Co-Creating Socio-Culturally-Appropriate Virtual Geriatric Care for Older Adults Living With HIV: A Community-Based Participatory, Intersectional Protocol
Bibliographic record
Abstract
The aging cohort of persons living with human immunodeficiency virus (HIV) in Canada has reached a critical point, with nearly half now 50 years age or older. Older persons living with HIV have specific needs which can be effectively addressed by geriatric specialists. However, the recognition of HIV care as a domain of geriatrics is recent, resulting in a lack of clinical recommendations and modern care models for delivering geriatric care to this population. Virtual care has been demonstrated to reduce existing barriers to accessing HIV care in some populations but before it can be adapted to geriatric HIV care a critical first step is to acknowledge and understand disparities in socioeconomic circumstances, technology access and ability and cultural differences in experiences. This protocol marks the initial step in a comprehensive program of research aimed at co-designing, implementing, and evaluating culturally-appropriate virtual geriatric care for diverse older adults living with HIV. The study employs qualitative methods with older adults living with HIV to lay the groundwork, to inform the future development of a virtual model of geriatric care. We will explore the perspectives of diverse groups of older persons with HIV on (1) The value and necessity of culturally-tailored virtual interventions for geriatric HIV care; and (2) Recommendations on how best to engage older persons with HIV in the future co-design of a virtual model of geriatric HIV care. Ultimately, a more culturally-appropriate approach to care will foster a more inclusive and supportive healthcare system for all individuals affected by HIV including those who are aging. Researchers can utilize this research protocol to employ qualitative co-design and participatory methods with diverse older adults living with HIV.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
| opus | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: yes | Qualitative | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".