Establishing a community advisory board to align harm reduction research with the unique needs of Black and Latine communities
Bibliographic record
Abstract
Death from opioid use is a growing public health concern, with stark racial and ethnic disparities. The randomized controlled trial described here aims to improve initiation and engagement in harm reduction services for Black and Latine people who use drugs to minimize mortality in these populations. The trial is informed by a Community Advisory Board (CAB) of stakeholders from racial and ethnic minoritized backgrounds committed to promoting health equity in populations disproportionately impacted by the drug overdose crisis. CABs are an underutilized mechanism for engaging communities in research to improve health outcomes. Hence, in this manuscript we outline the process and methods employed in creating a CAB, describe its impact on our research study, and recognize the challenges and adaptations made to the CAB during the study.CAB recruitment targeted active community members from Black and Latine communities in the Bronx, NY and New Haven, CT. After attending community organizational meetings in each place, follow-up email efforts were unsuccessful, prompting a revised approach. Emphasizing the study's focus on historically excluded voices, "research-naïve" individuals were sought through online searches and local grassroots organizations, excluding those affiliated with harm reduction groups to minimize bias. Once CAB members were identified, a remote orientation was held, and the CAB began providing regular feedback on research activities, from participant recruitment to educational script details. CAB members' diverse identities and life experiences generated nuanced discussions, which were distilled into feedback improving research materials and recruitment strategies. In the future, the CAB will also guide data analysis and research publications. Other areas of emphasis have included straightforward language in study materials, balanced messaging about harm reduction recommendations, and specific community outreach opportunities. Practical barriers that needed to be addressed for optimal CAB functioning included timely compensation with minimal institutional burden and assistance with meeting coordination and communication.The CAB has ensured that Black and Latine community voices are included in guiding our study, promoting equitable and ethical research. As harm reduction research advances, it is essential to center this work around the intersectional identities of people who use drugs to prevent the disproportionate burden and deaths among Black and Latine people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".