Refugees and Newcomers Emotional Wellness (ReNEW): Partnership for Best Practice
Bibliographic record
Abstract
As the integration of newcomers remains a central process in Canadian society, its dimensions continue to be extended and redefined. While language, citizenship and employment have long been a focus of settlement agencies and government funders, new and changing realities in the experiences of immigrants and refugees have compelled the addressing of wellness from an emotional and mental health perspective. Funders, however, have been slower to respond to these realities, and community-based settlement practitioners have had to evolve their services in multiple ways to respond to these perceived needs in their individual contexts. Some have focused on the channeling of cases to more specialized services in the community, while others have invested in more robust, in-house counselling services. Still others have blended self-care activities into their curricula in the pursuit of a more preventative strategy. The Refugee and Newcomers Emotional Wellness (ReNEW) Partnership for best practice is a three-year project between the University of Calgary and The Immigrant Education Society’s (TIES) Research Department to survey emotional wellness services delivered in four cities in three Canadian prairie provinces. This Immigration, Refugees and Citizenship Canada (IRCC)-funded project culminates in the piloting of a series of best practices that emerge from the first two years of data gathering. For this presentation, ReNEW’s Principal investigators from the University of Calgary and TIES will discuss the progress of the initiative so far, as well as some of the more interesting preliminary results of its analysis.
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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 | 0.089 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".