O15.6 Differences in reported testing barriers between clients of an online sti testing service (getcheckedonline.com) and a provincial sti clinic in vancouver, canada
Bibliographic record
Abstract
Introduction Online STI testing programs are thought to overcome barriers posed by in-clinic testing, though uptake could reflect social gradients (e.g., technology access, higher education). To understand types of barriers mitigated by online STI testing we compared clients of a large STI clinic to clients of GetCheckedOnline (GCO). Methods Our study was conducted in Vancouver after GCO was promoted to provincial STI clinic clients and men who have sex with men (MSM). Clinic and GCO clients were invited to an online survey 2 weeks after receiving test results. Survey questions included barriers/facilitators of testing at individual, provider, clinic and societal levels. We conducted bivariate comparisons between groups (significant results shown at p<0.01). Results GCO (n=87) were older than clinic clients (n=424; median 35 vs. 31 years) and a higher proportion were MSM (40.2% vs. 24.4. More GCO clients reported their reason for testing as routine (58.1% vs. 38.9%) and fewer for symptoms or STI contact (10.3% vs 33.5%). More GCO clients considered accessing online health resources important (76.1% vs 56.5%) but otherwise did not differ on technology skills/use. GCO clients were more likely to report delaying testing in the past year due to clinic distance (22.4% vs 9.7%), less likely to agree that clinic hours were convenient (58.2% vs 77.2%) or that making appointments was easy (49.4% vs. 65.4%), and more likely to report long wait times to see a health care provider (HCP) (47.6% vs 20.7%). GCO clients were more likely to be uncomfortable discussing their sexual history with HCP in general (15.5% vs 5.7%) and where they usually presented for health care (34.9% vs 20.6%), as well as reporting more fear of being judged by HCP (28.6% vs 15.4%). Conclusion Our study in Vancouver suggests that online testing services may effectively engage individuals with barriers to testing (i.e., clinic access, discomfort with HCP) with few social gradients in uptake. Further evaluation to verify these findings within different cities/populations is needed.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".