A rocky road to resiliency: An exploration of GetaKit by BlackCAP
Bibliographic record
Abstract
In Ontario, HIV diagnoses continue to remain highest among individuals who identify as gay, bisexual, or men who have sex with men (GBM), as well as persons of African, Caribbean, or Black (ACB) ethnicities. To address this, GetaKit, an internet-based service allowing for individuals to acquire a free HIV self-test (HIVST) partnered with the Black Coalition for AIDS Prevention (BlackCAP), an AIDS service organization in Toronto. As part of a larger mixed methods study, this work builds upon the quantitative data already published to explain the testing behaviors of ACB GBM. Using a focus group, this study supports what is already known about culturally sensitive health interventions to support community resiliency and liberation. However, the findings also demonstrate that there exist issues which threaten to undermine the fecundity of HIVST as a resiliency building tool, including concerns around trust, privacy, and misaligned, at times apotropaic, beliefs around the test itself.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".