GamePlan4Care, a Web-Based Adaptation of the Resources for Enhancing Alzheimer’s Caregiver Health II Intervention for Family Caregivers of Persons Living With Dementia: Formative, Qualitative Usability Testing Study
Bibliographic record
Abstract
Background: The negative consequences of caregiving can be mitigated by providing caregivers with support programs that increase their dementia care skills and provide emotional and tangible support. Web-based technology can increase the availability of evidence-based caregiver interventions. GamePlan4Care (GP4C) is a web-based adaptation of the Resources for Enhancing Alzheimer's Caregiver Health II (REACH II) intervention, redesigned and reformatted for web-based delivery. Objective: The goal of GP4C is to create a web-based family caregiver support platform that facilitates self-directed exposure to evidence-based skills training and support for caregivers of persons living with dementia. This multidimensional approach of using technology enhanced with live support has the potential for improved scalability and sustainability. In preparation for a randomized clinical trial of the new intervention, the GP4C platform underwent user interface/user experience (UI/UX) testing with caregivers as part of an iterative design process. Methods: UI/UX testing of caregivers' reactions to technical and content-related aspects of the platform was conducted with 31 caregivers recruited through partnerships with community-based organizations in central Texas. Usability testing consisted of performing system tasks, answering open-ended questions on the tasks, and providing feedback on their experience with the platform. Two researchers used an inductive thematic approach to data analysis using transcripts of individual audio and screen-recorded sessions with each participant. The analysis consisted of 3 phases: data familiarization, coding, and theme formulation. Results: In total, 18 participants tested technical-related aspects of the GP4C platform, and 13 participants tested content-related aspects. The average age of participants was 62 (SD 12.2, range 31-86). A majority of participants were female (27/31, 87.1%) and White or Caucasian (26/31, 83.1%) while almost one-third were Hispanic (10/31, 32.3%). The thematic analysis revealed 3 themes: supportive resources as a common theme, active engagement for technical aspects of the platform, and a comprehensive approach for content aspects of the platform. Participants also suggested changes in navigation and content. Conclusions: Findings from the usability testing sessions indicate that the platform provided engaging, useful content that the caregiver would continue to use, resonated with their caregiving experience, helped the caregivers think through their choices and emotions, and could be used to help communicate with the person living with dementia. Caregivers appreciated the personalization based on what they had already completed and the concept of having a Dementia Care Navigator when they needed additional help. Caregivers also provided multiple suggestions on how to improve the system, including changes for easier navigation and inclusiveness. This positive feedback indicates that with a few changes, the platform would be beneficial to meet the needs and provide resources for caregivers of persons living with dementia. The process of involving end users in usability testing during the development stage ensures that the finished tool will better meet users' expectations and current needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".