Cross-cultural communication in a women’s health service: A mixed-methods evaluation
Bibliographic record
Abstract
Abstract Background Effective communication is critical to safety and quality in healthcare. Women with limited English proficiency face cultural and linguistic barriers to quality communication in anglophone settings, perpetuating poor health outcomes and health inequity. Women’s perspectives are essential to inform future evidence-based strategies. This study aimed to evaluate and explore women’s experiences of cross-cultural communication within a women’s health service. Methods Women receiving maternity or gynaecology care at a women’s health service in Western Australia were eligible to participate if they had accessed interpretation support in Arabic, Burmese, Farsi, Mandarin or Vietnamese. A total 68 women completed a cross-sectional survey; 15 women participated in a semi-structured telephone interview. Quantitative and qualitative data underwent descriptive statistical and multilingual reflexive thematic analyses respectively. Results Participants reported diverse language preferences and skills, most frequently identifying Mandarin, Arabic and Vietnamese as their preferred language. At their most recent visit, most recalled having an in-person interpreter present speaking their preferred language. However, less than half of participants were provided with written health information resources in their preferred language. Three themes, the role of interpretation , navigating the health service with uncertainty , and a foundation of trust , explore facets of women’s experiences, linked by an overarching theme, Health is too important not to understand . Conclusions This study highlights the need for proactive service-wide approaches to cross-cultural communication encompassing but not exclusive to, clinical encounters, offering insight into key systemic gaps. Improved integration of interpretation services, language-concordant information resources and language-concordant support for service navigation are recommended. Plain language abstract Effective communication is critical for safety and quality in healthcare. Barriers to cross-cultural communication perpetuate health inequities. This study found that participants experienced inconsistent access to and quality of professional interpretation, provision of language-concordant health information resources was inadequate, and language support was limited to clinical interactions, impeding service navigation. Proactive strategies are recommended to improve service-wide cross-cultural communication not only in clinical encounters, but in women’s broader interactions with health services.
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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 | 0.111 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".