Need for Culturally Competent and Responsive Cancer Education for African Immigrant Families and Youth Living in the United States
Bibliographic record
Abstract
Cancer prevalence data for Black Americans is monolithic and fails to consider the diverse cultures and backgrounds within that community. For instance, African immigrants constitute a meaningful proportion of the foreign-born Black immigrants in the United States (42%), but the prevalence of cancer in the African immigrant community itself is unknown. Therefore, without accurate cancer prevalence data, it is impossible to identify trends and other key factors that are needed to support the health of African immigrants and their children. Moreover, it is impossible to understand how the culture and language of subgroups influence their cancer-related health behavior. While research in this area is limited, the existing literature articulates the need for culturally responsive and culturally tailored cancer education for African immigrants and their adolescent children, which is what we advocate for in this viewpoint paper. Existing projects demonstrate the feasibility of culturally responsive programming for adults; however, few projects include or focus on adolescents or children born to African immigrants. To best meet the needs of this understudied community, researchers must use culturally competent interventions alongside familiar, usable media. For adolescents, technology is ubiquitous thus, the creation of a culturally tailored digital intervention has immense potential to improve cancer awareness and prevention for youth and their community. More research is needed to address many of the existing research gaps and develop a rich understanding of the unique experience of cancer among African immigrant families that can be used to inform intervention development. Through this viewpoint, we review the current state of cancer-related research among African immigrant families in the United States. In this paper, we acknowledge the current knowledge gaps and issues surrounding measurement and then discuss the factors relevant to designing an educational intervention targeted at African immigrants and the role of African immigrant youth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".