The nature of work and organizations is changing. Over three decades ago, Terreberry (1968) observed that future organizational environments would evolve to become increasingly turbulent and unpredictable. As we prepare to enter the next millennium, that future is upon us. The dynamics and uncertainty associated with the constellation of external forces that impact organizations create pressures for innovation, flexibility, and adaptability. Adaptability as an individual, team, and organizational capability is increasingly critical to effectiveness.The increasing rate of technological innovation, and the penetration of technology into all aspects of organizational operations, is changing the nature of work (Howell & Cooke, 1989). Work is increasingly complex, placing a high emphasis on cognitive skills and specialized expertise. It is increasingly mediated by computerized systems that link distributed experts together. From air travel, to medical diagnostics, emergency response, engineering design, and command and control, among many others, organizational functions that range from the mundane to esoteric are all dependent on teams of distributed experts operating via complex systems. Although teams are often co-located in time and space, the increasing connectivity of technology systems facilitates the creation of far-flung virtual teams that interact dynamically, but never actually meet face to face (Bell & Kozlowski, 1998).There are economic pressures for improved efficiency, cost-control, and effectiveness. Traditional training is typically conducted off-site. A substantial portion of training costs --upwards of 80% --is devoted to simply getting trainees to the site, maintaining them while there, and absorbing their lost productivity. Because off-site training is often decontextualized --that is, abstracted from the work setting --it also incurs potential problems of skill transfer that impede effectiveness.This convergence of forces --environmental, technological, and economic --is driving a reconceptualization of the nature of training systems. Training is shifting from an inefficient, time consuming, and expensive enterprise to one that can be delivered efficiently, as needed, and just-in-time. It is shifting from an off-site single episode to a systematic series of learning experiences that are integrated in the workplace and embedded in work technology. It is shifting from a primary emphasis on retention and reproduction to a broader emphasis that also includes the development of adaptive knowledge and skills (Kozlowski, 1998). Training will not be a separate activity, but a continuous activity that is an integral part of the workplace and its systems. This reconceptualization of training systems is highlighted by three key terms in our title which constitute the theoretical and application focus of this chapter:$ Integrate --to form, coordinate, or blend into a functioning or unified whole; $ Embed --to enclose closely; to make something an integral part of; $ Adapt --to make fit; implies a modificati...

BackgroundPatient and Public Involvement (PPI) in mental health research is increasing, especially in early (pre-funding) stages. PPI is less consistent in later stages, including in analysing qualitative data. The aims of this study were to develop a methodology for involving PPI co-researchers in collaboratively analysing qualitative mental health research data with academic researchers, to pilot and refine this methodology, and to create a best practice framework for collaborative data analysis (CDA) of qualitative mental health research.MethodsIn the context of the RECOLLECT Study of Recovery Colleges, a critical literature review of collaborative data analysis studies was conducted, to identify approaches and recommendations for successful CDA. A CDA methodology was developed and then piloted in RECOLLECT, followed by refinement and development of a best practice framework.ResultsFrom 10 included publications, four CDA approaches were identified: (1) consultation, (2) development, (3) application and (4) development and application of coding framework. Four characteristics of successful CDA were found: CDA process is co-produced; CDA process is realistic regarding time and resources; demands of the CDA process are manageable for PPI co-researchers; and group expectations and dynamics are effectively managed. A four-meeting CDA process was piloted to co-produce a coding framework based on qualitative data collected in RECOLLECT and to create a mental health service user-defined change model relevant to Recovery Colleges. Formal and informal feedback demonstrated active involvement. The CDA process involved an extra 80 person-days of time (40 from PPI co-researchers, 40 from academic researchers). The process was refined into a best practice framework comprising Preparation, CDA and Application phases.ConclusionsThis study has developed a typology of approaches to collaborative analysis of qualitative data in mental health research, identified from available evidence the characteristics of successful involvement, and developed, piloted and refined the first best practice framework for collaborative analysis of qualitative data. This framework has the potential to support meaningful PPI in data analysis in the context of qualitative mental health research studies, a previously neglected yet central part of the research cycle.

Key features differentiate recovery colleges from traditional services, including an empowering environment, enabling relationships, and growth orientation. Service users who lack confidence, those with whom services struggle to engage, those who will benefit from exposure to peer role models, and those lacking social capital may benefit most. As the first testable characterization of mechanisms and outcomes, the change model allows formal evaluation of recovery colleges.