Addressing treatment and care needs of older adults living with HIV who use drugs
Bibliographic record
Abstract
INTRODUCTION: Older adults living with HIV (OALHIV; ≥50 years) who use drugs face unique needs and challenges that compromise their health and wellbeing due to the structural and environmental barriers they experience, in addition to being disproportionately affected by comorbidities. Nevertheless, research on this population is limited and work is needed to tailor and optimize their care and services. The purpose of this commentary is to address the key research gaps pertaining to OALHIV who use drugs. DISCUSSION: We identified four key research gaps specific to OALHIV who use drugs. Gap 1: Increased understanding of how older adults manage HIV alongside comorbidities in the context of substance use is critical to optimize their care management. Gap 2: More information on the geriatric characteristics of OALHIV who use drugs and the need and role of harm reduction in geriatric care is necessary for the provision of appropriate and effective care. Gap 3: Greater knowledge around the adoption of harm reduction and case manager approaches in various care facilities is essential to ensure equitable access to care for OALHIV who use drugs. Gap 4: Improved understanding of barriers to high-quality palliative care among OALHIV who use drugs is important to enhance quality of life across their life course. CONCLUSIONS: Addressing the identified gaps in literature will lead to a more fulsome understanding of the issues encountered by OALHIV who use drugs and inform the development and implementation of strategies that address disparities at the intersection of HIV, substance use and ageing.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".