Research with Refugee Populations in North America: Applying the NIH Guiding Principles for Ethical Research
Bibliographic record
Abstract
This article examines the ethics of research design and the initiation of a study (e.g., recruitment of participants) involving refugee participants. We aim to equip investigators and members of IRBs with a set of ethical considerations and pragmatic recommendations to address challenges in refugee-focused research as it is developed and prepared for IRB review. We discuss challenges including how refugees are being defined and identified; their vulnerabilities before, during, and following resettlement that impacts their research participation; recruitment; consent practices including assent and unaccompanied minors; and conflicts of interest. Ethical guidance and regulatory oversight provided by international bodies, federal governments, and IRBs are important for enforcing the protection of participants. We describe the need for additional ethical guidance and awareness, if not special protections for refugee populations as guided by the National Institutes of Health (NIH) Guiding Principles for Ethical Research.
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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 | 0.322 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.042 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".