What do I want my providers to know? Co-designing education with older adults living with HIV
Bibliographic record
Abstract
OBJECTIVES: To co-design an educational resource for primary healthcare providers in partnership with older adults living with HIV, with the aim of addressing key gaps in provider knowledge and enhancing the delivery of holistic, person-centered care. METHODS: A qualitative descriptive study using a co-design methodology was conducted with 12 older adults (aged ≥50) living with HIV in Ontario, Canada. Participants were recruited through purposive sampling to reflect diverse lived experiences. An in-person, arts-based workshop included patient experience mapping, small group discussions, and creative artmaking to explore healthcare experiences and education priorities. Qualitative analysis was iteratively integrated into the co-design process, informing the development of a provider-facing educational pamphlet, which underwent iterative refinement based on feedback from participants and external primary care and geriatric providers. RESULTS: Participants emphasized the need for long-term, HIV-informed care that affirms identity, fosters trust, and addresses fragmented care. Six core domains emerged as priorities for provider education: physical health, emotional health, mental health, social connection, spiritual well-being, and community support. Participants also highlighted the importance of addressing stigma, promoting cultural humility, and recognizing the cumulative impacts of aging with HIV. The final educational pamphlet used a circular, rainbow-themed design to reflect the interconnectedness of these domains and the diversity of the community. The resource was well-received by participants and external clinical reviewers for its clarity, relevance, and patient-centered approach. CONCLUSIONS: Co-designing education with older adults living with HIV resulted in a resource that reflects their lived experiences and care priorities. This approach demonstrates how participatory methods can bridge gaps between patient needs and provider practice. PRACTICE IMPLICATIONS: This resource may improve provider awareness and empathy, inform training curricula, and promote inclusive care practices. Implementation in primary care settings is planned, along with future evaluation of its impact on provider knowledge and patient experience.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".