A Systematic Review of the Facilitators and Challenges as Perceived by Dental Service Providers in the Provision of Oral Healthcare for Culturally and Linguistically Diverse Populations
Bibliographic record
Abstract
OBJECTIVES: Vulnerable groups, particularly those from Culturally and Linguistically Diverse (CALD) backgrounds, face heightened risks of poor oral health. There is a notable gap in systematically analysing the facilitators and challenges dental service providers face in meeting the needs of patients from CALD backgrounds. This study, therefore, sought to systematically review existing literature to address this gap and provide insights into the factors that influence dental service provision for CALD communities. METHODS: A comprehensive search of six electronic databases was conducted to identify facilitators and challenges in oral healthcare provision. Database searches covered January 1985 to May 2025 (last updated 31 May 2025). A meta-integration approach was employed to synthesise qualitative and quantitative findings. The quality of evidence was assessed using the Mixed Method Appraisal Tool (MMAT). Data organisation followed Ferlie and Shortell's healthcare model, with key themes identified via thematic analysis. RESULTS: Thirteen papers were included in this review, comprising three quantitative and 10 qualitative studies, sourced from diverse countries, including the United Kingdom, Australia, New Zealand, Japan, Sweden, Netherlands, Finland, Germany, the United States and Canada. Article quality varied from moderate to high. Although individual-level factors such as cultural beliefs and language barriers, impacted dental service provision and rigid organisational structures also served as a significant challenge. Structural/system-level challenges included policy implementation gaps, insufficient cross-cultural training for dental providers and affordability issues. CONCLUSION: The evidence from dental service providers in diverse settings suggests they encounter several challenges when providing dental care to CALD communities. In summary, delivering culturally sensitive oral healthcare is inherently complex. Policymakers must acknowledge that addressing the needs of CALD patients necessitates establishing supportive environments and strengthening institutional capabilities.
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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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".