Web design and implementation factors associated with missed opportunities to provide testing on GetCheckedOnline, British Columbia’s digital testing service for sexually transmitted and blood-borne infections: 2022 client experience survey findings
Bibliographic record
Abstract
Abstract Background We assessed associations between web-design/implementation factors and missed opportunities to provide testing via GetCheckedOnline and assessed if these associations were modified by sociodemographic factors. Methods A cross-sectional survey was conducted in November and December 2022 among clients who indicated needing testing when they created accounts between April and October 2022. Web-design (user interface and experience) and implementation (organization of clinical services around the website) factors were independently modelled against missed opportunities (self-reported inability/unwillingness to test despite needing testing at account creation) using multivariable logistic regression. Effect modification by sociodemographic factors were also conducted. Results Among 572 respondents needing testing at account creation, 183 (32.0%, 95%CI: 28.18-35.99%) experienced missed opportunities. Web-design factors associated with missed opportunities were difficulty using GetCheckedOnline’s website (adjusted odds ratio (aOR) 3.40, 95%CI:1.68-6.87), while implementation factors were difficulty getting to a laboratory (aOR:3.26, 95%CI:1.97-5.41); perceived inadequacy of tests offered through GetCheckedOnline (aOR:1.81, 95%CI:1.11-2.95) and being likely to complete testing if self-sampling was available (aOR:2.12, 95%CI:1.32-3.42). Findings were consistent in sensitivity analyses but concerns about privacy and security of personal information on GetCheckedOnline (aOR:1.93, 95%CI:1.11-3.35) was associated with missed opportunities. Sociodemographic factors modified associations as respondents with annual income < $20,000CAD, not employed full-time, immigrants, men (who did not agree GetCheckedOnline offered all needed tests) and women (who experienced difficulties getting to a laboratory) had higher odds missed opportunities. Conclusions Simplifying web-design, ensuring optimal client education, and including more laboratory locations and self-sampling as options for testing, could reduce missed opportunities and promote equitable access to GetCheckedOnline. Author Summary Digital health interventions like GetCheckedOnline aim to improve access to testing for sexually transmitted and blood-borne infections (STBBIs), but barriers related to web design and service implementation can limit their impact. Our study, based on a 2022 client experience survey, examined how these factors contribute to missed opportunities for testing among GetCheckedOnline users in British Columbia, Canada. We found that 32% of users who created accounts intending to test, reported not testing through the service. They reported barriers including difficulty navigating the website, accessing laboratories, and concerns about the adequacy of available tests. Importantly, these barriers varied across sociodemographic groups, with individuals with lower incomes, immigrants, and women facing the greatest challenges. Our findings suggest that simplifying website navigation, expanding laboratory access, and introducing self-sampling options could reduce missed opportunities and improve equitable access to digital STBBI testing. These insights highlight the need for ongoing, data-driven optimizations to ensure digital health services effectively reach those who face the greatest barriers to care.
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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 | 0.002 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".