Racism as a Social Determinant of Health for Newcomers towards Disrupting the Acculturation Process
Bibliographic record
Abstract
Previous research has demonstrated that racism is a social determinant of health (SDOH), particularly for racialized minority newcomers residing in developed nations such as the United States, Canada, New Zealand, and European countries. This paper will focus on racism as a SDOH for racialized newcomers in these countries. Racism is defined as “an organized system of privilege and bias that systematically disadvantages a group of people perceived to belong to a specific race”. Racism can be cultural, institutional, or individual. Berry’s model of acculturation describes ways in which racialized newcomers respond to their post-migration experiences, resulting in one of several modes of acculturation; these are integration, assimilation, separation, and marginalization. After examining the definition and description of racism, we argue that racism impacts newcomers at the site of acculturation; specifically, the paths they choose, or are forced to take in response to their settlement experiences. We posit that these acculturation pathways are in part, strategies that refugees use to cope with post-displacement stress and trauma. To support acculturation, which is primarily dependent on reducing the effects of cultural, institutional, and individual racism, health policymakers and practitioners are urged to acknowledge racism as a SDOH and to work to reduce its impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".