Care of Persons With Dementia in Their Environments (COPE) in Programs of All-Inclusive Care of the Elderly (PACE)

Summary

Participation Requirements

Description

PHASE 1a - The Online Training Program - We will develop ten self-paced online learning modules. These modules will enable OTs and RNs to participant to have anytime/anywhere access to content and activities to aid their learning. The modules will include rich multimedia content and interactive asse...

PHASE 1a - The Online Training Program - We will develop ten self-paced online learning modules. These modules will enable OTs and RNs to participant to have anytime/anywhere access to content and activities to aid their learning. The modules will include rich multimedia content and interactive assessments to keep the learner engaged. The modules will allow for easy packaging of the content into the latest interoperability standards for such content including the latest Shareable Content Object Reference Model (SCORM) specifications, which will allow for repurposing and sharing with other institutions. To accommodate diversity of learning needs, the modules will be designed using a hyperlearning model with four dimensions. The general principles will begin with the module learning objectives and follow with a review of core concepts and required and/or self-directed learning activities. The mini-lecture component of the modules will include information on the major concepts of the module. Since the modules will be self-paced, the learner can take his/her time going through them and perform in the embedded interactive learning activities. The clinical reasoning dimension will provide the learner with an opportunity for problem-solving and clinical decision-making. This dimension will contain vignettes and case studies with questions requiring analysis and synthesis. The final dimension will be evaluation/ assessment of learning outcomes. This dimension will use teacher-made and standardized pre-and post-tests to assess attainment of specified learning outcomes. The self-paced modules will be highly interactive featuring integrated multimedia content, assessments, and learner evaluations to allow PACE staff to engage with the content at a high level and practice application in simulated scenarios. Each module will require approximately 45-60 minutes/module for the learner to complete. Participants can use the modules separately at different times throughout a training curriculum or they can be assigned at the beginning of a training time by having this information front-loaded. To develop the modules, we will work intensely in year 01 with an instructional design team at Drexel University with specialists in dementia care, the COPE program and experts in simulation, use of standardized patients, and training of nurses and other health professionals from Penn, Trinity Health and Jefferson. We anticipate the modules to contain the following content: module 1 - introduction to COPE program, research evidence, and core principles underlying the program; module 2 - overview of delivery characteristics, role of RN and OT, three phases (assessment, implementation, generalizability) of the COPE program, permissible adaptations; module 3 and 4-assessment phase, introduction to clinical interview and all assessments and forms; module 5 and 6- implementation phase including helping caregiver identify 3 problem areas, engaging in problem solving and brainstorming, developing and providing an assessment report and offering prescriptions (strategies) for each identified problem area; module 7 and 8 - generalizability phase or helping caregivers use strategies for one problem area to address another and planning for the future; module 9- developing rapport and working with family caregivers from different backgrounds, cultures, living environments and relationships and helping families balance caregiving with other life roles, adjusting approach by level of readiness; module 10 - challenging cases, motivational interviewing, how to explain the program, how to meet caregivers where they are at and provide validation and support. Scripts for each module will be developed and shared with OTs/RNs who are not part of the study but work within Trinity PACE programs. This will allow for continuous feedback loops to assure that the scenarios meet the needs of PACE staff. We will compare the online program to our traditional 3-day face-to-face training currently used with COPE. The 3-day training program will be conducted by Dr. Piersol using a slide deck and case presentations as we have previously done. The comparison of the two training programs is described in Phase 2 and 3 below. PHASE 1b -The Fidelity Monitoring Program- We seek to develop a scalable approach to assess fidelity to the COPE Program when it is implemented in a real-world setting such as PACE using computational linguistics techniques (e.g. natural language processing). The essence of fidelity to the Core Principles of COPE program will be captured by using automatic classification programs that evaluate both the content that should be included in COPE sessions, and the style of delivery. While automatic classification programs have been applied to measure quality metrics of transcribed narratives in the field of psychotherapy (21), it has not been used to measure other aspects of quality- namely fidelity to evidence-based practices or dementia care and caregiver supportive programs. The development of the automated Fidelity Monitoring Program will occur in three steps and will be carried out by a technical team consisting of an expert in content analysis, Dr. Ani Nenkova, and a consultant expert in speech recognition and prosody, Dr Mari Ostendorf. Co- I Nenkova has worked extensively on automatic summarization, evaluation of automatic summarization and readability and linguistic style. The ultimate goal of our efforts is to develop a system that- given a recording of a COPE delivery session (e.g. in real time immediately after interaction between the clinician and the caregiver)- produces a three-tiered score, indicating if the fidelity was 'excellent', 'acceptable' or 'problematic'. Special emphasis will be given to the accuracy of identifying 'problematic' COPE fidelity which is not fateful to training and may not produce the same desired outcomes as intervention delivered with higher fidelity. First, we will obtain n-best list speech recognition of the COPE interaction . This will help mitigate recognition errors in the next stage. Until recently, audio recording transcription was fraught with challenges particularly in sessions involving two or more speakers. Advances in audio signaling and speech recognition have brought technology for automating language analysis within reach. Recent research has suggested that text based features may be more effective than using audio features alone when classifying fidelity in behavioral research (47). Automatic speech recognition software will be used to transcribe sessions and the resulting words will be used in a text-based model of fidelity. All COPE training, practice and implementation sessions will be audio taped with participant consent. There are several of automatic speech recognition tools that we can use. We will pick the one that best balances accuracy of recognition in our domain and privacy.Once the transcripts are obtained, there are two approaches that we will develop and contrast: (1) comparison with a reference delivery and (2) a supervised classification approach. The first has the advantage of needing only a small number of excellent deliveries and several acceptable deliveries, for each of the seven dimensions, while the other needs a larger set of labeled data but would potentially lead to higher accuracy of prediction. Comparison or similarity to reference (Steps A and B): Our approach will leverage techniques widely used in the evaluation of automatically produced content, such as machine translation and automatic text summarization. In these applications it is not feasible to track system improvement with human judgments of quality. Instead, most of the progress is measured by computing similarity between a set of sample reference text (i.e. what a 'good' translation or a 'good' summary would be) and the system output. Such automatic evaluation approaches are widely used for machine translation (48) and summarization (49). While there have been some concerns that the automatic measures are not fine enough to distinguish between levels of very good context, these measures show strong ability to distinguish 'very bad' content (50,51), (or poor fidelity) aligns with the needs of our project. In Step A we will compile 10 examples of 'excellent' COPE fidelity deliveries, with the regions where desired aspects is expressed will be explicitly marked, i.e. marked parts where person-environment fit is discussed, or parts of the interaction where the clinician asks open ended questions or confirms the caregiver understanding of the content. Next, new interactions will be ranked by their similarity with the reference 'excellent' interactions (52). In Step 2 we will identify 'problematic' fidelity deliveries. We will make use of 10 negative examples of 'acceptable' but imperfect deliveries. If the new interaction ranks lower than most of these, it will be considered unacceptable or 'problematic'. Parameters and decision rules will be developed at this step to determine cut-off levels for declaring an interaction problematic. Determination of "excellent" versus "problematic" fidelity ratings of the audio recordings will be assessed by Drs Hirshman and Renz (who were not the original developers of COPE and thus they offer an independent review) using the COPE Adherence Scale developed for the original clinical trial in order to evaluate the extent to which core treatment principles were implemented effectively. Ratings from the Adherence Scale have been standardized such that 1.00 represents perfect fidelity and 0.00 represent complete non-fidelity. The scale was previously modeled off of the NIH REACH I and II fidelity approach. Findings from the combined fidelity ratings will be used to refine the automated fidelity program into a "best model" to be tested in Step C (53). Supervised classification (Step C) For this approach, we will need to examine all recorded sessions (600+). We will train a supervised classifier or a regression model to predict the score (1 to 3, corresponding to excellent, acceptable and problematic) of a given interaction. We will experiment with a number of classifiers, including deep learning frameworks and more traditional support vector machine and (logistic) regression models.The resulting final validation set of 100 labeled interactions will serve to finalize the best model for fidelity prediction. PHASE 2 - Evaluation of Online Training Program in Interventionist Uptake and Fidelity Phase 2 of this study involves a series of activities designed to evaluate the whether an online training program is the same or better in improving interventionist uptake of- and fidelity to- COPE principles and protocols compared to a high intensity face-to-face traditional form of training. PHASE 3 (Aim 3) - Efficacy of COPE on PACE participant outcomes by type of COPE training. This aim will be accomplished by evaluating dyad outcomes of the COPE program under the two different training approaches. Following training, each of the PACE organizations will enroll 5 persons with dementia and their caregivers in the study. This will yield 50 family dyads (25 dyads in traditional training sites and 25 dyads in online training sites).

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