Getting Your Data Into SSDforR

This chapter covers how to measure target behaviors and use common software to record and edit client data. Readers are then shown how to import data into R and use the SSD for R functions to analyze their data. The first part of this chapter focuses on the type of data that is most appropriate to record and some common issues related to collecting these. Four different types of measurement are covered, each of which has its own strengths and weaknesses. These include direct behavioral observations, standardized scales, individual rating scales, and logs. When selecting one or more methods of measuring a target behavior, readers will want to consider the specific needs of their clients, the identified problem, and the practice or research situation. The second part of this chapter demonstrates how to use Excel or other spreadsheet programs to quickly and effectively record this data.

Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving

The runtime behavior of Simulated Annealing (SA), similar to other metaheuristics, is controlled by hyperparameters. For SA, hyperparameters affect how “temperature” varies over time, and “temperature” in turn affects SA’s decisions on whether or not to transition to neighboring states. It is typically necessary to tune the hyperparameters ahead of time. However, there are adaptive annealing schedules that use search feedback to evolve the “temperature” during the search. A classic and generally effective adaptive annealing schedule is the Modified Lam. Although effective, the Modified Lam can be sensitive to the scale of the cost function, and is sometimes slow to converge to its target behavior. In this paper, we present a novel variation of the Modified Lam that we call Self-Tuning Lam, which uses early search feedback to auto-adjust its self-adaptive behavior. Using a variety of discrete and continuous optimization problems, we demonstrate the ability of the Self-Tuning Lam to nearly instantaneously converge to its target behavior independent of the scale of the cost function, as well as its run length. Our implementation is integrated into Chips-n-Salsa, an open-source Java library for parallel and self-adaptive local search.

The Use of Disability Interactive Media to Improve Beginning Reading for Children with Multiple Disabilities Class III SDLB In SLB Agrowisata Shaleha Panjalu

This research is based on the problem of lack of the ability to read the beginning, the ability to recognize all the letters of the alphabet has not been felt, and has not been able to incorporate letters into syllables and words in children with multiple disabilities. Research aims to improve the ability to read the beginning of children with multiple disabilities with target behavior, namely the ability to read syllables and word reading ability. The subject of this study was a child with a multiple disabilities of class III SDLB in SLB Agrowisata Shaleha Panjalu. The method used is Single Subject Research with A-B-A research design. The processed Data is presented in a simple descriptive statistical form through graphs and percentage values. The results showed a significant increase in the ability to read the beginning of research subjects using Disability Interactive Media with the evidence that the turnover of the mean level value of the behavior of the syllable reading ability the baseline-1 (A1) phase gets an average value of 50%, the intervention phase (B) Gets an average value of 86.8% and the baseline-2 phase (A2) Gets an average value of 78.5%. Then a comparison of the mean level of the second target behavior, i.e. the ability to read word between baseline-1 (A1) Gets an average value of 33%, the intervention phase (B) Gets an average value of 81.37% and the baseline-2 phase (A2) Gets an average value of 75%.

The Regression Trap: Why Regression Analyses Are Not Suitable For Selecting Determinants To Target In Behavior Change Interventions

When developing behavior change interventions in a systematic way, an important step is to first identify determinants relevant to a target behavior. Subsequently, limited resources create a need to select those determinants that are most relevant to this behavior. Regression analyses are commonly used for selecting determinants, but the aim of this article is to explain why regression analyses are not suitable for this purpose (i.e., the regression trap). This aim is achieved in three ways. First, by providing a theoretical rationale based on overlap between determinants. The meaning of regression weights is commonly explained as expressing the association between a determinant and a target behavior 'holding all other predictors constant.' We explain that this often boils down to 'neglecting a part of human psychology.' Second, by providing mathematical proof based on the formulas that are used to calculate regression weights. We demonstrate that the interpretation of regression weights is distorted by correlations between determinants. Third, by providing examples based on real-world data to demonstrate the impact this has in practice. This results in interventions targeting determinants that are less relevant and, thereby, have less impact on behavior change. In closing, we discuss a possible solution to circumvent the regression trap.

Risk Area 4: Managing Challenging Behaviors

This chapter describes one of the five risk areas, namely, managing challenging behaviors. The chapter begins with a description of the ABC Process of Behavior Management. Learning this process will help clinicians structure the development of individualized Action Plans and target behavior with the greatest likelihood of success. The chapter also includes instructions to format Challenging Behavior Action Plans as well as guidelines for presenting these Action Plans to the caregiver. The chapter ends with tips, reminders, and advice for clinicians on working with caregivers to reduce and manage challenging behaviors of the person with dementia.

Supporting Occupational Physicians in the Implementation of Workers’ Health Surveillance: Development of an Intervention Using the Behavior Change Wheel Framework

Workers’ health surveillance (WHS) is an important preventive activity aimed at prevention of work-related diseases. However, WHS is not regularly implemented in some EU-countries. As occupational physicians (OPs) have to play an important role in implementation of WHS, this study aimed to develop an intervention to support OPs in implementation of WHS. The behavior change wheel framework (BCW) was used to develop the intervention. First, the problem was defined, and target behavior was selected by using results from a survey study among OPs. Subsequently, change objectives in target behavior were specified. Finally, appropriate intervention functions, behavior change techniques, and modes of delivery were identified to develop the intervention. Target behaviors were (1) OPs initiate WHS, and (2) OPs conduct preventive consultations with workers. OPs’ capabilities, and experienced opportunities were identified as change objectives. Intervention functions (education, training, enablement) and behavior change techniques (information about consequences, demonstration, instructions, behavioral practice, feedback on behavior, goal setting, action planning, reviewing goals) were selected to develop the intervention, delivered by face-to-face group training and e-learning. The proposed intervention consists of training and e-learning to support OPs in implementing WHS. Feasibility and effect of the intervention will be evaluated in future studies.

How Do People Experience and Respond to Social Control From Their Partner? Three Daily Diary Studies

Positive and negative forms of social control are commonly used to regulate another person’s health-related behaviors, especially in couples. Social control efforts have been shown to result in desirable, but also undesirable effects on different outcomes. Little is known for which outcomes, when, and under which contextual conditions these different effects unfold in people’s everyday lives. Using the dual-effects model of health-related social control, we predicted that same-day and previous-day positive social control would result in desirable effects on target behavior, and same-day positive control on affect. Same-day and previous-day negative control was assumed to result in undesirable effects on reactant responses (i.e., doing the opposite of what the partner wanted and hiding the unhealthy behavior), and same-day negative control on affect. Further, we explored whether it makes a difference if one or both partners intend to change their health behavior. Three daily diary studies addressed these questions for smoking (Studies 1 and 2), and physical activity (Study 3). Receiving more positive control related to more desirable target behavior, and feeling better; more negative control was associated with more reactant responses and feeling worse. Social control unfolded its effects within 1 day, but hardly across days, indicating that control and its reactions to it are fast-acting processes in daily life. The pattern of results were the same for couples with one and both partners intending to change their behavior. Further, results replicated when using partner-reported provided control. Based on these results, social control cannot be unanimously recommended as a behavior change strategy in couples. Future studies should follow up on dyadic and temporal dynamics of social control in couples’ everyday lives in different contexts.

Interdependent Group Contingencies

Interdependent group contingencies involve a common consequence based on the behavior of all members of the group, as groups may have to meet a behavioral criterion together or all group members must meet a criterion to access a consequence. Interventions such as the Good Behavior Game, Peer Reporting Interventions, and mystery motivators are examples of interdependent group contingencies that utilize peer influence to improve appropriate behaviors in classrooms. This chapter discusses implementation issues with group contingencies, including target behavior selection, classroom training, addressing group sabotage, and managing contingencies. This chapter also discusses considerations of diversity and equity within interdependent group contingencies.