Methodological Insights From an Experience-Based Co-Design Method Applied to a Study of Older Adults Living with HIV’s Perspectives on Virtual Geriatric Care
Bibliographic record
Abstract
This paper outlines the application of Experience-Based Co-Design (EBCD) to explore the perspectives of older adults living with HIV regarding virtual geriatric care. The study focuses on identifying the unique needs, challenges, and preferences of this population in the context of remote healthcare delivery models. EBCD, a participatory research method, engages service users, healthcare providers, and stakeholders in co-designing solutions to improve healthcare services. By involving participants throughout the research process, the approach ensures that resulting interventions are informed by real-world experiences, enhancing their likelihood of acceptance and effectiveness. The methodology includes in-depth interviews, focus groups, and journey mapping with older adults living with HIV to gather data. Through collaborative discussions, care gaps were identified, and key areas for improvement in virtual care were highlighted. Active participation from healthcare professionals ensured that these findings were translated into actionable solutions. Practical insights were also gained on fostering an inclusive and respectful environment for marginalized populations, ensuring that their voices were central to the co-design process. This study demonstrates that EBCD is an effective method for engaging older adults living with HIV in the design of virtual care interventions, leading to patient-centered solutions that address both clinical and psychosocial needs. Key contributions of the study include the development of a framework for applying EBCD in virtual geriatric care, identification of critical care gaps in this context, and the promotion of inclusive practices for vulnerable populations. The findings suggest that EBCD can play a significant role in advancing health equity and improving the quality of care for older adults living with HIV, especially as virtual healthcare continues to evolve.
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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 | 0.087 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".