Perceived racial discrimination, resilience, and oral health behaviours of adolescents with immigrant backgrounds
Bibliographic record
Abstract
INTRODUCTION: Unmet oral health needs remain a significant issue among immigrant adolescents, often exacerbated by experiences of racial discrimination. This study aimed to examine the associations between perceived discrimination and oral health behaviours in adolescents with immigrant backgrounds and explore the potential moderating role of resilience on this association. METHODS: Ethical approval for this cross-sectional study was obtained from the University of Alberta Research Ethics Board. Participants were 12 to 18-year-old adolescents from immigrant backgrounds. Participants were recruited through nine community organizations using a snowball sampling technique. After obtaining active parental consent and assent from the adolescent, the participants completed a questionnaire covering demographics, oral health behaviours, and perceived racial discrimination and resilience. Perceived racial discrimination and resilience were measured using validated scales. Descriptive statistics summarized variables. Logistic regression assessed associations, controlling for confounding factors. Resilience's moderating impact was analyzed via the interaction model of regression analysis. RESULTS: In this cross-sectional study of 316 participants, average age of 15.3 (SD = 1.9) years, and a median age of 15 years (Inter Quartile Range-12-18), 76% reported discrimination experiences. Adjusted analysis showed that an increase of one unit in the total discrimination distress score was associated with 51% less likelihood of categorizing self-rated oral health as good (OR = 0.49, 95% CI: 0.29-0.81). The odds of brushing teeth more than twice a day, as opposed to once a day, decreased by 58% with one unit increase in the total discrimination distress score (OR = 0.42, 95% CI: 0.25-0.71). The odds of visiting the dentist for an urgent procedure instead of a regular check-up were 2.3 times higher with a unit increase in the total discrimination distress score (OR = 2.3: 95% CI:1.3-4.0) Resilience did not moderate the observed association. CONCLUSION: Perceived racial discrimination was associated with the pattern for dental attendance, tooth brushing frequency, and self-rated oral health. Resilience did not moderate the observed association.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".