Dental care use by immigrant Canadians in Ontario: a cross-sectional analysis of the 2014 Canadian Community Health Survey (CCHS)
Bibliographic record
Abstract
BACKGROUND: Ontario is home to the largest number of immigrants in Canada. However, very little is known about their dental care utilization patterns. The purpose of this study is to determine the prevalence of poor dental health care use among the immigrant population of Ontario and how various socio-demographic, socio-economic and health-related factors are associated with it. METHODS: Analysis was performed on a total of 4208 Ontarian immigrants who participated in the dental care module of the 2014 cycle of the Canadian Community Health Survey. Poor dental care use was defined by the two variables: not visiting the dentist in the past year and/or visiting the dentist only for emergency purposes. Multivariable logistic regression was performed to assess the associations between the two outcomes and the socio-demographic, socio-economic and health-related factors. RESULTS: Thirty three percent of immigrants reported not visiting the dentist in the past year and 25% reported visiting only for emergencies. The leading components associated with poor dental care utilization were being a new immigrant, of male gender, having low educational attainment, low household income and lacking dental insurance. CONCLUSIONS: This study is the first to highlight oral health care use patterns amongst immigrants in Ontario. Given that a large proportion of the immigrant population in Ontario have poor dental care use, education and outreach programs informing incoming immigrants of preventative dental care may improve overall dental health.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".