A qualitative study on the oral health of humanitarian migrants in Canada.
Bibliographic record
Abstract
OBJECTIVES: There is limited evidence to guide oral health policy and services for the 25,000 refugees and asylum seekers who arrive in Canada yearly. The purpose of this study was to explore and understand the pre-migration use of dental services, oral health knowledge, and the effects of oral disease among newly arrived humanitarian migrants in order to inform policy and practice for the population. METHODS: Using focused ethnography and the public health model of the dental care process, we conducted face-to-face interviews (50-60 minutes) with a purposive sample of humanitarian migrants who had indicated the need for dental care. We observed mobile dental clinics that provided care to underserved communities in Montreal. Data were analyzed using a thematic and contextual approach that combined inductive and deductive frameworks. RESULTS: Participants included 25 humanitarian migrants from four global geographical regions. Five major thematic categories were explored: problem-based dental consultation, self-assessed oral health status, causes of oral diseases, personal oral hygiene, and good oral health for wellbeing. In their countries of origin, participants consulted a dentist when oral symptoms persisted. They cited excessive sugar consumption and inadequate oral hygiene as causes of oral diseases, and reported significant oral diseases impacts that limited their daily functions and wellbeing once in Canada. CONCLUSIONS: Humanitarian migrants were knowledgeable about causes of oral disease and the importance of good oral health, yet poor oral health continued to affect their lives in Canada in important ways.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".