Missed opportunities to provide sexually transmitted and blood-borne infections testing in British Columbia: An interpretive description of users’ experiences of Get Checked Online's design and implementation
Bibliographic record
Abstract
Background: Digital testing services for sexually transmitted and blood-borne infections (STBBIs), such as GetCheckedOnline, experience significant user drop-offs. For example, 32% of GetCheckedOnline users needing testing at account creation do not test, constituting missed opportunities. We explored the influence of users' expectations and experiences of GetCheckedOnline's web design and implementation on missed opportunities. Methods: This interpretive description purposively sampled 14 GetCheckedOnline users who created accounts between April 2022 and February 2023, indicated needed testing at account creation but did not test. We conducted semi-structured interviews and cognitive walkthroughs of GetCheckedOnline on Zoom, exploring participants' expectations and experiences, including problems using the service. Interviews were audio recorded, transcribed verbatim, and analyzed using reflexive thematic analyses. Results: Three themes were identified: (a) transitioning between GetCheckedOnline and laboratory services is a major testing barrier; (b) users' appraisal of their health and social contexts is a determinant of testing through GetCheckedOnline; and (c) tailoring GetCheckedOnline's design and implementation to accommodate varying user needs can promote equitable testing. Health equity issues occurred along sociodemographic gradients as the GetCheckedOnline-laboratory transition was more onerous for older users. Users' appraisal of their testing needs which varied by age and gender, and their assessment of time, and travel requirements for testing in remote communities influenced testing. Learning about GetCheckedOnline from healthcare providers improved testing compared with learning about the service through Google search which raised trust concerns regarding GetCheckedOnline's authenticity. Suggested improvements to promote health equity include personalized education, mail-in testing options, and simpler seamless web experiences. Conclusions: To promote equitable access to digital STBBI testing services such as GetCheckedOnline, we can adapt web-design and implementation to suit user needs and contexts, ensuring simplicity and options for testing that reduce user burdens.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".