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approach, content experts categorized the bene cial actions in terms of their importance in the context of the case Actions were considered essential for adequate care , important for optimal care , or appropriate for optimal care Non-indicated actions were similarly placed in one of three categories de ned in terms of the associated level of risk and/or intrusiveness Finally, a variable was created that represented the time frame in which the essential actions were completed To produce scores, counts of actions were produced for examinees These counts, along with the variable associated with timing, were then used as the dependent measures in a multiple regression The independent measure was a rating of the examinee s performance on the case In effect, the regression procedure allowed for the estimation of optimal weights for predicting the rating of the examinee s performance based on the seven scoring variables (counts of actions in each of the three categories representing bene cial actions, counts of actions in each of the three categories representing non-indicated actions, and the timing variable) The resulting weights could then be applied to produce predicted ratings for performances that had not been reviewed by experts This regression-based procedure produced scores that were highly correlated with the actual ratings The one potential limitation of the procedure was that although resulting scores had a high correspondence to the expert ratings the structure of the scoring procedure was at best an approximation of the logic that experts used in making their judgments In response to this limitation, a procedure was developed in which an effort was made to combine the scorable items using logic statements that directly approximated the rules that experts used when rating examinee performance These logical statements de ned the combinations of diagnostic studies, therapeutic interventions and follow-up required for a given score The logic also included requirements about timing and sequencing (eg treatment must be initiated prior to the end of the rst day or credit is given for treatment only if the results of a diagnostic study are seen prior to initiating treatment ) These latter two procedures were evaluated in detail during the years immediately preceding operational implementation of the simulation as part of USMLE An obvious rst step in that evaluation was to compare the correlations between the scores produced using these methods with the expert rating these scores were intended to approximate Several published studies report correlations between one or both of these scores and ratings of the same performances (Clauser, Margolis, Clyman, & Ross, 1997a; Clauser et al, 1997b; Clauser, Subhiyah, Nungester, Ripkey, Clyman, & McKinley, 1995) The results lead to two general conclusions: (1) the resulting scores have a generally high correlation with the related ratings and (2) scores from the regression-based procedure are generally more highly correlated with the ratings than are those from the rule-based procedure Table 91 provides one set of these correlations (taken from Clauser et al, 1997b) Subsequent studies compared the reliability (or generalizability) of scores produced using the two methods, as well as the correlation between the observed scores produced using these methods and the pro ciency measured.



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The PBNM architecture addresses both of the challenges with existing network management systems discussed earlier Con guration of all the individual network parameters is simpli ed and automated Administrators do not need to con gure each individual parameter They just de ne high-level policies and goals through the policy management tool and the PBNM system is then responsible for con guring the detailed individual con guration parameters automatically based on the high-level input This is accomplished by the PDP, which parses the high-level policies, as discussed earlier, and then con gures the PEPs in a way that is consistent with those policies The PBNM also deals effectively with changes whether they are introduced by the administrators or due to changes in the network itself Administrators can introduce new high-level policies or change existing policies through the policy management tool The PDP is then responsible for parsing the changes introduced by the administrators and recon guring the PEPs to ensure that the updated policies are enforced The PDP also gets noti ed about changes in the network such as cyber attacks, faults, and performance degradation The PDP is then responsible for recon guring the PEPs based on the new events to ensure that network effectively supports the highlevel policies The PBNM architecture is a very elegant architecture that can address the challenge of automating network management and in particular dealing with the constant changes that are of particular importance for wireless ad hoc networks There are two areas of the architecture that have proven to be challenging in implementing the high-level PBNM architecture and have attracted the interest of the research community The rst area is the de nition of policy languages that can be used for capturing the high-level goals that need to be enforced by the PBNM architecture The second area is the problem of extending the PBNM architecture to operate in a fully distributed environment We will now discuss these two areas in detail.





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