Use of GetCheckedOnline and testing through healthcare providers among repeat users of British Columbia's digital testing service for sexually transmitted and blood-borne infections: Findings from a cross-sectional survey
Bibliographic record
Abstract
Background: Digital testing services for sexually transmitted and blood-borne infections (STBBI) are becoming more common in Canada. There is little evidence supporting the assumption that these services reduce healthcare system burden. To explore this further, we described patterns of provider-based testing among repeat users of a digital STBBI testing service, and their association with access barriers. Methods: We conducted a cross-sectional survey in November 2022 of repeat GetCheckedOnline.com users (≥2 tests, with 1 test between April and October 2022). We stratified participants into three use patterns of GetCheckedOnline for testing, using ordinal logistic regression to examine associations with barriers reflecting availability, accessibility, acceptability and appropriateness of health services (applying weights to adjust for non-responders). Results: Of 798 participants (17.2% of 4633 eligible), 52.6% only and 35.8% mostly tested through GetCheckedOnline; 14.5% tested more often/equally through healthcare providers. Availability was associated with greater use of GetCheckedOnline (e.g., not having a primary care provider, OR 2.03, 95% CI [1.52-2.73]), and appropriateness with lower use (getting tested part of clinical care, OR 0.07 95% CI [0.05-0.11]). Participants < 25 years, high school educated or less or born outside Canada reported greater use of GetCheckedOnline for testing while 2S/LGBTQ+ and full-time employed participants reported lower use. Most participants (88.0%) would have tested through a provider if GetCheckedOnline were not available. Conclusion: GetCheckedOnline use was associated with barriers to the availability of provider-based testing. Digital STBBI testing services may improve access to testing and reduce demands on healthcare providers for testing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".