Understanding barriers and facilitators to HIV testing in Canada from 2009–2019: A systematic mixed studies review
Bibliographic record
Abstract
BACKGROUND: HIV testing is a core pillar of Canada's approach to sexually transmitted and blood-borne infection (STBBI) prevention and treatment and is critical to achieving the first Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 target. Despite progress toward this goal, many Canadians remain unaware of their status and testing varies across populations and jurisdictions. An understanding of drivers of HIV testing is essential to improve access to HIV testing and reach the undiagnosed. OBJECTIVE: To examine current barriers and facilitators of HIV testing across key populations and jurisdictions in Canada. METHODS: A systematic mixed studies review of peer-reviewed and grey literature was conducted identifying quantitative and qualitative studies of barriers and facilitators to HIV testing in Canada published from 2009 to 2019. Studies were screened for inclusion and identified barriers and facilitators were extracted. The quality of included studies was assessed and results were summarized. RESULTS: Forty-three relevant studies were identified. Common barriers emerge across key populations and jurisdictions, including difficulties accessing testing services, fear and stigma surrounding HIV, low risk perception, insufficient patient confidentiality and lack of resources for testing. Innovative practices that could facilitate HIV testing were identified, such as new testing settings (dental care, pharmacies, mobile units, emergency departments), new modalities (oral testing, peer counselling) and personalized sex/gender and age-based interventions and approaches. Key populations also face unique sociocultural, structural and legislative barriers to HIV testing. Many studies identified the need to offer a broad range of testing options and integrate testing within routine healthcare practices. CONCLUSION: Efforts to improve access to HIV testing should consider barriers and facilitators at the level of the individual, healthcare provider and policy and should focus on the accessibility, inclusivity, convenience and confidentiality of testing services. In addition, testing services must be adapted to the unique needs and contexts of key populations.
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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.033 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".