Implementation challenges and opportunities for HIV Treatment as Prevention (TasP) among young men in Vancouver, Canada: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Despite evidence supporting the preventative potential of HIV Treatment as Prevention (TasP), scientific experts and community stakeholders have suggested that the success of TasP at the population level will require overcoming a set of complex and population-specific implementation challenges. For example, the factors that might influence decisions to initiate 'early' treatment have yet to be thoroughly understood; neither have questions about the factors that enhance or impede their ability to achieve long-term adherence to ARVs or the social norms regarding various treatment regimens been examined in detail. This knowledge gap may hamper opportunities to effectively develop public health practices that are informed by the various challenges and opportunities related to TasP implementation and scale up. METHODS: Drawing on 50 in-depth, individual interviews with young men ages 18-24 in Vancouver, Canada, this study examines young men's perspectives regarding factors that might affect their engagement with TasP. RESULTS: While findings from the current study indicate young men generally have a high receptiveness to TasP, our findings also identify key social and structural forces that will warrant ongoing consideration for TasP implementation. For example, participants described how an enhanced awareness regarding treatment (including awareness of the universal availability of treatment in Vancouver) would be a necessary, but not sufficient, condition to decide to endorse TasP. Their decisions about engaging in HIV care in the context of TasP (e.g., HIV testing, treatment initiation, long-term adherence) also appear to be contingent on their ability to negotiate or 'balance' the risks and benefits to themselves and others. The findings also offer insight into the complex and sometimes controversial narratives that continue to emerge regarding risk compensation practices in the context of TasP. CONCLUSION: Based on the results of this study, we identify several areas that hold promise for informing the effective scale up of TasP, including new information regarding implementation adaptation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".