Implementing community-based Dried Blood Spot (DBS) testing for HIV and hepatitis C: a qualitative analysis of key facilitators and ongoing challenges
Bibliographic record
Abstract
BACKGROUND: In 2018, the Community-Based Research Centre (CBRC) invited gay, bisexual, trans, queer men and Two-Spirit and non-binary people (GBT2Q) at Pride Festivals across Canada to complete in-person Sex Now surveys and provide optional dried blood spot (DBS) samples screening for human immunodeficiency virus (HIV) and hepatitis C virus (HCV). As there is a lack of research evaluating the implementation of DBS sampling for GBT2Q in community settings, we aimed to evaluate this intervention, identifying key facilitators and ongoing challenges to implementing community-based DBS screening for HIV/HCV among GBT2Q. METHODS: We conducted sixteen one-on-one interviews with individuals involved with the community-based DBS collection protocol, including research staff, site coordinators, and volunteer DBS collectors. Most individuals involved with DBS collection were "peers" (GBT2Q-identified). The Consolidated Framework for Implementation Research (CFIR) guided our data collection and analysis. RESULTS: Interviewees felt that DBS collection was a low-barrier, cost-effective, and simple way for peers to quickly screen a large number of Sex Now respondents. Interviewees also noted that the community and peer-based aspects of the research helped drive recruitment of Sex Now respondents. Most interviewees felt that the provision of results took too long, and that some Sex Now respondents would have preferred to receive their test results immediately (e.g., rapid or point-of-care testing). CONCLUSION: Peer-based DBS sampling can be an effective and relatively simple way to screen GBT2Q at Pride Festivals for more than one sexually transmitted and blood borne infection.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".