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THE PROCESS AND ACCURACY OF CLINICAL JUDGEMENT:Comparison Studies

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LESSON 23
THE PROCESS AND ACCURACY OF CLINICAL JUDGEMENT
As scientific and objective as clinical psychology has tried to become, it is still virtually impossible to
evaluate its diagnostic and assessment techniques 'apart from the clinician involved. Clinical
Judgment," is enough to suggest that clinicians use inferential processes that are often far from
objective. The process, accuracy, and communication of clinical judgment are still very often extremely
personalized phenomena.
In the discussion ahead we will examine some of the means by which the clinicians put together
assessment data and arrive at particular conclusion. We will also discuss the accuracy of clinical
judgment and impressions.
PROCESS AND ACCURACY
The discussion of clinical judgment will begin with its basic element----Interpretation.
INTERPRETATION
It is hard to disagree with L. H. Levy's (1963) statement that "Interpretation is the most important single
activity engaged in by the clinician". Interpretation is an inferential process (Nisbett & Ross, 1980)
that takes where assessment leaves off. The interviews have been completed; the psychological tests have
been administered. Now, what does it all mean, and what decisions are to be made?
At the very least, clinical interpretation or judgment is a complex process. It involves
1. Stimuli----an MMPI-2 profile, an IQ score, a gesture, a sound, etc.
2. It also involves the clinician's response. "Is this patient psychotic?" "Is the patient's behavior
expressive of a low expectancy for success?" Or even "What is the patient like?"
3. It also involves the characteristics of clinicians their cognitive structures and theoretical orientations.
4. Finally, situational variables enter into the process. These can include everything from the type and
range of patients to the constraints that the demands of the setting place on prediction$. For
example, a clinician in a university mental health center may make a range of judgments from
hospitalization to psychotherapy to just dropping out of school---whereas a clinician in a prison
setting may be limited to many fewer options.
THE THEORETICAL FRAMEWORK
As we know that clinical psychologists strive to discover the etiology, or origins, of psychological
problems and to understand patients so that they can be helped. Clinical problems can be
conceptualized in a variety of ways (for example, psychodynamic, behavioral, and cognitive). The kinds
of interpretations made by a Freudian are vastly different from t h o s e m a d e by a behavioral clinician.
Two clinicians may each observe t h a t a chiid persistently attempts to sleep in his mother's bed.
For the Freudians, this becomes a sign of an unresolved Oedipus complex. For the behaviorist, the
interpretation may be in terms of reinforcement.
Indeed, one way in which clinicians can evaluate interpretations is by examining their consistency with
the theory from which they are derived. The number of interpretations that can be made from a set of
observations, interview responses, or test data is both awesome and bewildering. By adopting a
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particular theoretical perspective, clinicians can evaluate interpretations and inferences according to
their theoretical consistency and can also generate additional hypotheses.
SIGNS, SAMPLES AND CORRELATES
Patient data can be viewed in several ways.
1. Samples: First, one can view such data as samples. Observations, test scores, test responses, or
other data are seen as samples of a larger pool of information that could be obtained outside the
consulting room. For example, when a patient does poorly on the Wechsler Memory
Scale, this could be regarded as a sample of nontest behavior (memory problems).
2. Signs: A second way in which patient data can be interpreted is as signs of some underlying
state, condition, or determinant. Aside from radical behaviorists, many clinicians will seek to
infer from observations of the patient's behavior and test responses a variety of underlying
determinants. For some clinicians, the underlying determinant might be anxiety, for others, ego
strength; and for still others, expectancies. But in every case, the observation is seen as something that
signifies underlying determinants. For example, poor on a patient's Rorschach responses is often
interpreted as a sign of poor reality testing (psychosis).
3. Correlates: A third view of patient data emphasizes their status as correlates of other things. Once
the anxious behavior, the flat affect, or the inability to concentrate have been noted in a depressed
patient, the clinician might predict an associated decline in sexual activity, in social relationships,
in willingness to seek employment, and so on. In effect, then, assessment data can be inter-
preted to suggest behavioral, attitudinal or emotional correlates.
LEVELS OF INTERPRETATION
Whether clinicians view clinical data as samples, signs, or correlates, they are making inferences that will
enable them to go from those clinical data to recommendations, reports or predictions. Sundberg, Tyler,
and Taplin (1973) have described three levels of inferences or interpretations.
LEVEL 1 interpretation generally involves little in the way of inference and certainly nothing in the way
of a sign approach. From input to output, there are practically no intervening steps. For example, if it
is known on the basis of past experience that students who sit in the front row of a class almost always get A's
or B's, then clinicians can go directly from seat number to grade prediction without any necessity for
intervening attributions of intelligence scores, previous courses, and so on. This simple yet efficient ap-
proach can dispense with high-level clinicians who make exotic inferences prior to their predictions;
it can be handled by technicians, computers, or machines. Level I interpretations can often be used with
large populations if the prime purpose is screening and if predicting the outcome for a specific person is
relatively unimportant. A college entrance exam is a case in point. Here a single test score may predict
with considerable accuracy the academic performance of 1,000 freshmen. Although that single score may be
erroneous as a predictor for student X, a certain degree of error can easily be tolerated if one is interested-
primarily in the number who is likely to graduate.
LEVEL II interpretations involve two kinds' inferences. The clinician may observe a patient and then
conclude that the observe behavior generally characterizes the patient. Sundberg etal call this first kind of
inference descriptive generalization-----still at the descriptive level. Thus, for a patient who fidgets,
smokes cigarettes during the interview, and stammers the clinician may make a descriptive generalization---
-interview tension. If it turns out later that the patient has trouble relaxing at home, cannot sit through the
meeting at the office. And is very worried about paying off the mortage, the clinician may go to a broader
descriptive generalization. The second kind of inference is a hypothetical construct that suggests an inner state
and takes the clinician a bit beyond descriptive generalaization.When clinicians begin to make generalization
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and particularly, to impute inner determinants to the patient, they are moving directly to clinical interpretation
as it is often used.
LEVEL III interpretations take clinicians beyond level II primarily by being more inclusive and better
integrated. at this level, they attempt to achieve a consistent, broad understanding of the `individual in
situation', clinician will draw and integrated picture of the patient's developmental, social, and
psychological determinants that involves a highly articulated theoretical system of hypothesis and
deductions. For example, a preponderance of `blood' responses on the Rorschach might be interpreted as a
sign of underlying aggression that may lead to future impulsive outbursts or loss of control.
THEORY AND INTERPRETATION
Currently, clinicians may be assigned to three very broad interpretive classes.
BEHAVIORAL CLINICIANS
First, there are the behavioral clinicians. The strict behaviorist avoids making inferences about underlying
states and instead concentrates on the behavior of the patient. The behavioral clinician typically seeks
patient data based on personal observation or on direct reports from the patient or from the other observers.
These data are regarded as samples. Interpretation is Largely at Level I and II, although more recently some
behavioral clinicians have begun to show an interest in Level III interpretation.
PSYCHOMETRIC APPROACH
A second group of clinicians pride themselves on being empirical and objective. In particular, these
clinicians are likely to use objective tests to predict to relatively specific criteria. For example, will scores
from tests A, B, and C predict success in college, therapy outcome, or aggressive out bursts? This
psychometric approach to interpretation, as we shall see a bit later, is especially useful when the criteria
being predicted are crisp and well articulated. In general, this approach uses data as correlates of
something else-for example, a score at the 95th percentile on test X may be related to recidivism in
prisoners. The psychometrically oriented clinician is most concerned with standardized tests and their
norms, regression equations, or actuarial tables, and tends to employ_ Level I and II interpretation.
PSYCHODYNAMIC APPROACH
A third group of clinicians is more comfortable with a p s y c h o d y n a m i c a p p r o a c h . This has long been
a popular orientation in clinical psychology. Although current clinicians often seem to opt for a more
objective behavioral or psychometric approach, there is still more of the psychodynamicist in many of
them than they might like to admit. The psychodynamic approach strives to identify inner states or
determinants. Data from projective tests, unstructured clinical interviews and other sources are viewed as
signs of an underlying state. Interpretation tends to itched at Level III. A broad, often highly
impressionistic picture of the patient is drawn, although in many instances subtle normative assertions are
made.
QUANTITATIVE VERSUS SUBJECTIVE APPROACHES
Quietly embedded in the preceding discussion are two distinct approaches to clinical judgment and
interpretation. First is the quantitative or statistical approach, which emphasizes objectivity and is
presumably free from fuzzy thinking. Second is the subjective or clinical approach, which adherents
claim is the only method to offer truly useful interpretations and predictions.
THE QUANTITATIVE STATISTICAL APPROACH
Perhaps the simplest form of quantitative prediction that clinicians can use involves_ assigning scores
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to the various characteristics of their patients. This enables clinicians to determine the correlation
between any two characteristics. For example, suppose that after several years of practice, a clinician
begins to suspect a direct relationship between early termination of therapy and patients' needs for
independence. The clinician might attempt to verify this hypothesis by correlating "need for
independence" scores from a self-report inventory with the length of time that patients remain in
therapy. Should the correlation turn out to be substantially above .50, the clinician could use need
for independence scores to make interpretations and predictions regarding the duration of therapy.
Of course, more often than not, one cannot base important predictions on a single score or attribute. The
conclusion of therapy is more often a complex event that has a number of determinants.
Consequently, the clinician might want to obtain scores on several other variables, such as ego
strength, the experience of the therapist, marital satisfaction, and interpersonal trust. A multivariate
prediction model could then be constructed and tested. A particular caution to bear in mind,
however, is that even though a mu ltiple correlation from such an analy sis may turn out to be
quite high, it may well be much lower when applied to a new sample. This is especially true if the
original sample is small and the number of predictors is large. Further, the sample on which the
initial study is carried out may not be representative of therapy patients in general. What is true in
Kansas may not be true in California; what is true for psychoanalytic therapy may not be true for
behavior therapy. Therefore, clinicians have to be sure that they have correctly weighted various predictor
scores before they can generalize very far. They must cross-validate their prediction models using other
samples.
These statistical techniques permit a mechanical application that does not involve clinical decision
making at all once the formulas have been established. The feature that distinguishes these statistical
approaches from clinical approaches is that the former (no matter what their complex mathematical
development), once established, can be routinely applied by a clerk or a computer.
The quantitative, statistical approach, then, requires that the clinician keep careful records of the data,
observation and related material so that clinical interpretations and judgments can be quantified. Such
careful record keeping will permit the clinician to go beyond informal impressions based on previous
experience. With adequate records on large enough samples, the relations among a host of variables can
be assessed. Whether clinicians are evaluating their own performance or the performance of an entire
clinic, or are relating certain patient characteristics to various diagnostic or therapeutic outcomes,
quantified data can play a facilitating role. Such data enable clinicians to evaluate their judgment,
interpretations, and performance.
THE SUBJECTIVE CLINICAL APPROACH
The clinical approach is much more subjective, experiential, and intuitive. Here, subjective weights based
on experience suffice. The emphasis is on the application of judgment to the individual case. The classical
notation is that "clinical intuition" is not readily amenable to analysis and quantification It is a private
process in which clinicians themselves are sometimes unable to identify the cues in a patient's test responses
or verbalizations that led them to a given conclusion or judgment.
Once, for example, in the course of a Rorschach administration, a patient said, "This looks like a
Christmas tree." What did this mean? Perhaps nothing. Or perhaps it indicated a career in forestry. Or
perhaps it suggested an underlying sadness or depression in a person with few friends or family with
whom to enjoy the approaching holiday season. In this case, the last interpretation was later supported by
the patient during a discussion of his family background. The clinical student who had made the
correct interpretation in a training exercise explained her reasoning as follows: "It was near the Christmas
season; there were several references in the TAT to remote family figures; I remembered how I always
seem to become a little sad during Christmas; it suddenly popped into my head, and I just knew with
complete certainty that it was true-it simply felt right!"
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This example illustrates several things about clinical interpretation. First, such interpretation involves a
sensitive capacity to integrate material. The astute clinical psychologist pays attention to the wide range
of events that characterize the patient's behavior, history, other test responses, and so on. A clinician
must function a bit like the detective who takes in everything at the scene of the crime and then
makes a series of inductive or deductive generalizations that link these observations together. In
addition, there is often a willingness in the clinician to see a bit of him or herself in the patient-----a
kind of assumed similarity that enables the clinician to utilize his or her own experience in
interpreting the behavior and feelings of another.
Unfortunately, the presentation of this example has been one-sided. Little has been made of the
clinical student who believed that the Christmas tree suggested an interest in forestry .
Therefore, we may make two additional observations. First, there are individual differences in clinical
sensitivity. Second, for every instance of brilliant and sensitive clinical inference, there probably lurks
in the unrecalled recesses of memory an equally impressive misinterpretation.
Clinical interpretation, then, involves the sensitive integration of many sources of data into a coherent
picture of the patient. It also fulfills a hypothesis-generating function that is best of personality. But it
behooves responsible clinicians to make every effort to articulate the cues involved in their judgments
and to explicate the manner in which they make the leap from cues to conclusions. It is not enough to
be good clinicians. There is also a responsibility to pass on these skills to others.
COMPARING CLINICAL AND ACTUARIAL APPROACHES
Over the years, many studies have compared the relative accuracy of clinical and actuarial methods. Let
us now examine some of that work.
Comparison Studies
Sarbin (1943) contrasted the prediction of academic success of college freshmen made by a clerk
employing a regression equation with the predictions made by several counselors. The regression equation
predictors were aptitude test scores and high school rank. The counselors had available to them
the two preceding sources of data (but without their mathematical weighting), vocational interest
scores, interview data, and biographical data. Sarbin (1943) found that the counselors were no better than
the regression equation in their predictions even though they had the benefit of much mere
information.
Meehl (1954) surveyed a number of the studies available on clinical versus statistical, prediction and
concluded that in "all but one ... the predictions made actuarially [statistically] were either
approximately equal or superior to those made by a clinician" .In a later survey of additional research,
Meehl (1965) reaffirmed his earlier conclusions. However, Meehl (1954) also observed that, in several
studies, statistical predictions were made on the same data from which the regression equations
were developed. In short, the formulas were not cross-validated. Such formulas frequently show a
marked reduction in efficiency when they are applied to samples different from those used in their
derivation.
Sawyer (1966) regarded data collected by interview or observation as clinical data. He viewed
inventory, biographical, or clerically obtained data as statistical or mechanical. Having considered the
methodological problems and the equivocal results of the studies he examined, Sawyer concluded that
in combining data the mechanical mode is superior to the clinical mode. However, he also
concluded that the clinical method is useful in the data collection process. The clinical method can
provide an assessment of characteristics that would not normally be assessed by more mechanical
techniques of data collection. But once the data (from whatever source) are collected, they can best be
combined by statistical approaches.
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An example of an individual study comparing clinical and statistical prediction may help further
illustrate the nature of this controversy.
One of the most frequently cited studies of clinical versus statistical prediction was reported by
Goldberg(1965) .in this study, 13 PhD level staff-members and 16 predoctoral trainees were asked to
make judgments regarding the diagnostic status of more than 800 patients, based on these patients'
MMPI scores. These judgments were made without any contact with the patient or any additional
information on the patient. Each judge simply examined the MMPI profile (scores) for each patient
and then predicted whether the patient was "psychotic" or "neurotic." These judgments constituted
clinical predictions because it was left up to each judge as to how she or he used the MMPI information
to formulate a diagnosis.
In contrast, statistical predictions involved the application of a variety of algorithms, in which MMPI
scale scores were combined (added or subtracted), in some manner and previously established cutoff
scores for psychosis versus neurosis were used. In addition, some statistical predictions involved the
application of specified decision rules based on MMMPI high point codes or other psychometric signs.
A total of 65 different quantitatively based rules were considered.
What were these clinical and statistical predictions compared to in order to assess their accuracy? In
this study, the criterion diagnosis was the psychotic versus neurotic diagnosis provided by each
patient's hospital or clinic. Thus, the accuracy of each clinician's and each statistical algorithm's
prediction was determined by assessing the agreement between predictions and the actual criterion
diagnoses across all cases.
A variety of additional, updated reviews of the studies pitting clinical versus statistical prediction have
uniformly-demonstrated the superiority of s t a t i s t i c a l procedures (for example, Dawes, 1979, 1994;
Dawes, Faust, & Meehl. 1989; Garb, 1998; Goldberg, 1991; Kleinmuntz, 1990; Meehl, 1986; Wiggins,
1973). As stated by Meehl (1986):
Objections to These Findings
Dawes (1994) has outlined several of the major objections to large body- of evidence supporting the
superiority of statistical prediction, along with response, such objection.
First, critics argue that several of the individual studies reviewed contained research design flaws that may
have affected the findings. Dawes (1994) refers to this an "argument from a vacuum"-a possibility is
raised, but there is no empirical demonstration supporting the possibility. Although every study has its
limitations, it is difficult to imagine that the opposite conclusion (clinical prediction is superior) is
warranted when practically all of the studies support statistical prediction.
The second objection concerns the expertise of the judges/clinicians in these studies. Perhaps they were
not "true" experts, and a study employing expert clinicians would demonstrate the superiority of
clinical judgment. Although a wide variety of judges/clinicians were used in these studies, a number
of studies employed recognized "experts"-clinicians with many years of experience performing the
predictive task in question. There were a few instances in which an individual clinician performed as
well as the statistical formula, but this was more the exception than the rule. Thus, there is no
compelling empirical evidence that "expert" clinicians are superior.
A third objection is that the predictive tasks were not representative of prediction situations facing
clinicians (that is, not ecologically valid). A clinician's diagnosis may not be based only on the MMPI-2,
for example, but also on an interview with the patient. Dawes (1994) argues, however, that the
predictive tasks are components of what may go on in clinical practice clinicians purportedly use
the MMPI-2 information to make predictions. Further, several of the studies demonstrate that additional
information (such as interview material) obtained and used in the judge's clinical prediction may actually
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result in less accurate predictions than would be the case if the clinician had simply "stuck with
the statistical formula that was available.
Dawes (1994) goes on to suggest that much of the negative reaction to the findings is a function of our
human need to believe in a high degree of predictability in the world. This appears to be both a
cognitive and an emotional need. People have a built-in tendency to both seek and see order in the
world, and a lack of predictability in the world is likely to result in some degree of discomfort or
emotional distress. However, the need for predictability does not prove its existence.
BIAS IN CLINICAL JUDGMENT
Clinical judgment suffers when bias of any kind intrudes into the decision-making process. Bias exists
when accuracy of clinical judgment or prediction varies as a function of some client or patient
characteristic, not simply when judgments differ according to client characteristics (Garb, 1997, 1998).
For example, finding that a higher percentage of women than men are judged to suffer from major
depression would not indicate a bias against women. However, finding that a higher percentage of women
than men are given this diagnosis when the same symptoms are presented would indicate bias.
Garb (1997) recently reviewed the empirical evidence for race bias, social class bias, and gender bias in
clinical judgment. Interestingly, he found that many conventionally held beliefs about these types of
bias were not supported. For example, there was little support for the beliefs t h at
( 1 ) l o w e r - so c i o e c o n o m i c - c l a s s p a t i e n t s a r e judged to be more seriously disturbed than those from
higher socioeconomic classes or
(2) Women patients are judged to be more disturbed or dysfunctional than men patients. However, there
was strong evidence to support the existence of several other types of biases:
1. Black and Hispanic patients who have psychotic mood disorders are more likely to be
misdiagnosed with schizophrenia than are similar White patients.
2. Even when presenting the same constellation of symptoms, men are more likely to be diagnosed
as antisocial and women are more likely to be diagnosed as histrionic.
3. Middle-class patients are more likely to be referred for psychotherapy than lower-class patients.
4. Black patients are more likely to be prescribed antipsychotic medications than members of other
racial groups, even when the Black patients are not more psychotic.
Garb (1997) made the following recommendations to help clinicians overcome these and other bias
(1) Be aware of and sensitive to the biases that have been documented in the literature.
(2) Attend to the diagnostic criteria in diagnostic manuals.
(3) Whenever possible, use statistical prediction rules instead of clinical judgment or prediction
EXPERIENCE AND TRAINING
To date, empirical evidence does not support the position that increased clinical experience results in
increased accuracy in prediction (Dawes, 1994; Garb, 1989, 1998). This seems to fly in the face of
conventional wisdom. Why is it that we do not see evidence for the effect of clinical experience in
clinical psychology and other mental health fields? There are several possibilities (Dawes, 1994).
First, the accuracy of predictions is limited by the available measures and methods that are used as aids
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in the prediction process. If scores from psychological tests, for example, are not strongly correlated with
the criterion of interest (that is, highly valid), then it is unlikely one could ever observe an effect for
clinical experience. The accuracy of predictions will remain modest at best and will not depend on how
"clinically experienced" the clinician is.
Second, we often cannot define precisely what we are trying to predict (for example, "abusive
personality"), and no gold standards for our criteria exist to enable us to assess objectively the accuracy of
our predictions. As a result, true feedback is impossible, and diagnosticians are not able to profit from
experience.
Third, we tend to remember our accurate predictions and to forget our inaccurate ones. Therefore,
more experience in the prediction process does not necessarily lead to increased accuracy because the
feedback that is incorporated is incomplete.
As for the virtue of receiving specific types of professional training, there is not much evidence to
suggest that one profession is superior to another in making accurate diagnostic judgments. For
example, even in differentiating psychological symptoms that are masking medical disorders from those
without underlying medical disorders, medical and non medical practitioners did not differ in their
accuracy (Sanchez & Kahn, 1991).
All of this research is somewhat sobering for the field of clinical psychology. However, it is our
professional responsibility to be aware of the limits of our predictive ability and not to promote the "myth
of experience." One thing is sure. Clinicians will continue to make decisions-they have no choice.
The important thing is to ensure that clinical psychologists are as well prepared as' they can be, as well
as to train clinical psychologists to use the-best available measures and techniques for a given prediction
situation.
CONCLUSION
Given the current state of affairs, the following conclusions regarding the relative strengths of clinical and
actuarial methods seem warranted.
The clinical approach is especially valuable when:
1. Information is needed about areas or events for which no adequate tests are available. In this case,
the research fails to offer any evidence that the data-gathering function of the clinician can be
replaced by a machine.
2. Rare, unusual events of a highly individualized nature are to be predicted' or judged. Regression
equations or other formulas cannot be developed to handle such events, and clinical judgment is the
only recourse.
3. The clinical judgments involve instances for which no statistical equations have been developed.
The vast majority of instances, in effect, fall into this category. The day-to-day decisions of the
clinician are such that the availability of a useful equation would itself be a one and unusual event.
4 . T h e role of unforeseen circumstances could negate the efficiency of a formula. For example, a
formula might very easily outstrip the performance of a clinician in predicting suitability for hospital
discharge. In the role of data gatherer, however, the clinician might unearth important data from a
patient that would negate an otherwise perfectly logical statistical prediction.
The statistical approach is especially valuable when:
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1. The outcome to be predicted is objective and specific. For example, the statistical approach
w i l l b e e sp e c i a l l y e f f ec t i v e i n p r ed i c t i n g grades, successful discharge, vocational success,
and similar objective outcomes.
2. The outcomes for large, heterogeneous samples are involved, and interest in the individual case is
minimal. Having a statistical formul a to predict how many of 50,000 men will receive
dishonorable discharges from the Army will be highly useful to the Army, though less so
for the clinician who is dealing with Private Smith.
3. There is reason to be particularly concerned about human judgmental error or bias. Fatigue,
boredom, bias, and a h o st o f o t her h u man failings can be responsible for clinical error.
Oft en , su ch effect s are ran do m 'a n d u np redictable. Formulas, equations, and computers
never become tired, bored, or biased.
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Table of Contents:
  1. MENTAL HEALTH TODAY: A QUICK LOOK OF THE PICTURE:PARA-PROFESSIONALS
  2. THE SKILLS & ACTIVITIES OF A CLINICAL PSYCHOLOGIST:THE INTERNSHIP
  3. HOW A CLINICAL PSYCHOLOGIST THINKS:Brian’s Case; an example, PREDICTION
  4. HISTORICAL OVERVIEW OF CLINICAL PSYCHOLOGY:THE GREEK PERIOD
  5. HISTORY OF CLINICAL PSYCHOLOGY:Research, Assessment, CONCLUSION
  6. HOW CLINICAL PSYCHOLOGISTS BECAME INVOLVED IN TREATMENT
  7. MODELS OF TRAINING IN CLINICAL PSYCHOLOGY:PROFESSIONAL SCHOOLS
  8. CURRENT ISSUES IN CLINICAL PSYCHOLOGY:CERTIFICATION, LICENSING
  9. ETHICAL STANDARDS FOR CLINICAL PSYCHOLOGISTS:PREAMBLE
  10. THE ROLE OF RESEARCH IN CLINICAL PSYCHOLOGY:LIMITATION
  11. THE RESEARCH PROCESS:GENERATING HYPOTHESES, RESEARCH METHODS
  12. THE CONCEPT OF ABNORMAL BEHAVIOR & MENTAL ILLNESS
  13. CAUSES OF MENTAL ILLNESOVERVIEW OF ETIOLOGY:PANDAS
  14. THE PROCESS OF DIAGNOSIS:ADVANTAGES OF DIAGNOSIS, DESCRIPTION
  15. THE CONCEPT OF PSYCHOLOGICAL ASSESSMENT IN CLINICAL PSYCHOLOGY
  16. THE CLINICAL INTERVIEW:The intake / admission interview, Structured interview
  17. THE ASSESSMENT OF INTELLIGENCE:RELIABILTY AND VALIDITY, CATTELL’S THEORY
  18. INTELLIGENCE TESTS:PURPOSE, COMMON PROCEDURES, PURPOSE
  19. THE USE AND ABUSE OF PSYCHOLOGICAL TESTING:PERSONALITY
  20. THE PROJECTIVE PERSONALITY TESTS:THE RORSCHACH
  21. THE OBSERVATIONAL ASSESSMENT AND ITS TYPES:Home Observation
  22. THE BEHAVIORAL ASSESSMENT THROUGH INTERVIEWS, INVENTORIES AND CHECK LISTS
  23. THE PROCESS AND ACCURACY OF CLINICAL JUDGEMENT:Comparison Studies
  24. METHODS OF IMPROVING INTERPRETATION AND JUDGMENT
  25. PSYCHOLOGICAL INTERVENTIONS AND THEIR GOALS:THE EXPERT ROLE
  26. IMPORTANCE OF PSYCHOTHERAPY:ETHICAL CONSIDERATIONS
  27. COURSE OF NEW CLINICAL INTERVENTIONS:IMPLEMENTING TREATMENT
  28. NATURE OF SPECIFIC THERAPEUTIC VARIABLES:CLIENT’S MOTIVATION
  29. THE BEGINNING OF PSYCHOANALYSIS:THE CASE OF ANNA, THE INSTINCTS
  30. PSYCHOANALYTIC ALTERNATIVES:EGO ANALYSIS, CURATIVE FACTORS
  31. CLIENT CENTERED THERAPY:PURPOSE, BACKGROUND, PROCESS
  32. GESTALT THERAPY METHODS AND PROCEDURES:SELF-DIALOGUE
  33. ORIGINS AND TRADITIONAL TECHNIQUES OF BEHAVIOR THERAPY
  34. COGNITIVE BEHAVIORAL THERAPY:MODELING, RATIONAL RESTRUCTURING
  35. GROUP THERAPY: METHODS AND PROCEDURES:CURATIVE FACTORS
  36. FAMILY AND COUPLES THERAPY:POSSIBLE RISKS
  37. INTRODUCTION AND HISTORY OF COMMUNITY PSYCHOLOGY:THE ENVIRONMENT
  38. METHODS OF INTERVENTION AND CHANGE IN COMMUNITY PSYCHOLOGY
  39. INTRODUCTION AND HISTORY OF HEALTH PSYCHOLOGY
  40. APPLICATIONS OF HEALTH PSYCHOLOGY:OBESITY, HEALTH CARE TRENDS
  41. NEUROPSYCHOLOGY PERSPECTIVES AND HISTORY:STRUCTURE AND FUNCTION
  42. METHODS OF NEUROLOGICAL ASSESSMENT:Level Of Performance, Pattern Analysis
  43. FORENSIC PSYCHOLOGY:Qualification, Testifying, Cross Examination, Criminal Cases
  44. PEDIATRIC AND CHILD PSYCHOLOGY: HISTORY AND PERSPECTIVE
  45. INTERVENTIONS & TRAINING IN PEDIATRIC AND CLINICAL CHILD PSYCHOLOGY