This dissertation explores what it means to “Act Upon” measure-based feedback in the group therapy context. Eleven group leaders at three college counseling centers were provided feedback completed by their group members using the Group Questionnaire (GQ) and the Outcome Questionnaire (OQ-45). Researchers selected two a priori ways in which the feedback could be “acted upon”: via GQ Use and GQ Value. Leaders reported their use and value of the feedback using two data sources—weekly leader slips and end-of-term debrief interview transcripts. Both sources of data were content analyzed across several phases of coding. The resultant categories are intended to provide a preliminary understanding of how leaders treat the feedback received. Dimensions were then added to consolidate the meaning of the categories into a temporal pattern. Finally, using the resultant data, a scheme for quantifying the “acted upon” construct is proposed in effort to develop a potential moderator or mediator variable for future quantitative analyses. Implications of the dissertation are then discussed.

The use of treatment support tools to enhance client outcomes is not well understood in the youth treatment literature. Adult outcome researchers have found that the use of Clinical Support Tools (CST) leads to improved outcomes with clients identified as at risk for treatment failure. However, the American Psychological Association (APA) has noted that understanding important client factors that influence treatment is critical during the clinical formulation and treatment planning phase of therapy. No studies to date have evaluated the effectiveness of utilizing a CST as a treatment planning tool with youth clients. The purpose of this study was to evaluate the effectiveness of the Treatment Support Measure, a CST, for the purpose of treatment planning rather than as a reaction to clients who became at-risk for treatment failure. Two hundred and eight youth participants and their caregivers from three outpatient community mental health clinics were randomly assigned to a feedback (TSM-FB) or Non-FB condition. All participants completed the Youth Outcome Questionnaire (Y-OQ) at each session. The TSM was administered to clients in the TSM-FB condition during the intake session. Only therapists whose clients were in the TSM-FB condition received TSM and Y-OQ data. A multilevel model was created to evaluate for differences between conditions on the dependent variable. The initial randomization failed to create similar groups at intake and a statistically and clinically significant difference was detected on the Y-OQ at intake. As such, no conclusions can be drawn for hypotheses tied to the primary dependent variable. Premature termination (PT) rates were significantly lower for the TSM-FB condition when defined as attending more than one session. Contrastingly, there was no difference between conditions on PT when defining PT based on the therapist's opinion. A significant minority of therapists (40%) found that the TSM was useful for treatment planning compared to 10% which did not.

A recent study highlighted discrepancies between qualitative client self-reports of outcome and OQ-45 reports. Specifically, only 8.8% of clients who deteriorated during a course of therapy based on the OQ-45 perceived that they had deteriorated, while 50% of these clients perceived that they had improved in therapy (Top et al., 2018). This phenomenon, where different means of tracking outcomes yield divergent results, has been called “paradoxical outcome.” The trend suggests that the most advanced forms of tracking psychotherapy outcomes might not detect important facets of outcome from the perspective of psychotherapy clients. The current study is a qualitative investigation of the experience of psychotherapy clients who reported improvements in therapy despite meeting criteria for deterioration per the Outcome Questionnaire-45.2 (OQ-45; Lambert et al., 1996). We used a consensual qualitative research (CQR) protocol (Hill, 2012). CQR uses group consensus to detect themes in participant interviews. Common themes included attributing negative changes to factors outside of therapy, endorsing complicated circumstances, and reporting positive outcomes that were not well detected by the OQ-45. More results and their implications are discussed.

The OQ-45.2 (Outcome Questionnaire-45.2) is a measure of psychological distress that examines patients' emotional states and level of functioning in society. This measure was administered at admission and discharge to inpatients at a level II trauma center with in- and outpatient populations in addition to the BBHI-2 (Brief Battery for Health Improvement-2) and FIM (Functional Independence Measure). Results suggested that patients demonstrated psychological improvements from admission to discharge. In addition, the OQ-45.2 was found to correlate with nearly all subtests of the BBHI-2. Finally, diagnosis, length of stay, and number of psychotherapy sessions were not predictive of improvements on the OQ-45.2 total score, suggesting that this measure can be appropriately used on a heterogeneous medical population.

<p>SAMMANFATTNING</p><p>Under 2006 påbörjades arbetet med att nationellt höja kvaliteten på praktiskt socialt arbete. Socialstyrelsen gav då ut nya råd och riktlinjer för hur socialtjänsten kan stärka och utveckla sitt behandlingsarbete. Utifrån detta blir det allt vanligare att mer strukturerade manualer och behandlingsinstrument införs i arbetet. Ett sådant instrument är Youth Outcome Questionnaire, som under hösten 2007 implementeras i en kommunal behandlingsverksamhet i Örebro. Syftet med studien är att undersöka hindrande och möjliggörande faktorer som omgärdar implementeringen av ett behandlingsinstrument i en behandlingsverksamhet för ungdomar på institution. Syftet preciseras genom följande frågeställningar: Hur följdes det behandlingsarbetet upp innan implementeringen av instrumentet? Vad har föranlett att enheten beslutat att införa det nya behandlingsinstrumentet Y-OQ 2.0? Vilka möjliggörande eller hindrande faktorer har föregått implementeringsarbetet av instrumentet? Vilka negativa eller positiva faktorer framkommer i det praktiska handhavandet och tillämpningen av instrumentet? Studien har en kvalitativ ansats och bygger på semistrukturerade intervjuer med sex personer ur personalgruppen i den aktuella verksamheten. Tolkningsramen utgörs i huvudsak av implementeringsteori. Resultatet visar att personalen har en positiv inställning till arbetet med instrumentet. Man har inte tidigare arbetat utifrån strukturerade utvärderingsmetoder i form av instrument eller manualer och en majoritet av respondenterna ser behovet av utvärdering i verksamheten. Hindrande faktorer under implementeringen visar sig främst i att man upplever utbildningen som något bristfällig och att man inte heller fått någon handledning kring instrumentet. Detta har lett till osäkerhet kring användandet av detta liksom hur man ska använda de resultat som framkommer, vilket också visar på de praktiska svårigheter detta medfört. De möjliggörande faktorerna handlar till stor del om personalens positiva attityd och vilja att använda sig av det nya verktyget. I diskussionen förs resonemang kring implementeringens effekter på behandlingsverksamheten och instrumentets tillämpningsområde inom behandlingsarbetet.</p><p>Nyckelord: Implementering, implementeringsteori, behandlingsinstrument, Youth Outcome Questionnaire (Y-OQ 2.0), institutionsbehandling</p>

SAMMANFATTNING Under 2006 påbörjades arbetet med att nationellt höja kvaliteten på praktiskt socialt arbete. Socialstyrelsen gav då ut nya råd och riktlinjer för hur socialtjänsten kan stärka och utveckla sitt behandlingsarbete. Utifrån detta blir det allt vanligare att mer strukturerade manualer och behandlingsinstrument införs i arbetet. Ett sådant instrument är Youth Outcome Questionnaire, som under hösten 2007 implementeras i en kommunal behandlingsverksamhet i Örebro. Syftet med studien är att undersöka hindrande och möjliggörande faktorer som omgärdar implementeringen av ett behandlingsinstrument i en behandlingsverksamhet för ungdomar på institution. Syftet preciseras genom följande frågeställningar: Hur följdes det behandlingsarbetet upp innan implementeringen av instrumentet? Vad har föranlett att enheten beslutat att införa det nya behandlingsinstrumentet Y-OQ 2.0? Vilka möjliggörande eller hindrande faktorer har föregått implementeringsarbetet av instrumentet? Vilka negativa eller positiva faktorer framkommer i det praktiska handhavandet och tillämpningen av instrumentet? Studien har en kvalitativ ansats och bygger på semistrukturerade intervjuer med sex personer ur personalgruppen i den aktuella verksamheten. Tolkningsramen utgörs i huvudsak av implementeringsteori. Resultatet visar att personalen har en positiv inställning till arbetet med instrumentet. Man har inte tidigare arbetat utifrån strukturerade utvärderingsmetoder i form av instrument eller manualer och en majoritet av respondenterna ser behovet av utvärdering i verksamheten. Hindrande faktorer under implementeringen visar sig främst i att man upplever utbildningen som något bristfällig och att man inte heller fått någon handledning kring instrumentet. Detta har lett till osäkerhet kring användandet av detta liksom hur man ska använda de resultat som framkommer, vilket också visar på de praktiska svårigheter detta medfört. De möjliggörande faktorerna handlar till stor del om personalens positiva attityd och vilja att använda sig av det nya verktyget. I diskussionen förs resonemang kring implementeringens effekter på behandlingsverksamheten och instrumentets tillämpningsområde inom behandlingsarbetet. Nyckelord: Implementering, implementeringsteori, behandlingsinstrument, Youth Outcome Questionnaire (Y-OQ 2.0), institutionsbehandling

Machine intelligence techniques contribute to solving real-world problems. Reinforcement learning (RL) is one of the machine intelligence techniques with several characteristics that make it suitable for the applications, for which the model of the environment is not available to the agent. In real-world applications, intelligent agents generally face a very large state space which limits the usability of reinforcement learning. The condition for convergence of reinforcement learning implies that each state-action pair must be visited infinite times, a condition which can be considered impossible to be satisfied in many practical situations. The goal of this work is to propose a class of new techniques to overcome this problem for off-policy, step-by-step (incremental) and model-free reinforcement learning with discrete state and action space. The focus of this research is using the design characteristics of RL agent to improve its performance regarding the running time while maintaining an acceptable level of accuracy. One way of improving the performance of the intelligent agents is using the model of environment. In this work, a special type of knowledge about the agent actions is employed to improve its performance because in many applications the model of environment may only be known partially or not at all. The concept of opposition is employed in the framework of reinforcement learning to achieve this goal. One of the components of RL agent is the action. For each action we define its associate opposite action. The actions and opposite actions are implemented in the framework of reinforcement learning to update the value function resulting in a faster convergence. At the beginning of this research the concept of opposition is incorporated in the components of reinforcement learning, states, actions, and reinforcement signal which results in introduction of the oppositional target domain estimation algorithm, OTE. OTE reduces the search and navigation area and accelerates the speed of search for a target. The OTE algorithm is limited to the applications, in which the model of the environment is provided for the agent. Hence, further investigation is conducted to extend the concept of opposition to the model-free reinforcement learning algorithms. This extension contributes to the generating of several algorithms based on using the concept of opposition for Q(lambda) technique. The design of reinforcement learning agent depends on the application. The emphasize of this research is on the characteristics of the actions. Hence, the primary challenge of this work is design and incorporation of the opposite actions in the framework of RL agents. In this research, three different applications, namely grid navigation, elevator control problem, and image thresholding are implemented to address this challenge in context of different applications. The design challenges and some solutions to overcome the problems and improve the algorithms are also investigated. The opposition-based Q(lambda) algorithms are tested for the applications mentioned earlier. The general idea behind the opposition-based Q(lambda) algorithms is that in Q-value updating, the agent updates the value of an action in a given state. Hence, if the agent knows the value of opposite action then instead of one value, the agent can update two Q-values at the same time without taking its corresponding opposite action causing an explicit transition to opposite state. If the agent knows both values of action and its opposite action for a given state, then it can update two Q-values. This accelerates the learning process in general and the exploration phase in particular. Several algorithms are outlined in this work. The OQ(lambda) will be introduced to accelerate Q(lambda) algorithm in discrete state spaces. The NOQ(lambda) method is an extension of OQ(lambda) to operate in a broader range of non-deterministic environments. The update of the opposition trace in OQ(lambda) depends on the next state of the opposite action (which generally is not taken by the agent). This limits the usability of this technique to the deterministic environments because the next state should be known to the agent. NOQ(lambda) will be presented to update the opposition trace independent of knowing the next state for the opposite action. The results show the improvement of the performance in terms of running time for the proposed algorithms comparing to the standard Q(lambda) technique.

Machine intelligence techniques contribute to solving real-world problems. Reinforcement learning (RL) is one of the machine intelligence techniques with several characteristics that make it suitable for the applications, for which the model of the environment is not available to the agent. In real-world applications, intelligent agents generally face a very large state space which limits the usability of reinforcement learning. The condition for convergence of reinforcement learning implies that each state-action pair must be visited infinite times, a condition which can be considered impossible to be satisfied in many practical situations. The goal of this work is to propose a class of new techniques to overcome this problem for off-policy, step-by-step (incremental) and model-free reinforcement learning with discrete state and action space. The focus of this research is using the design characteristics of RL agent to improve its performance regarding the running time while maintaining an acceptable level of accuracy. One way of improving the performance of the intelligent agents is using the model of environment. In this work, a special type of knowledge about the agent actions is employed to improve its performance because in many applications the model of environment may only be known partially or not at all. The concept of opposition is employed in the framework of reinforcement learning to achieve this goal. One of the components of RL agent is the action. For each action we define its associate opposite action. The actions and opposite actions are implemented in the framework of reinforcement learning to update the value function resulting in a faster convergence. At the beginning of this research the concept of opposition is incorporated in the components of reinforcement learning, states, actions, and reinforcement signal which results in introduction of the oppositional target domain estimation algorithm, OTE. OTE reduces the search and navigation area and accelerates the speed of search for a target. The OTE algorithm is limited to the applications, in which the model of the environment is provided for the agent. Hence, further investigation is conducted to extend the concept of opposition to the model-free reinforcement learning algorithms. This extension contributes to the generating of several algorithms based on using the concept of opposition for Q(lambda) technique. The design of reinforcement learning agent depends on the application. The emphasize of this research is on the characteristics of the actions. Hence, the primary challenge of this work is design and incorporation of the opposite actions in the framework of RL agents. In this research, three different applications, namely grid navigation, elevator control problem, and image thresholding are implemented to address this challenge in context of different applications. The design challenges and some solutions to overcome the problems and improve the algorithms are also investigated. The opposition-based Q(lambda) algorithms are tested for the applications mentioned earlier. The general idea behind the opposition-based Q(lambda) algorithms is that in Q-value updating, the agent updates the value of an action in a given state. Hence, if the agent knows the value of opposite action then instead of one value, the agent can update two Q-values at the same time without taking its corresponding opposite action causing an explicit transition to opposite state. If the agent knows both values of action and its opposite action for a given state, then it can update two Q-values. This accelerates the learning process in general and the exploration phase in particular. Several algorithms are outlined in this work. The OQ(lambda) will be introduced to accelerate Q(lambda) algorithm in discrete state spaces. The NOQ(lambda) method is an extension of OQ(lambda) to operate in a broader range of non-deterministic environments. The update of the opposition trace in OQ(lambda) depends on the next state of the opposite action (which generally is not taken by the agent). This limits the usability of this technique to the deterministic environments because the next state should be known to the agent. NOQ(lambda) will be presented to update the opposition trace independent of knowing the next state for the opposite action. The results show the improvement of the performance in terms of running time for the proposed algorithms comparing to the standard Q(lambda) technique.

The Outcome Questionnaire (OQ-45; Lambert et al., 1996) has been widely employed as a psychotherapy outcome monitoring measure following research findings that support various aspects of its validity and sensitivity to change. Despite its broad usage in both clinical and research settings, some of its psychometric properties are not definite. The three subscales of the OQ-45 are designed to measure three distinct, but related, aspects of psychological functioning. However, neither the one- nor three-factor models have been supported by previous research. Likewise, the results of the current study supported neither of those factor structures. It was suspected that heterogeneity in data might have led to the lack of the confirmatory factor analysis model fit. Therefore, factor mixture modeling (FMM), a combination of confirmatory factor analysis and latent class analysis, was employed to investigate potential heterogeneity of the data. Among the series of factor mixture models with varying numbers of classes that were fitted, the two-class, unconditional FMM based on the revised three-factor solution was decided to best describe the data under analysis. Although three covariates of clinical status, sex, and race were selected as known sources of heterogeneity and incorporated into the FMMs (i.e., conditional model), the findings were contradictory to expectations. The implications of these findings in counseling were discussed in terms of aggregating OQ-45 scores and its score interpretation. Furthermore, this study demonstrates the process involved and dilemmas encountered in choosing the best fitting FMM. There is currently no criterion for assessing individual model fit. Instead, models’ fit are compared using various information criteria (IC). And, as was found in the current study, these ICs are frequently contradictory. Thus, the process of identifying the best fitting model cannot rest solely on fit indices but must also depend on interpretation of models and consideration of the ultimate use of the results. In the current study, consideration of transition matrices and the pattern of latent means across classes contributed as much to model selection as fit index interpretation. / text

The need for psychotherapy outcome research is growing in Japan as the societal demands for psychotherapy have increased in recent years. Although researchers in Japan recognize the importance of integrating clinical practice and empirical research in evaluating psychotherapy outcome, most Japanese studies to date have relied heavily on qualitative case studies (Haebara, 1997; Kanazawa, 2004; Tanno, 2001). With the help of six translators and 116 native Japanese pilot respondents, this study adapted the Outcome Questionnaire (OQ; Lambert et al., 1996), one of the most common quantitative measures of clinical outcome, for use in Japan. The translation of the original OQ into Japanese followed Beaton et al. (2000) to include forward translation, synthesis, back translation, and expert committee meetings. The study produced 4 pre-final versions, 2 pretests, and a pilot. With permission from the original questionnaire developers, a few items were modified to achieve cultural equivalence. The rigorous translation and adaptation processes, evaluated through the Translation Validity Index (Tang & Dixon, 2002) and Content Validity Index (Polit et al., 2007), sought semantic, content, and conceptual equivalence between the English and Japanese versions of the OQ. Study limitations and suggestions for further development of the Japanese OQ are discussed.