Intersectionality of Cultural Identities in Health Psychology: Key Recommendations for Working With African-Caribbean Immigrant Women
Bibliographic record
Abstract
While the field of health psychology has progressed over the years, much work still needs to be done when considering immigrants' health care and well-being. Particularly, for African-Caribbean immigrants, the intersectionality of their cultural identities in the health care system require much attention moving forward. Therefore, this article is particularly relevant for Canada's multicultural society; it describes cultural identity reconstruction within health psychology as a common issue for diverse groups, particularly African-Caribbean immigrant women. The article speaks to the holistic worldview that is required in a paradigm shift which engages a pluralistic society that is Canada. The author presents a key cultural identity model and assessment tool that should be integrated into the health care system to ensure culturally-sensitive and inclusive care for immigrants, especially women. As is argued in the article, contemporary research, advocacy, and social movements speak to invoking alternative ways to complement the prevailing downstream approach to health psychology. In a growing multicultural society that should strive on honoring and respecting the pluralistic cultural worldviews of all people in the health care system, many immigrant women struggle to cope with the social determinants of health post-migration. Their concerns are often pushed to the margins of health care services, with several individuals relying on their faith for coping strategies. This article concludes with culturally-informed and socially-just recommendations for health care professionals working with immigrant populations, particularly African Caribbean immigrant women.
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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 | 0.059 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.045 | 0.045 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.013 | 0.028 |
| 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".