Creating community learning for empowerment groups: an innovative model for participatory research partnerships with refugee communities
Bibliographic record
Abstract
Empowering communities to respond to humanitarian crises is one of the core principles of the United Nations High Commission for Refugees. In response to large numbers of refugees resettling in Canada from Syria as they fled its civil war, a community-based research partnership was initiated to examine the psychosocial needs and adaptation processes of Syrian individuals and families. In this article, we introduce Community Learning for Empowerment Groups (CLEGs) as a methodological innovation in participatory research partnerships and demonstrate how they can be used to harvest local knowledge and create critical spaces for transformative learning. We describe the process of co-creating CLEGs with seven recently resettled Syrian community leaders, examples of their implementation, and lessons learned in our community-based participatory research (CBPR). Grounded in a transformative paradigm, our CBPR project occurred over three phases of implementation. Activities undertaken by the research team in phase one aimed at empowering the leaders through a “train-the trainer” and collaborative learning approach to lead CLEGs in phase two. Focus groups were held with leaders in phase two to explore their experiences leading CLEGs. Discussions in focus groups revealed that leaders were empowered to adapt their learning from phase one according to their group dynamics and personal leadership style. Deepened insights and new facilitation approaches were evidence of leaders’ growth, as exemplified in the focus groups. Leaders were able to support their groups to generate and, in some cases, implement community-based solutions to their groups’ psychosocial challenges. Community Learning for Empowerment Groups are a promising model for supporting power sharing and knowledge co-construction in participatory research partnerships.
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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 | 0.105 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".