Advancing Our Understanding of Dental Care Pathways of Refugees and Asylum Seekers in Canada: A Qualitative Study
Bibliographic record
Abstract
The burden of oral diseases and need for dental care are high among refugees and asylum seekers (humanitarian migrants). Canada’s Interim Federal Health Program (IFHP) provides humanitarian migrants with limited dental services; however, this program has seen several fluctuations over the past decade. An earlier study on the experiences of humanitarian migrants in Quebec, Canada, developed the dental care pathways of humanitarian migrants model, which describes the care-seeking processes that humanitarian migrants follow; further, this study documented shortfalls in IFHP coverage. The current qualitative study tests the pathway model in another Canadian province. We purposefully recruited 27 humanitarian migrants from 13 countries in four global regions, between April and December 2019, in two Ontario cities (Toronto and Ottawa). Four focus group discussions were facilitated in English, Arabic, Spanish, and Dari. Analysis revealed barriers to care similar to the Quebec study: Waiting time, financial, and language barriers. Further, participants were unsatisfied with the IFHP’s benefits package. Our data produced two new pathways for the model: transnational dental care and self-medication. In conclusion, the dental care needs of humanitarian migrants are not currently being met in Canada, forcing participants to resort to alternative pathways outside the conventional dental care system.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".