Impact of social support on oral health among immigrants and ethnic minorities: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Adaptation to social life changes after migration may be beneficial or detrimental to migrants' oral health outcomes and related behaviors. This systematic review aims to synthesize the scientific literature on the impact of social support on immigrants' and ethnic minorities' oral health status and/or behaviors. METHODS: A comprehensive electronic search, up to November 2018, was conducted using five electronic databases. We included cross-sectional and longitudinal quantitative studies that examine associations between social support and oral health outcomes among immigrants and ethnic minorities. Study selection, data extraction, and risk of bias assessment were completed in duplicate and the Newcastle-Ottawa checklist was used to appraise the methodological quality of the quantitative studies. RESULTS: A total of 26 studies met the eligibility criteria. Included studies examined multiple oral health outcomes such as dental care utilization, oral health behaviors, oral health problems, self-rated oral health, oral health knowledge, and oral health-related quality of life among immigrants and ethnic minorities. The social support level is assessed either by social support indicators or by adapting certain scales. Overall, social support was found to be positively associated with dental care utilization, number of carious teeth, periodontal disease, oral health behaviors, oral health knowledge, oral health-related quality of life, and self-rated oral health. CONCLUSION: Although immigrants and ethnic minorities encounter several challenges after migration to a new country that could affect their oral health, social support from their surrounding environment in the form of structural or functional support plays an important role in improving their oral health outcomes.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| 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) | 0.004 | 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".