Socioculturally Appropriate Internet-Based Geriatric Care Model for Older Adults Living With HIV: Experience-Based Co-Design Approach
Bibliographic record
Abstract
BACKGROUND: Older adults living with HIV face challenges accessing regular geriatric care, and while virtual care services could offer a solution, they may come with limitations. OBJECTIVE: This study aimed to co-design a culturally appropriate virtual care model tailored to older adults' needs using the experience-based co-design methodology. METHODS: We used a qualitative, experience-based co-design approach with 19 older adults living with HIV. The process involved 3 phases: identifying needs through interviews and questionnaires, codeveloping a care model prototype through focus groups and a workshop, and refining the model using feedback from a world café format. Data were analyzed using thematic content analysis. RESULTS: The co-design process led to a virtual care model prototype that directly addressed participants' key needs. These included personalized communication methods, simplified technology interfaces for easier access, and culturally responsive care practices. Participants emphasized the importance of privacy in virtual consultations, flexible scheduling to accommodate health fluctuations, and ongoing support for managing both HIV and aging-related conditions. Their feedback shaped a model designed to bridge service gaps, offering a more inclusive, accessible, and patient-centered approach to virtual geriatric care. CONCLUSIONS: This study co-designed a potential virtual geriatric care model grounded in the experiences of older adults living with HIV. By integrating participants' insights throughout the design process, the model offers a promising approach to improving care for this vulnerable population. Future directions for research to test this model are proposed.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".