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Record W4411429551 · doi:10.2196/preprints.78561

Health equity analysis of awareness and use of GetCheckedOnline, British Columbia’s web-based intervention for sexually transmitted and blood-borne infection testing: A cross-sectional study (Preprint)

2025· preprint· en· W4411429551 on OpenAlexaboutno aff
Rodrigo Sierra Rosales, Aidan Ablona, Hsiu-Ju Chang, Devon Haag, Heather Pedersen, Catherine Worthington, Daniel Grace, Rod Knight, Devon Greyson, Mark Gilbert

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHealth equityCross-sectional studyConfidence intervalReproductive healthLogistic regressionOdds ratioDemographyFamily medicineEnvironmental healthPublic healthNursingSociologyPopulation

Abstract

fetched live from OpenAlex

BACKGROUND Digital sexually transmitted and blood-borne infection (STBBI) testing services are used to improve testing access, but might replicate existing social inequities. GetCheckedOnline is a web-based STBBI testing service that generates laboratory requisitions without the need to visit a healthcare provider in British Columbia (BC), Canada. Previous research has shown that it has improved access to testing. As part of the program's continuous evaluation, we examined awareness and use of the service in five urban, suburban, and rural communities where the program has expanded. OBJECTIVE To determine if social location is associated with differences in awareness and use of the service in five communities outside Vancouver, British Columbia. METHODS We conducted a cross-sectional survey recruiting (in-person, online) sexually active people 16+ years old in five communities where GetCheckedOnline is available. We examined differences in awareness and use by age, gender identity, sexual identity, race/ethnicity, education, and income using logistic regression models informed by the Health Equity Measurement Framework. RESULTS Of 1,658 participants (63.8% in-person, 36.2% online), 35.3% were aware of GetCheckedOnline and 19.5% had used it. Awareness and use were lower in the first and last age quartiles compared to the second quartile (38+ years: awareness odds ratio (OR) 0.23 [95% Confidence Interval: 0.17 – 0.32], use OR 0.19 [0.12 – 0.28]; <25 years: awareness OR 0.39 [0.28 – 0.53], use OR 0.28 [0.18 –0.41]). Awareness and use were also lower in the lowest income group compared to the highest (awareness OR 0.39 [0.24 – 0.65]), use OR 0.36 [0.20 – 0.65]). Awareness and use were higher among genderfluid, genderqueer and non-binary compared to men (awareness OR 2.27 [1.63 – 3.18], use OR 1.97 [1.36 – 2.84]), transgender compared to cisgender participants (awareness OR 2.17 [1.54 – 3.06], use OR 2.15 [0.46 – 3.13]), and non-heterosexual compared to heterosexual participants (awareness OR 2.37 [1.89 – 2.97], use OR 2.53 [1.91 – 3.38]). People of Colour had higher awareness and use versus white participants (awareness OR 1.74 [1.34 – 2.26], use OR 2.01 [1.48 – 2.72]). Indigenous participants had higher awareness than white participants (OR 1.65 [1.19 – 2.20]) but no difference in use, while women had similar awareness but lower use compared to men (OR 0.68 [0.50 – 0.92]). CONCLUSIONS GetCheckedOnline is an equitable means of access to STBBI testing for some but not all equity-owed groups in BC. Further adaptations should consider factors such as differences in material circumstances to improve its accessibility for all.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.086
GPT teacher head0.426
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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