The Oral Health of Refugees and Asylum Seekers in Canada: A Mixed Methods Study Protocol
Bibliographic record
Abstract
Canada received over 140,000 refugees and asylum seekers between 2015 and 2017. This paper presents a protocol with the purpose of generating robust baseline data on the oral health of this population and build a long-term program of research to improve their access to dental care in Canada. The three-phase project uses a sequential mixed methods design, with the Behavioral Model for Vulnerable Populations as the conceptual framework. In Phase 1a, we will conduct five focus groups (six to eight participants per group) in community organizations in Ontario, Canada, to collect additional sociocultural data for the research program. In Phase 1b, we will use respondent-driven sampling to recruit 420 humanitarian migrants in Ontario and Quebec. Participants will complete a questionnaire capturing socio-demographic information, perceived general health, diet, smoking, oral care habits, oral symptoms, and satisfaction with oral health. They will then undergo dental examination for caries experience, periodontal health, oral pain, and traumatic dental injuries. In Phase 2, we will bring together all qualitative and quantitative results by means of a mixed methods matrix. Finally, in Phase 3, we will hold a one-day meeting with policy makers, dentists, and community leaders to refine interpretations and begin designing future oral health interventions for this 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 | 0.026 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".