Recruiting Under-Represented Racialized Young Adults to the Stem Cell Registry: How We Applied a Community-Engaged Research Approach and Lessons Learned
Bibliographic record
Abstract
Over the past two decades health researchers have increasingly applied principles from community-based or community-engaged research to address health disparities and improve healthcare access and outcomes for under-served communities. Many countries rely on a hematopoietic stem cell registry to help match unrelated donors to patients. Research indicates that people who are racialized wait longer for a match than those of White/European ancestry. As such, many countries, including Canada, make efforts to raise awareness of stem cell donation and recruit under-represented racialized young adults to the registry. This paper describes our efforts to apply community-engaged research principles to a qualitative stem cell recruitment project in Canada and lessons learned. We contribute to methodological scholarship that provides in-depth description of the methods used and steps taken in community-engaged research to offer transparency and detail that enables critical engagement. We offer suggestions to improve co-learning, equitable power-sharing, and building relationships over time.
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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 | 0.289 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.045 | 0.046 |
| Scholarly communication | 0.032 | 0.026 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".