An overview of recent evidence on barriers and facilitators to HIV testing
Bibliographic record
Abstract
Background: To address the issue of undiagnosed HIV infections, the Public Health Agency of Canada released the Human Immunodeficiency Virus-HIV Screening and Testing Guide in 2012, which identified several barriers and facilitators for HIV testing.Objective: The objective of this overview is to summarize the most recent evidence regarding barriers and facilitators to HIV testing, to expand upon the research conducted for the HIV Screening and Testing Guide.Methods: A review of the literature published between 2010 and 2014 was conducted using Scopus, PubMed (MEDLINE), and the Cochrane Library; websites of groups such as the Centers for Disease Control and Prevention, European Centre for Disease Prevention and Control, Australian Department of Health, and New Zealand Ministry of Health were searched for recent reports.Studies were categorized based on the barrier or facilitator identified, and the results were summarized.Results: In addition to the known barriers of lack of perceived risk, lack of comfort or knowledge, provider time constraints, and fear of the diagnosis, stigma and discrimination, new studies have identified additional barriers including: fear regarding disclosure or lack of confidentiality, lack of access, lack of compensation of providers, and lack of human resources to carry out testing.In addition to the known facilitators of increased awareness and normalization of HIV screening and testing, opt-out testing was identified as a facilitator in recent studies. Conclusion:Since 2010, research has advanced our knowledge of barriers and facilitators and can be applied to help decrease the number of undiagnosed HIV infections.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".