What Is the Nature of the Montana Constitution?

What Is the Nature of the Montana Constitution

Adaptive treatment strategies in internet-delivered Cognitive behavior therapy : predicting and avoiding treatment failures

Background: Internet-delivered Cognitive Behavior Therapy (ICBT) is efficacious for a number of psychiatric disorders and can be successfully implemented in routine psychiatric care. Still, only about half of patients experience a good enough treatment outcome. Using data from the early part of treatment to identify patients with high risk of not benefitting from it, and target them with additional resources to prevent the predicted failure is a potential way forward. We call this an Adaptive Treatment Strategy, and a very important part of it is the ability to predict the outcome for a specific patient. Aims: To establish a proof of concept for an Adaptive Treatment Strategy in ICBT, and explore outcome prediction further by evaluating the accuracy of an empirically supported classification algorithm, the time point in treatment when acceptable accuracy can be reached, and the accuracy of ICBT-therapists’ own predictions. Preliminary benchmarks regarding the clinical usefulness of prediction will be established. Studies: Four studies were performed: Study I was a randomized controlled trial (RCT; n=251) where patients’ risk of treatment Failure (Red=high risk of failure, Green=low risk) was predicted during week 4 out of 9 in ICBT for Insomnia. Red patients (n=102) were then randomized to either continuing with standard treatment (n=51) or having their treatment individually adapted (n=51). In Study II, the classification algorithm from Study I was evaluated in terms of classification accuracy and the contribution of the different predictors used. In Study III, data from 4310 regular care ICBT-patients having received treatment for either Depression, Social anxiety disorder or Panic disorder were analyzed in a series of multiple regression models using weekly observations of the primary symptom measure as predictors to classify risk of Failure. As a contrast, Study IV examines ICBT therapists’ own predictions on both categorical and continuous treatment outcomes, as they made predictions for each of their patients (n=897) during week 4 in the same three treatments as in Study III. Results: The RCT was successful in that Red patients receiving Adapted treatment improved significantly more than Red patients receiving standard treatment, and their odds of failure were nearly cut in half. Green patients did better than Red patients, indicating that the accuracy of the classification algorithm was clinically useful. Study II showed that the balanced accuracy of the classifier was 67% and that only 11 of 21 predictors correlated significantly with Failure. Notable predictors were symptom levels as well as different markers of treatment engagement. Study III and IV showed that acceptable predictions could be made halfway through treatment using only symptom scores and basic statistics, and that ICBT-therapists predicted outcomes better than chance but on average 9.5 % less accurate than the statistical models. Therapist predictions reached the clinical acceptance benchmark only for remission in Social anxiety disorder. At treatment week four, therapist could predict on average 16% of the variance in continuous outcomes, compared to a statistical model explaining 39%. Conclusions: We find support for the clinical usefulness of an Adaptive Treatment Strategy in ICBT for insomnia, and establish a preliminary benchmark that a classification algorithm with at least 67% balanced accuracy should be sufficient for clinical purposes. Simple statistical models using only symptom scores can reach clinically acceptable levels of accuracy halfway through 12-week ICBT-programs. Previous findings that therapists’ predictions are less accurate than statistical models seem to hold also for therapists providing ICBT. However, it was also indicated that clinicians’ ratings of adherence and activity do add unique information to prediction algorithms. In line with previous findings, the vast majority of useful prediction variables were found during early treatment, rather than before treatment start

Planning under risk and uncertainty

This thesis concentrates on the optimization of large-scale management policies under conditions of risk and uncertainty. In paper I, we address the problem of solving large-scale spatial and temporal natural resource management problems. To model these types of problems, the framework of graph-based Markov decision processes (GMDPs) can be used. Two algorithms for computation of high-quality management policies are presented: the first is based on approximate linear programming (ALP) and the second is based on mean-field approximation and approximate policy iteration (MF-API). The applicability and efficiency of the algorithms were demonstrated by their ability to compute near-optimal management policies for two large-scale management problems. It was concluded that the two algorithms compute policies of similar quality. However, the MF-API algorithm should be used when both the policy and the expected value of the computed policy are required, while the ALP algorithm may be preferred when only the policy is required. In paper II, a number of reinforcement learning algorithms are presented that can be used to compute management policies for GMDPs when the transition function can only be simulated because its explicit formulation is unknown. Studies of the efficiency of the algorithms for three management problems led us to conclude that some of these algorithms were able to compute near-optimal management policies. In paper III, we used the GMDP framework to optimize long-term forestry management policies under stochastic wind-damage events. The model was demonstrated by a case study of an estate consisting of 1,200 ha of forest land, divided into 623 stands. We concluded that managing the estate according to the risk of wind damage increased the expected net present value (NPV) of the whole estate only slightly, less than 2%, under different wind-risk assumptions. Most of the stands were managed in the same manner as when the risk of wind damage was not considered. However, the analysis rests on properties of the model that need to be refined before definite conclusions can be drawn

23P. Dialogue Model for Media:

Computer-mediated communication research focuses on media richness and naturalness in communication tools and their correlation for communication effectiveness. The underlying idea is that the more similar the medium is to face-to-face communication, the richer it is. The more equivocal a task in the organization is, the richer medium is required for communication. This approach has two problems. First, it focuses on the richness of the medium used not the process of communicating. Second, as for the medium, it is the support for richness that is expected, not their support for the process of communication. This paper presents a tentative Dialogue Model for Media that aims to support the communication process. The model is based on central elements that are required to support the communication process