Manuals may take a variety of forms. For example, a manual for outpatient therapy might lay out a sequence of 1-hr sessions, whereas a manual for an educational program might consist of a curriculum in which the learner has to master each step before going to the next, along with techniques for the teacher to use in delivering instruction. For an ABA intervention package, it might be organized around functional relations (e.g., a set of procedures for problem behavior that serves to escape or avoid situations, another set of procedures for behavior that serves to gain access to tangible items). The manual might present a complete script for a session or merely outline key points to cover (Frankel, 2004). It may include one or more types of supporting materials such as handouts, worksheets, videos for training interventionists or for illustrating concepts for consumers, data sheets for monitoring progress, and so on. A manual may undergo several revisions to take into account findings from initial testing of its acceptability (social validity) and usability (extent to which providers implement procedures with fidelity and recipients of the intervention adhere to the protocol). As described previously, the manual may encompass one intervention procedure or many, provided that it offers a solution to a problem presented in practice and strategies that community providers can use to implement the procedures effectively and efficiently. If the manual centers on only a few procedures, it may be possible to embed it within a research article; otherwise, it is likely to require a separate publication (Chambless & Hollon, 1998).

As another example that may be more pertinent to the customary scope of practice for behavior analysts, Baer (1975) drew on the history of a program for juvenile delinquents (Achievement Place). He suggested that research might begin with SSED studies that test the effects of a single procedure. For instance, the studies might use a multiple baseline design across participants. In this approach, all participants start in a baseline (no intervention) condition, and then the intervention procedure is introduced for one participant at a time. The next stage of research might involve testing a package of procedures (as opposed to an individual procedure) in a multiple baseline design across participants, and a subsequent stage might be to evaluate the package in a multiple baseline design across groups of participants, with the package being introduced to one group at a time. Although actual research programs do not follow such a neat progression, SSED studies that involve large groups of participants do exist (Biglan, Ary, & Wagenaar, 2000). However, such studies are equivalent to commonly used group designs. As recognized by Bailey and Burch (2002), Baer's example corresponds to a group design, referred to as a wait-list control, wherein one group starts intervention at the outset of the study and is compared to another group that is placed on a waiting list to start intervention when the first group finishes. Biglan et al. (2000) demonstrated that their example could be described as a group design called an interrupted time series study (Shadish, Cook, & Campbell, 2002).


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Despite the potential benefits of moving from SSED studies to manuals and group studies, some cautions must be acknowledged. First, there is no guarantee that this process will come to fruition. For instance, investigators reported substantial success in SSED studies on token economies to improve the behavior of individuals with schizophrenia (Paul & Lentz, 1977) and created a manual for this approach (Paul & Menditto, 1992). However, the intervention was seldom tested in larger scale studies (McMonagle & Sultana, 2009) or adopted in practice (Paul & Menditto, 1992). Commentators debate whether the intervention languished because investigators and providers hold distorted views about it (Wong, 2006) or because the data are less favorable than proponents claim (Wakefield, 2006). Regardless, the implication is that research and dissemination may stall even for interventions that appear to be promising.

Running a closed feedback loop computer vision quality inspection application (Level 200)

 Defect and anomaly detection in the quality inspection is a vital step to ensure the quality of the products, as timely detection of faults or defects and taking appropriate action often incur significant operational and quality-related costs. In addition, manual feedback loops are often subjective, time consuming and difficult to scale, resulting in production bottlenecks and slows down time to market. In this session, we share how you can build a robust, effective, and scalable closed loop quality inspection at the edge, generate objective decisions with the quick feedback loop and reduce quality related costs.


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