Increasing Rates of HIV in Older Adults: Contributing Factors and Possible Interventions
Bibliographic record
Abstract
The number of adults over 50 years of age living with HIV is on the rise in North America (Roberson, 2018). There are two reasons for the growth in this demographic. First, people with HIV are living longer lives due to treatment options, like Highly Active Antiretroviral Therapy (HAART), which makes HIV a chronic, manageable condition. Secondly, the number of new HIV infections is increasing in older adults (Bourgeois et al., 2016). In Canada in 2015, approximately 24% of new diagnoses were of adults over 50 years old and the new cases in this age group have been steadily increasing over the last five years (Bourgeois et al., 2016). My research was guided by the question: why is the incidence rate of HIV in older adults increasing? I argue the two areas of greatest concern are: (1) the lack of HIV prevention programs aimed at older adults; and (2) a lack of discussion of older adult sexuality. Older adults are sexually active even into their 90s (Lindau et al., 2006), and represent a significant at-risk population for contracting HIV. Healthcare providers represent one of the greatest possible areas for improvement to accessible sexual health and HIV prevention information for adults over 50 (Davis et al., 2016). The literature review for this JCURA poster acted as formative research for my Honours project and will inform my future research at the graduate level.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".