Experiences and meaningfulness of breast cancer survivorship care in improving the quality of life of immigrant women: A qualitative systematic review
Bibliographic record
Abstract
Introduction: Holistic healthcare approaches tailored to people's needs can enhance quality of life. However, needs of immigrant women in breast cancer survivorship have received little attention. Language barriers can result in significant challenges to accessing healthcare and difficulty in translating information from healthcare providers into self-management. The purpose of this review was to examine the experiences and meaningfulness of breast cancer survivorship care in improving the quality of life for immigrant breast cancer survivors. Methods: A qualitative systematic review using a systematic search of peer-reviewed and non-peer-reviewed articles of empirical research was conducted. Articles about immigrant women who were diagnosed, treated, and completed their initial course of breast cancer treatment were included. Exclusion criteria were non-immigrant women, men, younger than 18 years, no breast cancer diagnosis, not currently undergoing active or advanced treatment, and not receiving palliative or end-of-life care. Thematic synthesis and qualitative systematic review included studies of survivorship care targeting breast cancer treatment and post-treatment. Articles were obtained from PubMed/MEDLINE, CINAHL, Embase, PsycINFO, Web of Science, and Google Scholar searches, with findings configured according to the JBI methodology. Results: Twenty studies, most of moderate-to high-quality were included. The thematic synthesis identified two overall themes regarding breast cancer survivorship care, as well as subthemes for both. The subthemes of the first theme, challenges and experiences accessing and using healthcare services, were inadequate insurance and benefits, less acculturation and decision-making, discrimination and differential treatment, and influencing sociocultural factors. The subthemes of the second theme, facilitators and barriers to accessing and using healthcare services, included ineffective knowledge and communication, lack of continuity and information, cultural and language barriers, limited access to services, and quality of care. Conclusions: Supporting and engaging immigrant women throughout their breast cancer journey is critical. To do so requires integrating advancements in survivorship care as a systematic approach to lead to a better quality of life and improving patient-reported outcomes.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".