Popularity of HIV self-tests may say more about the state of our primary care system than about the device itself
Bibliographic record
Abstract
Background: In Canada, HIV transmission continues to disproportionately affect the same communities of gay men, bisexual men and men who have sex with men (gbMSM); members of African, Caribbean or Black communities (ACB); people who use injection drugs; Indigenous people; and women who belong to the aforementioned groups. While primary care is an ideal location for HIV testing for members of these groups, many people do not have access to such healthcare services. In response, we launched GetaKit to distribute HIV self-tests. Methods: In light of reduced access to healthcare services as a result of the pandemic and in anticipation of Health Canada's approval of an HIV self-test, a clinician-scientist research team at the University of Ottawa developed GetaKit: an online platform to provide access to sexual health services. When GetaKit first launched in Ottawa in July 2020 with funding from the Ontario Ministry of Health, its objectives were to ensure that access to the newly approved device remained 1) clinically appropriate, 2) accessible and 3) linked to care. Results: Over the course of the study, there were a stable number of individuals who reported having never been tested for HIV before. These individuals tended to be younger and more likely to be members of racialized minority groups; similar characteristics to those who also face the most barriers to primary care access. Conclusion: With new reports indicating that nearly six million Canadians are without a primary care provider, it was proposed that the popularity of the HIV self-test may tell more about this lack of access than about the utility of the device itself. While projects like GetaKit should be part of the broader strategy to overcome historic testing barriers, such as geographic distance and inconvenient clinic hours, it is important that this occurs in an environment where a strong primary care health system can support treatment, follow-up and specialist referrals, as required.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".