Healthcare access for refugee women with limited literacy: layers of disadvantage
Bibliographic record
Abstract
BACKGROUND: Record numbers of people, across the world, are forced to be displaced because of conflict or other violations of their human rights, thus becoming refugees. Often, refugees not only have a higher burden of disease but also compromised access to healthcare, as they face many barriers, such as limited knowledge of the local language. However, there is very limited knowledge on the lived experiences of this population. Moreover, the strategies people might develop in their efforts to access healthcare have not been explored in depth, despite their value in establishing peer- support, community based programs. METHODS: In this article, we present the findings of a study aiming to explore the lived experiences of accessing healthcare in the greater Vancouver area for recently-arrived, government-assisted refugee women, who were non-literate and non-English-speaking when they arrived in the country. We carried out sixteen semi-structured interviews with eight refugee women, guided by descriptive phenomenology. RESULTS: The findings highlight the intersection of limited knowledge of the local language with low literacy, gender, and refugee status and how it impacts women's access to healthcare, leading to added layers of disadvantage. We discuss three themes: (1) Dependence, often leading to compromised choice and lack of autonomy, (2) Isolation, manifesting as fear in navigating the healthcare system, rejection, or shame for a perceived inadequacy, and (3) Resourcefulness in finding ways to access healthcare. DISCUSSION: We propose that a greater understanding of the intersections of gender, low literacy, and refugee status can guide healthcare workers and policy makers in improving services for this population. Furthermore, It is important to enable seldom-heard, hard to reach populations and facilitate their participation in research in order to understand how vectors of disadvantage intersect.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".