Dental Insurance Coverage, Dentist Visiting, and Oral Health Status among Asian Immigrant Women of Childbearing Age in Canada: A Comparative Study
Bibliographic record
Abstract
OBJECTIVES: This study examined the dental insurance coverage, dentist visits, self-perceived oral health status, and dental problems among Asian immigrant women of childbearing age in contrast to Canadian women of childbearing age and non-Asian immigrant women of childbearing age. Potential barriers to dental care services among Asian immigrant women were explored. METHODS: This analysis utilized data from the combined Canadian Community Health Survey from 2011 to 2014. The analytical sample consisted of 5737 females whose age was between 20 and 39 years. Multivariable logistic regression models assessed immigrant status and other factors in relation to the indicators of dental health (i.e., dental visit, self-perceived oral health, acute teeth issue, and teeth removed due to decay). RESULTS: Amongst Asian women immigrants of childbearing age, there was a significantly lower frequency of dentist visits compared to non-immigrant counterparts (OR = 0.53; 95% CI: 0.37-0.76). The most commonly reported reason for not seeking dental care in the last three years was that the "respondent did not think it was necessary". Relative to Canadian born women of same age bracket, Asian women of childbearing age reported fewer acute teeth issues (OR = 0.67; 95% CI: 0.49-0.91) and had a greater risk of tooth extracted due to tooth decay (OR = 3.31; 95% CI: 1.64-6.68). Furthermore, for Asian women immigrants, their major barriers to dental care included low household income (≤$39,999 vs. $40,000-$79,999 OR = 0.26) and a lack of dental insurance (no vs. yes OR = 0.33). CONCLUSIONS: Asian immigrant women showed lower utilization of dental services than non-immigrant women. A perceived lack of necessity, lower household income, and dental insurance coverage were major barriers to professional dental usage for most Asian immigrants of childbearing age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".