The effectiveness of ethno-specific and mainstream health services: an evidence gap map
Bibliographic record
Abstract
BACKGROUND: People of culturally and linguistically diverse (CALD) background face significant barriers in accessing effective health services in multicultural countries such as the United States, Canada, Europe and Australia. To address these barriers, government and nongovernment organisations globally have taken the approach of creating ethno-specific services, which cater to the specific needs of CALD clients. These services are often complementary to mainstream services, which cater to the general population including CALD communities. METHODS: This systematic review uses the Evidence Gap Map (EGM) approach to map the available evidence on the effectiveness of ethno-specific and mainstream services in the Australian context. We reviewed Scopus, Web of Science and PubMed databases for articles published from 1996 to 2021 that assessed the impact of health services for Australian CALD communities. Two independent reviewers extracted and coded all the documents, and discussed discrepancies until reaching a 100% agreement. The main inclusion criteria were: 1) time (published after 1996); 2) geography (data collected in Australia); 3) document type (presents results of empirical research in a peer-reviewed outlet); 4) scope (assesses the effectiveness of a health service on CALD communities). We identified 97 articles relevant for review. RESULTS: Ninety-six percent of ethno-specific services (i.e. specifically targeting CALD groups) were effective in achieving their aims across various outcomes. Eighteen percent of mainstream services (i.e. targeting the general population) were effective for CALD communities. When disaggregating our sample by outcomes (i.e. access, satisfaction with the service, health and literacy), we found that 50 % of studies looking at mainstream services' impact on CALD communities found that they were effective in achieving health outcomes. The use of sub-optimal methodologies that increase the risk of biased findings is widespread in the research field that we mapped. CONCLUSIONS: Our findings provide partial support to the claims of advocacy stakeholders that mainstream services have limitations in the provision of effective health services for CALD communities. Although focusing on the Australian case study, this review highlights an under-researched policy area, proposes a viable methodology to conduct further research on this topic, and points to the need to disaggregate the data by outcome (i.e. access, satisfaction with the service, health and literacy) when assessing the comparative effectiveness of ethno-specific and mainstream services for multicultural communities.
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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 | 0.063 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".