, why does an indicator as unstandardized and as remote in time as UGPA have such a persistent and pervasive influence on mood throughout medical school? We believe that the answer lies in the unusually high reliability and validity of UGPA as a measure of academic success, and in the implications of that success for psychological functioning in medical school. UGPA is not a pure measure of ability or drive, although it doubtlessly reflects both. In a narrow sense, it is simply a measure of academic success. It is, however, an unusually good measure of academic success, because it embodies a fundamental principle of psychometrics: the principle of aggregation. Low correlations in psychological research are often imposed by error of measurement resulting from inadequate data sampling. As the number of observations is increased, reliability coefficients also increase to the limit of the reliability of the measure under investigation.15 UGPA is a direct measure of academic success which summarizes numerous evaluations (varying in difficulty and incentive) as made by numerous evaluators (instructors). As such, it is an unusually solid measure, which probably accounts for its superiority over the MCATs in predicting medical school performance.16 An accumulation of successful experiences has subjective consequences for the student as well. A modicum of psychological well-being is necessary to achieve academic success. Those who achieve and who know that they are doing well tend to develop a strong and abiding sense of academic self-confidence which motivates them to work harder and perform better in school.” This sense of self-confidence may also serve as a buffer against depressive reactions to uneven medical school performance.
GRADES AND DEPRESSED MOOD IN MEDICAL
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Students who did less well in college, by contrast, have two obstacles to overcome when they need to reassure themselves during medical school. First, they must develop the requisite skills to succeed academically. Second, they must ward off depressive affects and thoughts, and cannot draw on the recollection of such a successful academic history for comfort. Several authors have demonstrated that the most effective interventions to improve academic performance are those which address these two issues simultaneously.‘7~‘8 In the absence of undergraduate BDI scores, a high UGPA serves as an indirect measure of resistance to depression in our subjects, as well as a predictor of future academic performance. This interpretation is supported by the observation, illustrated in Fig 4, that students in the bottom two thirds of the class as ranked by UGPA show a mood pattern that rises and falls in direct response to the episodic stresses of medical school. Students in the top third appear relatively nonreactive to the same schedule of pressures. The resiliency of medical students with a high UGPA merits further investigation. This interpretation would be bolstered if the structural equation model could be replicated using self-esteem and academic confidence measures in place of the BDI. We attempted, but were unable to replicate our findings in this manner. However, both measures are rather crude indices of subtle psychological states, and we arc unwilling to reject our interpretation of the data on their account at this point in time. The subset of 52 subjects selected for this analysis were representative of the class as a whole by virtue of their BDI scores, undergraduate gradepoint averages, and first-year medical school gradepoint averages. Since the UGPA and MGPAI measures were the major predictors of subsequent academic performance for this sample and the entire class of 121 students, it seems fair to conclude that the sample of 52 (albeit restricted) reflected the mood responses and academic abilities of the class as a whole. Nevertheless, it must be noted that the sample of 52 performed better than the rest of the class on MCATs and NBS, raising a concern that capable students who do not perform well on formal standardized examinations (as opposed to course exams) were underrepresented in our sample. Medical educators, advisers, and medical students should be alert to the possibility that student demoralization or depression during the months preceding Boards exerts a small but detrimental influence on Boards performance. Of equal concern is the possibility that lower Boards scores augment depressed mood. The two effects did not quite reach levels of significance in the context of the structural equation model. We highlight them to draw attention to a potential problem most likely to influence students already mildly depressed at the end of their second year. in a manner calculated to color their third-year learning experiences on core clerkships. Since students who do not perform well on formal standardized exams may have been underrepresented in our analysis, as previously mentioned. it is possible that we have underestimated the magnitude of this problem. REFERENCES I. Clark DC. Clayton PJ, Andreasen NC, et al: Intellectual functioning and abstraction ability m major affective disorders. Compr Psychiatry 26:313-325, 1985 2. Gibbons RD, Davis JM: The price of beer and the salaries of priests: Analysis and display of longitudinal psychiatric data. Arch Gen Psychiatry 4l:l 182-l 184. 1984 3. Notman MT. Salt P, Nadelson CC: Stress and adaptation in medical students: Who is most vulnerable’! Compr Psychiatry 25:355-366. 19X4
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4. Mitchell RE, Matthews JR, Grandy TG, et al: The question of stress among first-year medical students. J Med Educ 58:367-372, 1983 5. Kilpatrick DG, Dublin W, Marcotte DB: Personality, stress of the medical education process, and changes in affective mood state. Psycho1 Rep 34:1215-1223, 1974 6. Waring EM: Psychiatric illness in physicians: A review. Compr Psychiatry 15:519-530, 1974 7. Beck A, Beamesderfer A: Assessment of depression: The Depression Inventory. Mod Probl Pharmacopsychiatry 7:151-169, 1974 8. Rosenberg M: Society and Adolescent Self Image. Princeton, NJ, Princeton University Press, 1965 9. Eysenck S, Eysenck H: An improved short questionnaire for the measurement of extraversion and neuroticism. Life Sci 3:1103-l 109, 1964 10. Zeldow PB, Clark DC, Daugherty SR: Masculinity, femininity, type A behavior and psychosocial adjustment in medical students. J Pers Sot Psycho1 48:481-492, 1985 Il. Joreskog KG, Sorbom D: LISREL-V Users’ Guide. Chicago, National Educational Resources, 1981 12. Shingles RD: Causal inference in cross-lagged panel analysis, in Blalock HM (ed): Causal Models in Panel and Experimental Designs. New York, Aldine, 1985, pp 219-249 13. Erdmann JB: The Medical College Admission Test and the selection of medical students. N Engl J Med 310:386-389, 1984 14. Nowacek GA, Pullen E, Short J, et al: Validity of MCAT scores as predictors of preclinical grades and NBME Part I examination scores. J Med Educ 62:989-991, 1987 15. Epstein S: The stability of behavior: I. On predicting most of the people much of the time. J Pers Sot Psycho1 37:1097-l 126, 1979 16. Grover PL, Smith DU: Academic anxiety, locus of control, and achievement in medical school. J Med Educ 561727-736, 1981 17. Felson RB: The effect of self-appraisals of ability on academic performance. J Pers Sot Psycho1 47~944-952, 1984 18. Wilson TD, Linville PW: Improving the performance of college freshmen with attributional techniques. J Pers Sot Psycho1 49:287-293, 1985