Differences in experiences of barriers to STI testing between clients of the internet-based diagnostic testing service GetCheckedOnline.com and an STI clinic in Vancouver, Canada
Bibliographic record
Abstract
OBJECTIVES: Internet-based STI testing programmes may overcome barriers posed by in-clinic testing, though uptake could reflect social gradients. The role these services play in comparison to clinical testing services is unknown. We compared experiences of testing barriers between STI clinic clients to clients of GetCheckedOnline.com (GCO; where clients take a printed lab form to a lab). METHODS: Our 10-month cross-sectional study was conducted after GCO was promoted to STI clinic clients and men who have sex with men (MSM). Clinic and GCO clients completed an online survey assessing testing barriers and facilitators; responses were compared using bivariate analysis (level of significance P<0.01; significant results below). RESULTS: Compared with 321 clinic clients, the 73 GCO clients were more likely to be older (median 35 vs 30 years), MSM (45% vs 16%), be testing routinely (67% vs 39%), have delayed testing for any reason (76% vs 54%) and due to clinic distance (28% vs 9%), report delays due to wait times (50% vs 17%), embarrassment with testing (16% vs 6%), discomfort discussing sexual health where they usually go for testing (39% vs 22%), as well as discomfort discussing sexual history with (19% vs 5%) and fearing judgement from (30% vs 15%) any healthcare provider. GCO clients were less likely to have found clinic hours convenient (59% vs 77%) and clinic appointments easy to make (49% vs 66%), and more likely to report long wait times (50% vs 17%). We found no differences in technology skills/use. CONCLUSIONS: In this urban setting, an internet-based testing service effectively engaged individuals experiencing testing barriers, with few social gradients in uptake. While some testing barriers could be addressed through increasing access to clinical services, others require social and structural changes, highlighting the importance of internet-based STI testing services to increasing test uptake.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".