Impact of acculturation on oral health among immigrants and ethnic minorities: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Cultural changes faced by immigrants and ethnic minorities after moving to a host country may have a detrimental or beneficial influence on their oral health and oral health-related behaviors. Therefore, this paper reviews the literature to see the impact of acculturation on immigrants and ethnic minorities' oral health outcomes. METHODS: We searched seven electronic databases up to January 2018. All cross-sectional and longitudinal quantitative studies that examined associations between acculturation and oral health status and/or oral health behaviors among ethnic minority and immigrant population[s] were included. Study selection, data extraction, and risk of bias assessment were completed in duplicate. The Newcastle-Ottawa checklist was used to appraise the methodological quality of the quantitative studies. A meta-analytic approach was not feasible. RESULTS: A total of 42 quantitative studies were identified. The studies showed a positive association between acculturation and oral health status/behaviors. The most frequently used acculturation indicators were language spoken by immigrant and ethnic minorities and length of stay at the host country. High-acculturated immigrant and ethnic minority groups demonstrated better oral health outcomes, oral health behaviors, dental care utilization, and dental knowledge. CONCLUSIONS: According to existing evidence, a positive effect of acculturation on oral health status and behaviors was found. PRACTICAL IMPLICATIONS: Dental practitioners should be culturally competent to provide the appropriate services and treatments to immigrant and ethnic minorities. Policymakers should also be sensitive to cultural diversities and properly address the unique needs of each group in order to maintain oral health equity.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".