A recipe for increasing racial and gender disparities in HIV infection: A critical analysis of the Canadian guideline on pre-exposure prophylaxis and non-occupational post-exposure prophylaxis’ responsiveness to the HIV epidemics among women and Black communities
Bibliographic record
Abstract
Clinical guidelines have emerged as an important tool for improving healthcare quality and controlling healthcare costs. However, they also have the potential to limit access to treatment and services for those who could greatly benefit. In November 2017, a clinical guideline for prescribing HIV pre-exposure prophylaxis (PrEP) was published in the Canadian Medical Association Journal. After careful review, we determined the current Canadian PrEP guideline is not wholly inclusive and lacks sufficient sensitivity for detecting HIV seroconversion risk in African, Caribbean, and Canadian Black (Black) communities, as well as women. In this article, we present several scenarios to illustrate how the Canadian guideline for HIV PrEP compromises patient-centered HIV prevention for Black communities and women and may imperil efforts to reduce HIV incidence in these communities. As it stands, the current PrEP Canadian guideline omits the behavioural, clinical, and social factors known to contribute to HIV risk, which also disproportionately affect Black communities. We recommend that healthcare providers, who opt to consult the guideline, exercise clinical discretion, and consider relevant contextual information about the patient presenting for care. However, for a greater impact, the existing guideline should be revised or supplemented with additional recommendations to ensure equitable access for all individuals who would benefit from PrEP.
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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 | 0.043 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".