Qualitatively-Driven Mixed-Methods Health Research Protocol: “HIV/STBBIs in a Post-Pandemic World: Challenges and Opportunities for Addressing the Needs of People Living With HIV”
Bibliographic record
Abstract
This qualitatively-driven, mixed-methods study explores the impacts of the COVID-19 pandemic on people living with human immunodeficiency virus (HIV) testing, treatment, and linkage to care in Manitoba, Canada. Building on the “Landscape of Risk” study, a population-level quantitative analysis examining associations between COVID-19 vaccination and HIV/sexually transmitted and blood borne infection (STBBI) testing, this protocol describes the qualitative phase designed to contextualize those findings. Preliminary data suggests that people living with HIV (PLWHIV) were more likely to receive COVID-19 vaccinations than the general population while also being more likely to acquire the virus. This contrasts with lower vaccination rates among other STBBI-affected cohorts, raising critical questions about health system engagement, trust, and access. Through interviews and focus groups with both PLWHIV and health care service providers, this study aims to validate quantitative results, explore pandemic-era shifts in HIV/STBBI service use, and generate recommendations for improving health equity in future pandemic responses. Guided by constructivist, critical, and poststructuralist paradigms, and informed by ethical, community-based research principles, the study uses a sequential exploratory design to integrate community narratives with administrative health data. Data will be collected in partnership with Nine Circles Community Health Centre and analyzed using thematic analysis. This study’s commitment to rigor, reflexivity, and participant voice ensures relevance to public health policy, while offering insights into resilience, service adaptations, and equity-oriented strategies for vulnerable populations navigating intersecting health risks in a post-pandemic world.
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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 | 0.161 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.024 |
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".