Empowering Refugee Voices: Using Nominal Group Technique with a Diverse Refugee Patient Advisory Committee (PAC) to Identify Health and Research Priorities
Bibliographic record
Abstract
Abstract Background Despite rising forced displacement globally, refugees’ health and research priorities are largely unknown. We investigated whether a diverse refugee committee could utilize participatory methods to identify health priorities and a research agenda to address them. Methods We conducted a qualitative study with focus groups of current and former refugees, asylum claimants and evacuees from a specialized refugee clinic over a year. We collected sociodemographic data using standardized instruments, then utilized a four-step nominal group technique process (idea generation, recording, discussion, and voting) to identify and rank participants’ health and research priorities. Participants ranked their top five priorities across three time periods: Pre-migration/early arrival (0–3 months), post-migration (3 months–2 years), and long-term health (>2 years). Participants created overarching priorities and corroborated findings via a member checking step. Findings Twenty-three participants (median age 35 years) attended one or more of five focus groups. Twenty-one completed sociodemographic surveys: 16/21 (76%) were women, representing 8 countries of origin. Participants identified “more family physicians” and “improving health system navigation” (11/60 votes each) as top health and research priorities respectively across all resettlement periods. Participants also prioritized pre-departure healthcare system orientation and improved post-arrival and long-term mental health services. Twelve participants completed the member checking process, affirming the results with minor clarifications. Interpretation This proof-of-concept study illustrates how refugees can use a rigorous consensus process without external influence to prioritize their healthcare needs, direct a health research agenda to address those needs, and co-produce research. These low-cost participatory methods should be replicated elsewhere. Funding Funded by the O’Brien Institute for Public Health at the University of Calgary.
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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 | 0.073 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".