Engaging community members to ensure culturally specific language is used in research: should I use gay, queer, MSM, or this other new acronym?
Bibliographic record
Abstract
Researchers often use terminology to define their participant groups that is rooted in a clinical understanding of the group's shared identity(ies). Such naming often ignores the ways that the individuals who comprise these populations identify themselves. One oft-cited benefit of patient-oriented or community-engaged research is that language is local and relevant to impacted communities. This paper aims to contribute to the literature on how this local and relevant language can best be established. We ask how researchers can identify and implement accurate terminology, even when divergent perspectives exist within the communities involved. We draw from our experience with the Expanding Plasma Donation in Canada study, a community-engaged research study, which explored the views of people impacted by the "men who have sex with men" (MSM) blood donation policies in Canada. We describe the collaborative process through which we came to a consensual naming of this population, the challenges we faced, and a set of guiding principles we used to address them. We did not find an all-encompassing term or acronym that worked for all stages of research. Instead, we offer a set of guiding principles that can aid researchers engaging in a similar process: harm reduction, consent and transparency, collaboration and community involvement, recognition of missing voices, and resisting and/or restructuring oppressive standards.
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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 | 0.111 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.052 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".