Assessing excellence in community-based research: Lessons from research with Syrian refugee newcomers
Bibliographic record
Abstract
In this article, we critically reflect on three Syrian refugee research projects that were conducted simultaneously in Ontario, Canada, in order to: (1) strengthen the community system of support for refugee newcomers; (2) address social isolation of Syrian parents and seniors; and (3) promote wellbeing of Syrian youth. Our purpose in this article is to demonstrate a tangible way of assessing research projects which claim to be community-based, and in so doing gain a deeper understanding of how research can be a means of contributing to refugee newcomer resilience. Our assessment of the three studies was done through the reflective lens of the Community Based Research Excellence Tool (CBRET). CBRET is a reflective tool designed to assess the quality and impact of community-based research projects, considering the six domains of community-driven, participation, rigour, knowledge mobilisation, community mobilisation and societal impact. Our assessment produced four main lessons. The first two lessons point to the benefit of holistic emphasis on the six categories covered in the CBRET tool, and to adaptability in determining corresponding indicators when using CBRET. The last two lessons suggest that research can be pursued in such a way that reinforces the rescue story and promotes the safety of people who arrive as refugees. Our lessons suggest that both the findings and the process of research can be interventions towards social change. The diversity of the three case examples also demonstrates that these lessons can be applied to projects which focus on both individual-level and community-level outcomes.
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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 | 0.246 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.046 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".