How chronic conditions are understood, experienced and managed within African communities in Europe, North America and Australia: A synthesis of qualitative studies
Bibliographic record
Abstract
This review focuses on the lived experiences of chronic conditions among African communities in the Global North, focusing on established immigrant communities as well as recent immigrant, refugee, and asylum-seeking communities. We conducted a systematic and narrative synthesis of qualitative studies published from inception to 2022, following a search from nine databases—MEDLINE, EMBASE, PsycINFO, Web of Science, Social Science Citation Index, Academic Search Complete, CINAHL, SCOPUS and AMED. 39 articles reporting 32 qualitative studies were included in the synthesis. The studies were conducted in 10 countries (Australia, Canada, Denmark, France, Netherlands, Norway, Sweden, Switzerland, United Kingdom, and the United States) and focused on 748 participants from 27 African countries living with eight conditions: type 2 diabetes, hypertension, prostate cancer, sickle cell disease, chronic hepatitis, chronic pain, musculoskeletal orders and mental health conditions. The majority of participants believed chronic conditions to be lifelong, requiring complex interventions. Chronic illness impacted several domains of everyday life—physical, sexual, psycho-emotional, social, and economic. Participants managed their illness using biomedical management, traditional medical treatment and faith-based coping, in isolation or combination. In a number of studies, participants took 'therapeutic journeys'–which involved navigating illness action at home and abroad, with the support of transnational therapy networks. Multi-level barriers to healthcare were reported across the majority of studies: these included individual (changing food habits), social (stigma) and structural (healthcare disparities). We outline methodological and interpretive limitations, such as limited engagement with multi-ethnic and intergenerational differences. However, the studies provide an important insights on a much-ignored area that intersects healthcare for African communities in the Global North and medical pluralism on the continent; they also raise important conceptual, methodological and policy challenges for national health programmes on healthcare disparities.
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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 | 0.052 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".