Black Expert Working Group to Inform National HIV Surveillance
Bibliographic record
Abstract
Research and local public health surveillance data have shown that Black communities are disproportionately impacted by HIV in Canada. However, national HIV surveillance data do not contain sufficient race and/or ethnicity information to adequately describe the magnitude of the problem. Systemic racism and discrimination contribute to vulnerability, exposure to HIV, and health system barriers to accessing prevention technologies, testing and care and have also contributed to the data gaps that limit our ability to describe, address, and monitor these inequities. The low quality and completeness of race and/or ethnicity information in the national HIV surveillance data limits the ability to use this data to inform prevention and care programming, funding decisions, and the monitoring of outcomes. A group of Black researchers and practitioners came together in 2022 to advocate for new approaches to research and policy to address Black people's continued disproportionate exposure to HIV. During engagements with the Public Health Agency of Canada (PHAC)’s National HIV/AIDS Surveillance Program (HASS), data quality concerns and opportunities for improvement were discussed. As a result of this engagement, the Black Expert Working Group (BEWG) was launched in Summer 2023. The BEWG will provide advice to HASS and contribute to the co-development and implementation of strategies to improve the completeness of the race and/or ethnicity data. This work will include reviewing the language in national HIV surveillance reports, critically reviewing race and/or ethnicity categories currently in use in Canada, developing data collection scripts and training tools, and building capacity among Black stakeholders for evidence-informed responses to HIV. The BEWG has the potential to support a robust response to HIV among Black communities through community leadership in optimizing the quality, completeness and use of data on HIV
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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 | 0.069 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".