Promoting equitable access to digital sexually transmitted and blood borne infection testing interventions in British Columbia, Canada
Bibliographic record
Abstract
Background: Using GetCheckedOnline – British Columbia’s (BC) digital intervention for sexually transmitted and blood-borne infections (STBBI) testing, this dissertation contributes knowledge to reduce disparities in uptake of digital STBBI testing services by identifying web design and implementation factors influencing their utilization. Methods: This dissertation includes; 1) a scoping review of health equity impacts of digital STBBI testing interventions; 2) interrupted time series analyses of program data evaluating the COVID-19 pandemic’s impact on GetCheckedOnline’s long-term utilization trends; 3) analyses of the 2022 GetCheckedOnline client survey data to identify web design and implementation factors associated with missed opportunities to provide testing (i.e., self-reported inability to test despite needing testing at account creation); and 4) an interpretive description of experiences and expectations of GetCheckedOnline’s web and implementation among users experiencing missed opportunities. Results: The scoping review found only 3/27 included articles used methods allowing exploration of health equity impacts of digital STBBI testing. While increasing STBBI testing across sociodemographic strata, uptake of these interventions occurs along existing sociodemographic gradients, being higher among white, urban residents, and women with higher socioeconomic status. Interrupted time series analyses revealed significantly higher trends in GetCheckedOnline’s utilization after 19 months of the pandemic, especially among people 40 years or older, men who have sex with men, racialized minority populations and first-time testers. The GetCheckedOnline client survey revealed 32% of users experienced missed opportunities. Web design factors including ease of website use, and implementation factors like difficulty accessing a laboratory, perceived inadequacy of STBBI tests on GetCheckedOnline and preference for self-sampling were associated with missed opportunities. Interviews suggested transitioning between GetCheckedOnline, and partner laboratory services is a major barrier, users’ appraisal of their health and social contexts is a determinant of testing, and users believe tailoring GetCheckedOnline’s web and implementation to varying user needs can promote equity. Conclusions: Digital STBBI testing interventions may reinforce inequitable STBBI testing with current designs. While GetCheckedOnline has become increasingly relevant especially for historically marginalized groups since the pandemic, easing the transition between the website and partner laboratory services, and adapting implementation options to user contexts including self-sampling can promote equitable digital STBBI 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".