The former refers to decision-making procedures that involve the formal combination of variables or pieces of information, by way of equations or other algorithmic processes, to reach a decision. The latter is defined by a lack of such rules. In contemporary risk assessment, approaches may be generally classified as structured and unstructured.
As described below, unstructured risk assessment is, in essence, the clinical prediction to which Paul Meehl referred. Structured risk assessment includes actuarial prediction as well as a more recent approach termed structured professional judgment.
By definition, it is based primarily on professional opinion, intuition, and clinical experience. Assessors have absolute discretion in terms of selecting risk factors to consider, how to conceptualize them, how to synthesize case material, and how to interpret this information to render decisions. As such, this method is inherently informal and subjective. Although clinical judgment is a routine and necessary component within many clinical decision-making contexts, the defining feature of clinical judgment in terms of prediction is the lack of rules to integrate case information.
Although this permits flexibility, ostensible widespread applicability, and relevance to the individual patient, there are numerous problems with this approach. First, because of the lack of rules, critics contend that the technique generally lacks consistency because independent clinicians may focus on dissimilar sources of information and subsequently form disparate conclusions low interrater reliability. Second, clinicians may or may not attend to variables that actually relate to violent behavior low content validity.
Third, either failing to attend to important risk factors, attending to irrelevant variables, or giving improper weight to risk factors, will inevitably decrease the accuracy of decisions low predictive validity. Fourth, detractors argue that unaided clinical decision making precludes transparency of decision making, which is essential in a legal forum low legal helpfulness.
Other factors leading to low or at least inconsistent accuracy include susceptibility to decisional biases and heuristics, failure to consider base rate information, failure to integrate situational information, and a lack of specificity about the criterion variable.
Research bears these weaknesses out: The accuracy of unstructured risk assessment has been shown a to vary considerably across different clinicians and b though predictive of violence, to be less strongly related to violence than more systematic approaches.
In response to the shortcomings of the unstructured clinical approach and the disquieting implications these held for important legal decisions, researchers started to investigate structured approaches. Contemporary structured risk assessment approaches share common features such as a inclusion of a fixed set of risk factors, b operational definitions of risk factors, c scoring or coding procedures for risk factors, and d direction for how to integrate risk factors to reach a final decision about risk.
As described below, however, there are important differences between the two primary approaches to structured risk assessment. The first structured approach that was investigated was actuarial prediction. Technically, a prediction approach is said to be actuarial when it uses formal rules to combine variables or risk factors to make a decision. This process, therefore, involves the formal application of a predetermined set of explicit and formulaic decision rules to make a decision about the likelihood of violence.
The actuarial approach has been described as algorithmic, mechanical, well specified, and completely reproducible. An associated, though not defining, feature of actuarial prediction is the use of empirical item selection; that is, the variables that comprise risk factors on an actuarial risk assessment measure are often selected because they demonstrated statistical associations with violence in one or, more rarely, two or more specific construction or calibration sample.
Another associated feature of actuarial prediction is that the risk factors that are derived empirically are typically weighted according to the strength of association with violence observed in the construction sample s.
The primary argument in support of actuarial prediction techniques is that they facilitate interrater reliability and predictive validity, especially in comparison with unstructured approaches. Because actuarial procedures use explicit rules for combining risk factors, they yield the same decision regardless of who uses them high interrater reliability , and given the presence of the same risk factors across cases, they yield the same outcome.
Furthermore, they are transparent reviewable and accountable. Many actuarial prediction techniques are statistically optimized because they weigh variables according to their relationship with violence.
Hence, at least in the samples in which they were developed, they tend to have high predictive validity in comparison with unstructured approaches. There is general agreement that the actuarial approach to risk assessment yields higher predictive accuracy than does the unstructured approach when the two are compared for group-based nomothetic predictions within the same sample.
Perhaps the best evidence of this stems from a meta-analysis of studies conducted by William Grove and colleagues that directly compared actuarial prediction with unstructured clinical prediction. Actuarial prediction was more accurate than clinical prediction in approximately one-third to half of the studies. In approximately half the studies, there was no difference in predictive accuracy.
In a small minority of studies, unstructured clinical prediction was more accurate. Despite the important advantages of enhanced inter-rater reliability and predictive validity that actuarial prediction possesses, commentators have noted several weaknesses.
Perhaps most important, the predictive properties of actuarial models tend to be optimized within the sample of development, with no guarantee that these properties will apply to novel settings or samples generalizability. The doctor can approach the problem using her clinical judgment, or by using her actuarial judgment.
Clinical judgment requires the doctor -- generally a psychiatrist, physician, or psychologist -- to process the data in his head. Margot Phaneuf, R.
The professional making the judgments should have prior training, which enhances his understanding of the subject at hand. The advantage of clinical judgment is that the professional may have knowledge of local health phenomena -- for instance, cancer clusters -- that aids with diagnoses. Clinical judgment also has the advantage of including rare events that may not be included in an actuarial formula.
The actuarial, or statistical method, is more objective. In the words of Dawes, Faust and Meehl: "The human judge is eliminated and conclusions rest solely on empirically established relations between data and the condition or event of interest.
For example, mental health professionals, in particular, are often called upon to try to predict future violent behavior. For many years, up to the s, it was assumed this was not possible.
Today's actuarial methods disprove this, and since the s mental health professionals have been able to predict violent behavior with clearly better than chance accuracy. Actuarial judgment has long been used in such areas as the setting of insurance rates, and is generally recognized to be the more accurate and objective of the two methods.
According to Douglass Mossman, M. D, "when it comes to making predictions, clinical judgment -- making predictions by putting together information in one's head -- often is inferior to using simple formulae derived from empirically demonstrated relationships between data and outcome.
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