Complex role of digital health literacy in awareness and use of digital sexually transmitted and blood-borne infections testing: a structural equation modelling analysis of the 2022 GetCheckedOnline survey
Bibliographic record
Abstract
BACKGROUND: Although digital health literacy (DHL) is recognised as a determinant of access to digital sexually transmitted and blood-borne infection (STBBI) testing, empirical evidence about its contribution to access disparities remains limited. We applied multidimensional DHL measures to examine inequities in awareness and use of GetCheckedOnline, British Columbia's (BC) publicly funded digital STBBI testing service. METHODS: We analysed data from GetCheckedOnline's 2022 community survey of English-speaking BC residents aged ≥16 years who were sexually active in the past year. Outcomes were awareness and use of GetCheckedOnline (yes/no). DHL was measured using latent factors from the eHealth Literacy Scale: Information Navigation, Resource Appraisal and Confidence in Use. Structural equation modelling (SEM) was used to estimate associations and mediation pathways between DHL, sociodemographic characteristics and service outcomes. Model fit was assessed using standard SEM indices. RESULTS: Among 1657 respondents (mean age 33 years, SD 11.77), Information Navigation was positively associated with awareness (β=0.162, p<0.001) and use (β=0.063, p=0.020) of GetCheckedOnline. Confidence in Use was positively associated with awareness (β=0.206, p=0.014) and use (β=0.115, p=0.020). In contrast, Resource Appraisal was negatively associated with awareness (β=-0.263, p=0.006) and use (β=-0.150, p=0.010). DHL factors mediated the effects of age, income, education and digital access on both outcomes. CONCLUSIONS: DHL operates as a multidimensional and socially patterned determinant of access to digital STBBI testing services. While information navigation and confidence in use facilitate access, higher resource appraisal may reduce use, potentially reflecting concerns about service fit, privacy or trust. Findings highlight the need for digital interventions that are not only accessible but also contextually relevant, trusted and responsive to the needs of diverse users.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".