Impacts of English language proficiency on healthcare access, use, and outcomes among immigrants: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Immigrants from culturally, ethnically, and linguistically diverse countries face many challenges during the resettlement phase, which influence their access to healthcare services and health outcomes. The "Healthy Immigrant Effect" or the health advantage that immigrants arrive with is observed to deteriorate with increased length of stay in the host country. METHODS: An exploratory qualitative design, following a community-based research approach, was employed. The research team consisted of health researchers, clinicians, and community members. The objective was to explore the barriers to healthcare access among immigrants with limited English language proficiency. Three focus groups were carried out with 29 women and nine men attending English language classes at a settlement agency in a mid-sized city. Additionally, 17 individual interviews were carried out with healthcare providers and administrative staff caring for immigrants and refugees. RESULTS: A thematic analysis was carried out with transcribed focus groups and healthcare provider interview data. Both the healthcare providers and immigrants indicated that limited language proficiency often delayed access to available healthcare services and interfered with the development of a therapeutic relationship between the client and the healthcare provider. Language barriers also impeded effective communication between healthcare providers and clients, leading to suboptimal care and dissatisfaction with the care received. Language barriers interfered with treatment adherence and the use of preventative and screening services, further delaying access to timely care, causing poor chronic disease management, and ultimately resulting in poor health outcomes. Involving untrained interpreters, family members, or others from the ethnic community was problematic due to misinterpretation and confidentiality issues. CONCLUSIONS: The study emphasises the need to provide language assistance during medical consultations to address language barriers among immigrants. The development of guidelines for recruitment, training, and effective engagement of language interpreters during medical consultation is recommended to ensure high quality, equitable and client-centered care.
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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 | 0.007 | 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 | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".