Methodological reflections on research with racialized communities and stigmatized topics: Towards a model of transformative engagement
Bibliographic record
Abstract
Through our participation as the Calgary site for the Strength in Unity (SIU) project, a pan-Canadian randomized controlled trial, our team anticipated learning much about the seldom researched area of racialized men’s experiences with mental health stigma and their responses to novel interventions. Distinct from the study’s formal objectives and research queries, we encountered significant recruitment challenges, which engendered unanticipated but welcomed learnings concerning culturally sensitive recruitment practices. To help build the capacity of researchers to meaningfully and respectfully engage ethnoculturally diverse populations, this article discusses five major approaches to participant recruitment and engagement used by the Calgary-based SIU team, as well as the strengths and limitations of each identified approach. In this critical commentary we examine conventional recruitment processes employed in Calgary during early stages of the broader SIU study, thereby illuminating unanticipated barriers to the success of these accepted recruitment practices, as well as report lessons learned that may benefit projects endeavoring to use community-based recruitment strategies to engage participants from diverse cultural groups, particularly for projects considering matters stigmatizing (real or potential) to the community or communities of interest. From our serendipitous learnings we proffer the terminology, “transformative engagement” to characterize a novel process for social work researchers (and/or allied health professionals) to engage with communities and peoples in meaningful, respectful, lasting, and transformative processes, that move beyond traditional and even culturally-sensitive research recruitment practices.
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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 | 0.570 | 0.437 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.029 | 0.136 |
| Scholarly communication | 0.038 | 0.033 |
| Open science | 0.014 | 0.035 |
| Research integrity | 0.013 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".