Impact of the COVID-19 Health Crisis on Key Populations at Higher Risk for, or Living With, HIV or Hepatitis C Virus and People Working With These Populations: Multicountry Community-Based Research Study Protocol (EPIC Program)
Bibliographic record
Abstract
BACKGROUND: Information concerning the impact of the COVID-19 health crisis on populations most affected by HIV and hepatitis C virus (HCV; or key populations [KP]), and those working with these populations in community settings, is limited. Community-based organizations working in the field of HIV and viral hepatitis are well placed to identify and meet the new needs of KP owing to the health crisis. OBJECTIVE: This study aims to describe the development and implementation of an exploratory and descriptive multicountry, community-based research program, EPIC (Enquêtes Pour évaluer l'Impact de la crise sanitaire covid en milieu Communautaire), within an international network of community-based organizations involved in the response to HIV and viral hepatitis. The EPIC program aimed to study the impact of the COVID-19 health crisis on KP or people living with HIV or HCV and people working with these populations at the community level (community health workers [CHWs]) and to identify the key innovations and adaptations in HIV and HCV services. METHODS: A general protocol and study documents were developed and shared within the Coalition PLUS network. The protocol had a built-in flexibility that allowed participating organizations to adapt the study to local needs in terms of the target population and specific themes of interest. Data were collected using surveys or interviews. RESULTS: From July 2020 to May 2022, a total of 79 organizations participated in the EPIC program. Across 32 countries, 118 studies were conducted: 66 quantitative (n=12,060 among KP or people living with HIV or people living with HCV and n=811 among CHWs) and 52 qualitative (n=766 among KP or people living with HIV or people living with HCV and n=136 among CHWs). CONCLUSIONS: The results of the EPIC program will provide data to describe the impact of the health crisis on KP and CHWs and identify their emerging needs. Documentation of innovative solutions that were put into place in this context may help improve the provision of services after COVID-19 and for future health crises. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45204.
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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 | 0.046 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".