Developing a Toolkit for Hybrid Instruction for Adult Newcomer Literacy Learners: A Participatory Research Approach.
Bibliographic record
Abstract
Background: The Public Health Agency of Canada (PHAC) has recognized that anti-Black racism is a significant determinant of mental health and well-being. While issues of housing and racism has been extensively discussed, the interactive effects of anti-Black racism, housing and mental health have not called enough attention. Methods: We used Group Concept Mapping methods to bring together a total of 174 stakeholders including community leaders, volunteers, and service providers to synthesize ideas of actions that need to be taken to promote the health and mental health of Black refugees in Edmonton and Calgary, Alberta. The generated idea statements were further sorted and rated in order of importance and ideas seen in action (or implementation) by a group of 51 participants. Results: In this presentation, we will present the findings that emphasize the close connection between housing, anti-Black racism, and mental health. We found that when ranking which social determinants were most important in addressing the mental health of Black refugees in Canada, there were significant discrepancies between the perceptions of Black community members as compared to the perceptions of professional service providers. Specifically, community leaders and informal support persons with lived experience of anti-Black racism ranked housing as one of the most important and least addressed issues in terms of mental health equity for Black communities, while service providers ranked it as one of the least important and most addressed issue. Conclusion: Our presentation will discuss the relevance and implications of these discrepancies among stakeholders. Housing is a significant mental health and racial equity issue. Associated recommendations for policy makers and practitioners grounded in the needs and perspectives of Black newcomers living in Canada will be discussed.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".