Dental health status, dentist visiting, and dental insurance of Asian immigrants in Canada
Bibliographic record
Abstract
Abstract Objective This study examined the dental care utilization and self-preserved dental health of Asian immigrants relative to non-immigrants in Canada. Factors associated with oral health-related disparities between Asian immigrants and other Canadians were further examined. Methods We analyzed 37,935 Canadian residents aged 12 years and older in the Canadian Community Health Survey 2012–2014 microdata file. Factors (e.g., demographics, socioeconomic status, lifestyles, dental insurance coverage, and year of immigration) associated with disparities in dental health (e.g., self-perceived teeth health, dental symptoms during past one month, and teeth removed due to decay in past one year) and service utilization (e.g., visiting dentist within the last three years, visiting dentist more than once per year) between Asian immigrants and other Canadians were examined using multi-variable logistic regression models. Results The frequency of dental care utilization was significantly lower in Asian immigrants than their non-immigrant counterparts. Asian immigrants had lower self-perceived dental health, were less likely to be aware of recent dental symptoms, and more likely to report tooth extractions due to tooth decay. Low education (OR = 0.42), male gender(OR = 1.51), low household income(OR = 1.60), non-diabetes(OR = 1.87), no dental insurance(OR = 0.24), short immigration length (OR = 1.75) may discourage Asian immigrants from dental care utilization. Additionally, a perceived lack of necessity to dentist-visiting was a crucial factor accounting for the disparities in dental care uptake between Asian immigrants and non-immigrants. Conclusion Asian immigrants showed lower dental care utilization and oral health than native-born Canadians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".