Respectful community engagement in health research with diverse im/migrant communities
Bibliographic record
Abstract
Introduction Global migration and immigration are increasing, and migrants and immigrants (im/migrants) have specific health needs and healthcare experiences. Yet, im/migrant involvement in immigration and health research in Canada is inconsistent. Heretofore, involvement has primarily been in research planning, data collection and analysis, with little community involvement during knowledge exchange or through training and colearning opportunities. Community engagement has been especially uncommon in mixed-method and quantitative research in Canada. Objective This article describes lessons learnt from the Evaluating Inequities in Refugee & Immigrants’ Health Access (IRIS) project from 2018 to 2023, an ongoing mixed-method, community-based research project in British Columbia, Canada. Specifically, we share our core community engagement project structures, Commitments to Community and our Community Engagement Backbone , both collaboratively developed with im/migrant community memebers. Participants People with varied experiences of im/migration and connections to multiple, specific im/migrant communities participate in the project as participants, community researchers, community advisory board members, faculty members and students. Core research activities are supported in English, Farsi, Spanish and Tigrinya. We engage community members throughout the research process, from identifying research topics to knowledge exchange. Conclusion We found that these structures offer an accessible visual representation of the project’s commitments to community engagement, and the ways these commitments are demonstrated through values and action. Our training opportunities, colearning activities and knowledge exchange efforts also confirmed the accuracy of interpretation, prompted additional analysis to clarify or add depth to findings, and helped us identify additional research topics. We hope these learnings can be used to expand engagement with diverse im/migrant communities in health and immigration 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 | 0.071 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".