Obstacles and Pathways on the Journey to Access Home and Community Care by Older Adults Living With HIV/AIDS in British Columbia, Canada: Thrive, a Community-Based Research Study
Bibliographic record
Abstract
Older adults living with HIV (OALHIV) (i.e., age ≥50) now constitute over 50% of all people accessing HIV treatment in British Columbia (BC), Canada. As OALHIV age, the need for supportive care in non-acute settings, including home and community care (HCC), is increasing. The Thrive research project was co-created alongside OALHIV in BC to support people to thrive with a good quality of life (as contrasted with just surviving). Phase 1 of the project linked treatment and demographic records for 5603 OALHIV accessing care in BC. Phase 2 took a community-based research approach with semi-structured interviews to understand obstacles and pathways experienced by 27 OALHIV in accessing HCC. This article summarizes previously published Phase 1 findings and explores Phase 2 findings in-depth. On the HCC journey traveled by OALHIV in BC, there are four main junctures at which obstacles and pathways appear: (1) before referral, (2) during the referral process, (3) at the assessment, and (4) while receiving services. Obstacles are largely related to fluctuating HCC priorities and funding cuts tied to election cycles, requiring systemic and policy changes to enable positive outcomes and impacts in the provision of HCC services. These obstacles can be transformed into pathways through public policy and client-centered, culturally safe care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".