Anticipating the potential for positive uptake and adaptation in the implementation of a publicly funded online STBBI testing service: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Online health services are a rapidly growing aspect of public health provision, including testing for sexually transmitted and other blood-borne infections (STBBI). Generally, healthcare providers, policymakers, and clients imbue online approaches with great positive potential (e.g., encouraging clients' agency; providing cost-effective services to more clients). However, the promise of online health services may vary across contexts and be perceived in negative or ambiguous ways (e.g., risks to 'gold standard' care provision; loss of provider control over an intervention; uncertainty related to budget implications). This study examines attitudes and perceptions regarding the development of a novel online STBBI testing service in Vancouver, Canada. We examine the perceptions about the intervention's potential by interviewing practitioners and planners who were engaged in the development and initial implementation of this testing service. METHODS: We conducted in-depth interviews with 37 healthcare providers, administrators, policymakers, and community-based service providers engaged in the design and launch of the new online STBBI testing service. We also conducted observations during planning and implementation meetings for the new service. Thematic analysis techniques were employed to identify codes and broader discursive themes across the interview transcripts and observation notes. RESULTS: Some study participants expressed concern that the potential popularity of the new testing service might increase demand on existing sexual health services or become fiscally unsustainable. However, most participants regarded the new service as having the potential to improve STBBI testing in several ways, including reducing waiting times, enhancing privacy and confidentiality, appealing to more tech-savvy sub-populations, optimizing the redistribution of demands on face-to-face service provision, and providing patient-centred technology to empower clients to seek testing. CONCLUSIONS: Participants perceived this online STBBI testing service to have the potential to improve sexual health care provision. But, they also anticipated actions-and-reactions, revealing a need to monitor ongoing implementation dynamics. They also identified the larger, potentially system-transforming dimension of the new technology, which enables new system drivers (consumers) and reduces the amount of control health care providers have over online STBBI testing compared to conventional in-person 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 | 0.039 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".