Dental health and dental care utilization among childbearing age Asian women immigrants in Canada
Bibliographic record
Abstract
This study examines the dentistry care utilization, self-reported dental health status, and oral health issues of Asian immigrants and Asian women immigrants of childbearing age, using the combined Canadian Community Health Survey from 2012-2014 and 2011-2014 data. Reports show that amongst Asian immigrants and Asian women immigrants of childbearing age, there are significantly lower frequency of dental care utilization compared to non-immigrant counterparts. Furthermore, the difference in dentist visiting behavior between native-born Canadians and Asian immigrants mitigated with increase in length of residence in Canada. Socioeconomic status, lifestyle factors, dental health and dental insurance coverage cannot fully explain the behavioral differences in visiting the dentist between Asian immigrants and Asian women immigrants of childbearing age compared to the native-born citizens. For both Asian immigrants and Asian women immigrants of childbearing age, the primary reason for not visiting the dentist in the last three years is that the “Respondent did not think necessary.” This response is much different than reasons for not visiting the dentist provided by other groups which such as the “cost” and “haven’t gotten around to it.” Asian immigrants, as well as Asian women of childbearing age, had a significantly greater risk of tooth extracted due to tooth decay than other ethnicities. Asian immigrants also had a higher prevalence of having fair or poor selfreported dental health than Canadian born residences. Surprisingly, the prevalence of dental health problems for Asian immigrants is like that of a native-born Canadians, with Asian women immigrants of childbearing age showing the least prevalence of dental symptoms among the three population groups. Our results suggest that oral health beliefs in the lack of necessity in dental services exist among recent Asian immigrants and Asian women immigrants of childbearing age, with their oral health gradually changing over time throughout their stay in Canada. Early symptoms of dental problems that lead to decay may most likely result from the lack of visits to the dentist in Asian immigrants. Finally, the study findings do not appear to support the ‘Healthy Immigrant Effect’ for dental health and teeth issues.
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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 | 0.001 | 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".