contemporary clinical psychology. Where is clinical psychology going What is its future What are some of the current hot topics in clinical psychology What are its current and future challenges These are some of the questions addressed in this chapter. One of the best examples of a current hot topic in clinical psychology is managed health care. Some have suggested that managed health care may alter clinical psychology in a permanent and profound way (Anders, 1996; Benedict & Phelps, 1998; C. Chambliss, 2000; N. Cummings, 1984, 1995; R. Fox, 1994; A. Kovacs, 1996). Although managed health care will be addressed in more detail later, a brief introduction to this important issue demonstrates how quickly fundamental aspects of clinical psychology can change. Since the 1970s (and much earlier for physicians), someone who wanted to see a clinical psychologist for psychotherapy or other psychological service could choose to be treated by any licensed psychologist in his or her geographical area. The psychologist would conduct as many sessions as necessary to best treat the patient, using whatever theoretical orientation(s) and techniques he or she deemed appropriate to provide competent and professional service. Insurance carriers such as Blue Cross/Blue Shield would rarely question the treatment plan, and would typically reimburse between 50% and 80% of the fees regardless of whether the treatment lasted for 3 sessions or 300. A similar scenario existed for physicians of all specialties. However, in more recent years (primarily during the late 1980s and 1990s), this scenario has changed radically. Today, the patient typically contacts his or her insurance carrier to determine which therapists in the patient s area are part of the insurance company s preferred provider list. The patient then contacts the professional and is allowed a specific number of sessions authorized by the insurance carrier.
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Research Findings for Self-Efficacy in Exercise Early work in this area tended to focus on patients, such as those in cardiac rehabilitation. For example, C. E. Ewart, Taylor, Reese, and DeBusk (1983) studied self-efficacy in the context of treadmill running with post-myocardial infarction patients. Before and after treadmill exercise, assessment of self-efficacy to take part in walking, running, stair climbing, sexual intercourse, lifting, and general exertion was made. Results showed that positive changes in self-efficacy took place following treadmill exercise, and that this was greatest for running, suggesting that efficacy effects can generalize but appear to have stronger effects on similar exercise modes. When counseling also took place, it was found that efficacy perceptions for some
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When the conditional independence assumption is valid, the na ve Bayes classi ca tion is the same as the MAP classi cation. Therefore, we investigate whether the assumption of conditional independence holds for our churn data set example, as shown in Table 5.5. In each case, note that the approximation for the nonchurners is several times closer than for the churners. This may indicate that the assumption of conditional independence assumption is best validated for nonrare categories, another argument in support of balancing when necessary. We now proceed to calculate na ve Bayes classi cations for the churn data set. For a new customer belonging to both plans, we have for churners, p(I |C) p(V |C) p(C) = 0.0470(0.1449) = 0.0068 and for nonchurners, p(I |C) p(V |C) p(C) = 0.0193(0.8551) = 0.0165 The na ve Bayes classi cation for new customers who belong to both plans is therefore churn = false since 0.0165 is the larger of the two values. It turns out that just as for the MAP classi er, all four cases return a na ve Bayes classi cation of churn = false. Also, after 25.31%/74.69% balancing, new customers who belong to the International Plan are classi ed by na ve Bayes as churners, regardless of VoiceMail Plan membership, just as for the MAP classi er. These results are left to the exercises for veri cation. When using naive Bayes classi cation, far fewer probabilities need to be estimated, just k m probabilities rather than k m for the MAP classi er: in other words, just the number of predictor variables times the number of distinct values of the target variable. In the marital status example, where we had k = 5 distinct marital statuses and m = 10 predictor variables, we would need to compute only km = 5(10) = 50 probabilities rather than the 9.7 million needed for the MAP classi er. At 10 records per cell, that would mean that only 500 records would be needed compared to the nearly
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Figure 4.20 Global query tree for Example 4.8.
behalf. Psychologists must not solicit testimonials from current patients or others who are potentially vulnerable. Psychologists must be very careful in how they represent themselves to the public. They
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