Poor perceived oral health is associated with adverse mental health outcomes among Syrian refugees in Canada
Bibliographic record
Abstract
While inadequate oral health has been linked to adverse mental health outcomes, there is limited understanding of such implications among refugees who bear a disproportionate burden of oral health disparities. This study aims to examine the effect of self-rated oral health on depression, anxiety, and stress among Syrian refugee parents resettled in Ontario. In this cross-sectional study, a total of 540 Syrian refugee parents who resided in Ontario for an average of 4 years and had at least one child under 18 years old were interviewed between March 2021 and March 2022. Information about self-rated oral health was gathered based on the question "In general, how would you rate the health of your teeth and mouth". Responses ranged from 1 representing "excellent" and 5 representing "very poor". The mean score (SD) of self-rated oral health was 3.2 (1.2). Mental health outcomes of depression, anxiety, and stress were measured using the Depression Anxiety Stress Scales (DASS-21). Multiple linear regression analyses were performed to assess the independent relationship between self-rated oral health and depression, anxiety, and stress, adjusting for other variables including, sociodemographic-, migration-, and health-related factors. Among participants, 6.3% rated their oral health as excellent, 26.9% as good, 23.1% as fair, 24.8% as poor, and 18.7% as very poor. Results of the multiple linear regression analyses indicated that poorer self-rated oral health was significantly associated with higher levels of depression (Adjβ = 0.98; p = 0.002; 95% CI = 0.38-1.59), anxiety (Adjβ = 1.03; p< 0.001; 95% CI = 0.54-1.52), and stress (Adjβ = 1.25; p< 0.001; 95% CI = 0.61-1.88). Further efforts and targeted interventions are needed to address the unmet oral health needs of Syrian refugees to improve mental health outcomes within this vulnerable population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".