Racism in Canadian Media as a Social Determinant of Refugee Psychosocial Wellness
Bibliographic record
Abstract
Background: The Mental Health Commission of Canada states that we have a responsibility to foster incoming refugees’ mental health and wellness. Recently, western countries have demonstrated a pattern of increasing hesitance to accept refugees as full members of society, which is arguably related to the racialization of refugees. For example, media portrayals of refugees have constructed them as hidden terrorists, bogus claimants, and sources of disease/risk. Research shows that racism negatively impacts health; thus, the constructs above may have detrimental impacts on refugee health and wellness. Methods: We aim to investigate the impact of refugee media constructs on the psychosocial wellness of refugees through a systematic literature review. We will do this through a comprehensive, systematic search of peer reviewed, published, academic journal articles using databases such as PubMed, EMBASE, MEDLINE, and CINAHL. We will search for articles published in North America for the past 10 years and will use the SPIDER framework to guide our search and synthesis. Two reviewers will screen the titles and abstracts as well as the full texts of the articles that are selected for further review. Expected results: This research will contribute to the literature demonstrating how discourse impacts our knowledge, attitudes, and behaviour, reinforcing racist perceptions and power structures that often contribute to inequities in health and wellness. It will also inform approaches to community mental health promotion for refugees. Our findings may impact health and social services policy and practice within health organizations and community-serving agencies that work with refugees.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".