Journal of Population and Therapeutics and Clinical Pharmacology
Bibliographic record
Abstract
Objectives: Culturally and linguistically diverse (CALD) communities experience widespread inequalities in dental care utilization. While, several studies have reported factors contributing to such inequalities, a synthesis of evidence is lacking for CALD carers. This review examined the barriers and facilitators to dental care utilization among CALD carers. Methods: Medline, CINAHL, ProQuest, Scopus and Web of Science were searched for dental utilization and related factors, without geographic limitations. An integrated mixed-method design was adopted, where both qualitative and quantitative findings were combined into a single synthesis. Critical appraisal was conducted using JBI tools, and a Universal Health Coverage (UHC) framework guided the synthesis approach. Reliability and researcher triangulation occurred throughout the conduct of this review. Results: A total of 20 papers were included: qualitative (n = 8), quantitative (n = 8) and mixed method (n = 4). Studies were from Australia, Canada, South Korea, Trinidad and Tobago, United Kingdom and the United States. Three studies insufficiently reported confounding variables and nine qualitative papers lacked philosophical perspectives. Affordability was the foremost barrier at the system level, followed by psychosocial negative provider experiences and language/communication issues at the provider level. Cultural, knowledge, attitudes and beliefs were individual-family level factors. Utilizing a UHC framework, the barriers and facilitators were aggregated at three levels; financial-system, provider and individual-family levels and illustrated in the rainbow model of CALD oral health. Conclusion: The review strengthens evidence for multilayered, system-related policies and culturally sensitive provision of services for reducing oral healthcare inequalities in CALD carers.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".