Many practice situations require that social workers make predictions about the future behavior of their clients. Returning children to the care of their natural parents, screening mentally ill people for admission to psychiatric treatment facilities, referring high-risk elderly patients for aftercare services, assessing an individual’s chances of committing suicide—all these and many other activities require that practitioners make some type of prognosis or forecast about their clients’ future progress.
In addition, practitioners find themselves subject to growing pressures to provide expert opinions about the future course of events in their clients’ lives. This situation has arisen because the need for professional opinions, especially those regarding individuals whom society considers as particularly vulnerable (for example, children and elderly people) or threatening (for example, individuals classified as dangerous), is becoming a "public and collective concern" (Bosk, 1989, p. 461).
Error is a problem of special concern in clinical prediction, but despite the growing importance of this topic (Gambrill, 1990), social work practice texts typically give it scant notice. Current events, however, are forcing a reevaluation of this position as community concern, liability issues, and legal and administrative pressure force professionals to pay increased attention to understanding the prediction process and the ways it can be improved (Besharov, 1985; Gambrill, 1990)
Prediction is a common feature of clinical reasoning and constitutes an important feature of the clinical inference process. According to Wiggins (1973), prediction is generally composed of the following four steps:
1. Data collection, during which available information is assembled and reviewed
2. Identification of variables: during which meaningful cues and patterns are noted
3. Formation of hypothesis: during which clinical inferences are made based on variables that have been identified
4. Forecasting: during which hypotheses are used to generate probabilities about future trends and results
Although these steps are not followed in every case, they reflect commonly described features of clinical prediction. In form they resemble the process of reasoning generally associated with the physical sciences (Gill, 1986). This process is also used in social work practice.
A social worker in a psychiatric hospital was asked by ward staff to assist a 32-year-old female patient in arranging her air travel to another state in preparation for her discharge. The social worker contacted the airline about travel information and was told that as the social worker, she would be required by airline regulations to sign a statement that, in the worker's professional judgment, the patient was psychiatrically stable and sufficiently in control so as to pose no risk to others and that the patient did not require any specialized behavioral management during her flight.
To honor this request, the social worker met with the patient to evaluate her condition, discussed her progress with staff, and quickly reviewed her hospital records. While interacting with the patient and staff, the worker searched for patterns or cues that would indicate the presence of problems that would preclude air travel by the patient. Because none were found, the social worker felt that she could confidently predict that the patient was fully capable of making the flight. The worker then completed and signed the statement required by the airline company.
While driving the patient to the airport, however, the worker suddenly felt that she had made an error. She observed that, evidently as a result of anxiety, the patient was nervously gesturing and softly talking to herself while chain-smoking cigarettes. Because the worker realized that this behavior might alarm airline personnel and other passengers, she counseled the patient about the potential negative effect of such mannerisms. After counseling the patient voluntarily took a prescribed tranquilizer on arrival at the airport and agreed to control her behavior more carefully. Because the patient now appeared calmer and in better control, the worker felt that the patient posed no risk and could proceed with her flight.
As indicated in this example, clinical prediction is a process involving continuous observation, hypothesis generation, and hypothesis testing. Error can occur at any stage in this process because of such factors as inaccurate information, biased observations, faulty hypotheses, improper testing, and unwarranted conclusions (Kahneman, Slovic, & Tversky, 1982).
Types of Error
There are two principal types of errors in clinical prediction: Type I errors, in which it is concluded that a clinical hypothesis is false when it is actually true, and Type II errors, in which it is concluded that a clinical hypothesis is true when it is actually false (Scheff, 1972).
Using the example of the psychiatric patient on the airplane, the worker took great pains to avoid making a Type I error by ensuring that all possible attempts were made to test the hypothesis "this patient is too sick for air travel." The worker finally concluded that this hypothesis was false and allowed the patient to board the airplane. Had the worker committed a Type I error (that is, had the hypothesis "this patient is too sick" been true), the results might well have been at the least unpleasant and at the most disastrous.
In general, social workers and most other human services professionals try hardest to avoid Type I errors in clinical prediction (Scheff, 1972). Practitioners are increasingly expected to provide guarantees that certain behaviors or conditions will not occur. When these do in fact occur, professionals find their judgment questioned and possibly their position as expert attacked.
Other instances in which Type I errors can be significant are found in child welfare, when workers are required to determine whether parents or family members have the potential for child abuse (Johnson & L'Esperance, 1984); in corrections, where workers must determine whether offenders constitute a danger to the community (Ashford, 1987); and in mental health settings, where practitioners need to assess individuals for involuntary psychiatric hospitalization (Dvorkin, 1989).
In other situations, however, the social work practitioner might be especially wary of committing Type II errors.
In correctional settings, for example, social workers are sometimes required to predict whether offenders will benefit from community drug rehabilitation programs or instead will require continued incarceration. A Type II error in prediction would mean that, on the basis of the social worker's prediction, an offender incapable of rehabilitation could be sent to a drug rehabilitation program and then released, presumably without receiving adequate punishment for his or her crime (Ashford, 1987).
--Murdach, A. D. (1994). Avoiding Errors in Clinical Prediction. Social Work, 39(4), 381.
Reflection Exercise Explanation
Goal of this Home Study Course is to create a learning experience that enhances
your clinical skills. We encourage you to discuss the Personal Reflection
Journaling Activities, found at the end of each Section, with your colleagues.
Thus, you are provided with an opportunity for a Group Discussion experience.
Case Study examples might include: family background, socio-economic status, education,
occupation, social/emotional issues, legal/financial issues, death/dying/health,
home management, parenting, etc. as you deem appropriate. A Case Study is to be
approximately 75 words in length. However, since the content of these Personal
Reflection Journaling Exercises is intended for your future reference, they
may contain confidential information and are to be applied as a work in
progress. You will not
be required to provide us with these Journaling Activities.
Reflection Exercise #1
The preceding section contained information
about errors in clinical prediction. Write case study example
regarding how you might use the content of this section in your practice.
Peer-Reviewed Journal Article References:
Fokkema, M., Smits, N., Kelderman, H., & Penninx, B. W. J. H. (2015). Connecting clinical and actuarial prediction with rule-based methods. Psychological Assessment, 27(2), 636–644.
Karon, B. P. (2000). The clinical interpretation of the Thematic Apperception Test, Rorschach, and other clinical data: A reexamination of statistical versus clinical prediction. Professional Psychology: Research and Practice, 31(2), 230–233.
Nahum, L., Barcellona-Lehmann, S., Morand, S., Sander, D., & Schnider, A. (2012). Intrinsic emotional relevance of outcomes and prediction error: Their influence on early processing of subsequent stimulus during reversal learning. Journal of Psychophysiology, 26(1), 42–50.
Ruscio, J. (2000). The role of complex thought in clinical prediction: Social accountability and the need for cognition. Journal of Consulting and Clinical Psychology, 68(1), 145–154.
What is prediction generally composed of? Record the letter of the correct answer the