Culturally Sensitive Approaches in Psychosocial Interventions to Enhance Well-Being of Immigrant Adults Diagnosed with Breast Cancer: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The objective is to synthesize the literature on culturally sensitive approaches in psychosocial interventions to enhance the well-being of immigrant adults diagnosed with breast cancer. METHODS: We conducted a systematic review following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and reporting literature searches, and a multi-database search strategy of qualitative research studies and reports published in academic journals and grey literature within a 20-year duration. RESULTS: We extracted data from twenty-two studies that met the inclusion criteria. Content analysis revealed experiences of cultural considerations in the care and psychosocial well-being of immigrants such as the development of culturally responsive care models; barriers and gaps in culturally responsive care in rural communities; patient information, education, and culturally responsive care; cultural stigma, and self-perception of the access, use, and role of healthcare providers, the impact of cancer and linguistically appropriate care; and challenges with psychosocial well-being and culturally responsive care. CONCLUSIONS: Concerns relating to psychosocial well-being of immigrant adults diagnosed with breast cancer are consistently described in the literature. Interventions exist to address psychosocial well-being; however, none have been developed or tested in immigrant adults diagnosed with breast cancer. Addressing the psychosocial well-being of immigrant adults will require the integration of culturally appropriate considerations in care to attitudes impacting patient care and reported outcomes.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".