Delivering COVID self‐tests through GetaKit.ca: Creating testing access during a pandemic
Bibliographic record
Abstract
OBJECTIVES: To determine the real-world outcomes associated with using the GetaKit.ca website to distribute COVID self-tests to persons with risk factors, with a focus on facilitating testing for persons who are Black, Indigenous, or of Colour (BIPOC). METHODS: GetaKit was an open cohort observational study to distribute free self-tests, starting with HIV self-testings and then adding the Lucira Check-It® COVID self-test. Participants would register on our website and complete a risk assessment, which would calculate their need for each type of testing. RESULTS: Focusing on the COVID self-tests, from September to December 2021 (with targeted outreach in winter 2022), we distributed 6474 COVID self-tests to 3653 persons through 4161 unique orders, of which 47% came from BIPOC participants. Compared to white participants, BIPOC participants were more likely to have been a contact of COVID but less likely to be vaccinated. As well, 69% of results were reported back via the GetaKit.ca website, with 5.3% of these being positive. The positivity rate for our 3653 participants was 9.6%. CONCLUSIONS: Delivering COVID self-tests via our website provided tests to BIPOC communities and yielded positivity rates that matched local COVID testing centres. This highlights the utility of such systems for delivering testing during future pandemics.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".